Turner Dissertation_Final

UNIVERSITY OF CALIFORNIA

Los Angeles

The Development of Methods to Assess Radiation Dose to Organs

from Multidetector Computed Tomography Exams

Based on Detailed Monte Carlo Dosimetry Simulations

A dissertation submitted in partial satisfaction of the

requirements for the degree Doctor of Philosophy

in Biomedical Physics

by

Adam Christopher Turner

2011

© Copyright by


2011

ii

The dissertation of Adam Christopher Turner is approved.

Christopher Cagnon

John DeMarco

Matthew Brown

David Saltzberg

Michael McNitt-Gray, Committee Chair

University of California, Los Angeles

2011

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I dedicate this dissertation to my parents Gary and Marilynn Turner.

I owe you everything.

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Table of Contents

Chapter 1 Background and Motivation ....................................................................................... 1

1.1 Radiation Risks from CT Exams ........................................................................................... 4

1.2 Routine Clinical CT Dosimetry Assessment (CTDI and DLP) ............................................. 6

1.3. Limitations of the CTDI ...................................................................................................... 10

1.4 Effective Dose from CT Exams and its Limitations ............................................................ 11

1.5. Existing Organ Dose Estimation Methods .......................................................................... 13

1.6. Discussion ........................................................................................................................... 18

Chapter 2 Specific Aims .............................................................................................................. 19

Chapter 3 UCLA Monte Carlo MDCT Dosimetry Package .................................................... 21

3.1 Radiation Transport Methods .............................................................................................. 21

3.2 Modifications to Model MDCT Scanners ............................................................................ 22

3.3 Post Simulation Processing .................................................................................................. 24

3.4 Validation of Dose Simulations ........................................................................................... 25

Chapter 4 A Method to Generate Equivalent MDCT Source Models Based on

Measurements .............................................................................................................................. 27

4.1 Introduction .......................................................................................................................... 27

4.2 Methods ............................................................................................................................... 29

4.3 Results .................................................................................................................................. 44

4.4 Discussion ............................................................................................................................ 49

Chapter 5 The Feasibility of Scanner-Independent CTDIvol-to-Organ Dose Coefficients .... 57

5.1 Introduction .......................................................................................................................... 57

5.2 Methods ............................................................................................................................... 58

5.3 Results .................................................................................................................................. 66

5.4 Discussion ............................................................................................................................ 73

Chapter 6 Size Dependence of CTDIvol-to-Organ Dose Coefficients ....................................... 80

6.1 Introduction .......................................................................................................................... 80

6.2 Methods ............................................................................................................................... 81

6.3 Results .................................................................................................................................. 91

6.4 Discussion ............................................................................................................................ 97

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Chapter 7 Estimating Dose to Partially-Irradiated Organs using CTDIvol -to-Organ Dose

Coefficients ................................................................................................................................. 105

7.1 Introduction ........................................................................................................................ 105

7.2 Methods ............................................................................................................................. 108

7.3 Results ................................................................................................................................ 114

7.4 Discussion .......................................................................................................................... 121

Chapter 8 The Feasibility of CTDIvol -to-Organ Dose Coefficients that Account for Tube

Current Modulation................................................................................................................... 126

8.1 Introduction ........................................................................................................................ 126

8.2 Methods ............................................................................................................................. 130

8.3 Results ................................................................................................................................ 139

8.4 Discussion .......................................................................................................................... 148

Chapter 9 Advanced MDCT Monte Carlo Dosimetry Validation Methods ......................... 154

9.1 Introduction ........................................................................................................................ 154

9.2 AAPM Task Group 195 ..................................................................................................... 157

9.3 Half Value Layer and Bowtie Profile Measurements as Benchmarks ............................... 181

9.4 Surface Dose Measurements on a Thorax Anthropomorphic Phantom ............................. 191

9.5 Conclusions ........................................................................................................................ 203

Chapter 10 Dissertation Summary and Conclusions .............................................................. 208

Appendix A. Supplementary Tables from Chapter 4 ............................................................. 212

Appendix B. Energy Dependence of Small Volume Ionization Chambers and Solid State

Detectors at Diagnostic Energy Ranges for CT Dosimetry – Assessment In Air and In

Phantom ...................................................................................................................................... 219

Appendix C. Summary of Organ Dose Estimation Method .................................................. 224

References ................................................................................................................................... 229

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List of Figures

Figure 1.1 CT images of various anatomical regions including A) abdomen, B) chest, C) head. ... 1

Figure 1.2 A) Diagram of a third-generation CT scanner including the rotating x-ray source,

rotating detector array, and the translating table. B) Illustration of the x-ray source path for a

helical CT scan1. .............................................................................................................................. 2

Figure 1.3 Probability distribution function (PDF) of a typical tungsten anode x-ray tube spectrum

for a tube voltage of 140 kV (i.e. 140 kVp) used for CT scanners. ................................................. 3

Figure 1.4 The longitudinal dose profile from a contiguous axial exam. The profile for each

rotation and the summation is shown. Reprinted from C.H. McCollough, et al.11

.......................... 7

Figure 1.5 16 cm diameter ―head‖ and 32 cm diameter ―body‖ CTDI phantoms composed of

PMMA and containing pre-drilled holes at center and four periphery positions. ............................ 9

Figure 1.6 A) Screen shot from the ImPACT Dosimetry Calculator showing the MIRD

mathematical phantom used by the NRPB Monte Carlo Study. B) Adult females from GSF

Family of Voxelized Models. ........................................................................................................ 16

Figure 4.1 Diagram of HVL measurement set up that utilizes a stationary (non-rotating) x-ray

source. ............................................................................................................................................ 32

Figure 4.2 Diagram of bowtie profile measurements that characterize the attenuation across the

fan beam. ........................................................................................................................................ 33

Figure 4.3 Illustration of method for generating equivalent spectrum from measured. ................. 36

Figure 4.4 The cumulative percentage of CTDI100 simulations that are characterized by the level

agreement with measured CTDI100 values specified by each category: (1: ≤±1% 2: >±1% but

≤±2% 3: >±2% but ≤±5% 4: >±5% but ≤±10% 5: >±10). .......................................................... 49

Figure 5.1 of Irene from the GSF Family of Voxelized Models. Note the individual segmentation

of radiosensitive organs. ................................................................................................................ 61

Figure 5.2 Organ dose (DS,O), in mGy, and effective dose (DS,ED), in mSv, for a 100 mAs/rot scan

for scanners 1–4. ............................................................................................................................ 68

Figure 5.3 CTDIvol, S normalized organ (nDS,O), and effective (nDS,ED) doses for scanners 1–4. ... 71

Figure 6.1 Fig. 1. Illustrations of the GSF Family of Voxelized Phantoms as described in

Petoussi-Henss, Zankl, et al.39

and Fill, Zankl, et al.40

. Additional information provided in Table

6.1. ................................................................................................................................................. 83

Figure 6.2 Mean CTDIvol normalized organ doses across scanners as a function of patient

perimeter (in cm). The exponential regression curve, equation, and correlation coefficient for

stomach is shown as an example. .................................................................................................. 93

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Figure 6.3 The proposed method to estimate patient-, scanner-, and exam-specific organ dose

using the size coefficients (AO, BO), patient perimeter (in cm), and the CTDIvol reported by the

scanner. ........................................................................................................................................ 104

Figure 7.1 Illustration of the process to segment partially-irradiated organs into "in-beam" and

"out-of-beam" segments. .............................................................................................................. 110

Figure 7.2 CTDIvol normalized dose values for the in-beam segment of each partially-irradiated

organ as a function of patient perimeter in cm. The exponential trendline for bone surface is

shown as an example. .................................................................................................................. 117

Figure 7.3 Diagram of the proposed method to estimate patient-, scanner-, and exam-specific

dose to partially-irradiated organs using the size coefficients (AO,in, BO,in), average percent

coverage (αorgan), patient perimeter (in cm), and the CTDIvol. ...................................................... 124

Figure 8.1 Tube current function illustrating modulation of the tube current (mA) in the axial

plane (high-frequency oscillations) and along the longitudinal plane (low-frequency oscillations).

..................................................................................................................................................... 126

Figure 8.2 An anonymized dose report for an exam performed with TCM on a Siemens Sensation

64 located at UCLA. For this exam, the first scan was a used to generate a two-dimensional

planning image called a ―topogram‖. Then, two helical scans were performed and information

including the kVp, average mAs, TCM reference mAs, and CTDIvol for both is included in the

report. ........................................................................................................................................... 128

Figure 8.3 Generation of a voxelized model: (a) original patient image, (b) radiologist‘s contour

of the breast region, (c) threshold image to identify glandular breast tissue and (d) the resulting

voxelized model. Reprinted from Angel, et al.61,62

. ..................................................................... 133

Figure 8.4 (mean organ dose/CTDIvol across scanners) from fixed tube current scans as a

function of patient perimeter (in cm) for lung and glandular breast tissue. The exponential

regression curves for each organ are also shown. ........................................................................ 140

Figure 8.5 (mean organ dose/CTDIvol across scanners) from fixed tube current scans as a

function of patient perimeter (in cm) for liver, spleen, and kidney. The exponential regression

curves for each organ are also shown. ......................................................................................... 140

Figure 8.6 Simulated organ dose values in mGy from simulations of Siemens Sensation 64 chest

exams performed with TCM as a function of perimeter in cm for lung and glandular breast tissue.

..................................................................................................................................................... 142

Figure 8.7 Simulated organ dose values in mGy from simulations of Siemens Sensation 64

abdomen/pelvis exams performed with TCM as a function of perimeter in cm for liver, spleen,

and kidney. ................................................................................................................................... 142

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Figure 8.8 A) Lung dose estimates calculated with CTDIvol,Avg mAs and lung doses from TCM

simulations. B) Percent error of lung dose estimates calculated with CTDIvol,Avg mAs with respect to

lung doses from TCM simulations. .............................................................................................. 144

Figure 8.9 kP,O (simulated organ dose/estimated organ dose) as a function of patient perimeter (in

cm) for lung and glandular breast tissue. The linear regression curves for each organ are also

shown. .......................................................................................................................................... 146

Figure 8.10 kP,O (simulated organ dose/estimated organ dose) as a function of patient perimeter

(in cm) for liver, spleen, and kidney. The linear regression curves for each organ are also shown.

..................................................................................................................................................... 146

Figure 8.11 The proposed method to estimate patient-, scanner-, and exam-specific organ dose

using the size coefficients (AO, BO), TCM correction factor coefficients (CO, DO) patient

perimeter (in cm), and the CTDIvol corresponding to the Quality Reference mAs. ..................... 151

Figure 9.1 Diagram of the simulation geometry used to simulate HVL and QVL measurements as

defined by Task Group 195. ......................................................................................................... 164

Figure 9.2 Diagram of CTDI-like phantom simulation as defined by AAPM Task Group 195. . 170

Figure 9.3 Diagram of CTDI-like phantom. Note the two CTDI rod-like inserts and the first

projection angle. ........................................................................................................................... 171

Figure 9.4 Diagram of the contiguous axial tally regions for the Test 1. For these simulations the

source is fixed and located at the longitudinal center of the phantom (z=0). .............................. 172

Figure 9.5 MCNPX simulated kerma for the axial segments of CTDI-like phantom from the

monoenergetic beam in keV/kg/NPS for the narrow and wide beam widths. ............................. 175


polyenergetic beam in keV/kg/NPS for the narrow and wide beam widths. ............................... 176

Figure 9.7 MCNPX simulated kerma tallied in the center and peripheral rods from fixed source

positions at gantry angles ranging from 0 to 360 degrees on a logarithmic scale........................ 177

Figure 9.8 Diagram of the set up used to measure the HVL and QVL for both Scanners 1 and 2.

The x-ray source remained stationary at the 6o'clock position. ................................................... 185

Figure 9.9 Diagram of the set up used to measure the bowtie profile for both Scanners 1 and 2.

The x-ray source remained stationary at the 3 o'clock position. .................................................. 186

Figure 9.10 Percent error of bowtie profile simulations as a function the distance from isocenter

(in cm) for Scanner 1. .................................................................................................................. 188

Figure 9.11 Percent error of bowtie profile simulations as a function the distance from isocenter

(in cm) for Scanner 2. .................................................................................................................. 189

Figure 9.12 The Alderson Chest/Lung Phantom from Radiological Support Devices, INC.78

... 192

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Figure 9.13 Generation of a voxelized model: (a) original patient image, (b) radiologist‘s contour

of the breast region, (c) threshold image to identify glandular breast tissue and (d) the resulting

voxelized model. Reprinted from Angel, et al.61,62

. ..................................................................... 194

Figure 9.14 Axial view of the voxelized model created from images of the Alderson Lung/Chest

Phantom. ...................................................................................................................................... 195

Figure 9.15 Sagital view of the voxelized model created from images of the Alderson Lung/Chest

Phantom. ...................................................................................................................................... 195

Figure 9.16 Coronal view of the voxelized model created from images of the Alderson

Lung/Chest Phantom. ................................................................................................................... 196

Figure 9.17 The measured and simulated doses to the ionization chamber located on the surface

of the thorax phantom as a function of tube start angle. .............................................................. 199

Figure 9.18 Diagram to illustrate how a lateral shift results in a phase shift and amplitude change

for dose as a function of tube start angle plot. ............................................................................. 202

Figure 9.19 Proposed approach for robustly validating the accuracy of a Monte Carlo CT

simulation package. Starting at the top, each level introduces a new level of complexity in order

to assess a different component of the simulation package. ........................................................ 207

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List of Tables

Table 1.1 ICRP Publication 103 recommended tissue weighting factors.5 .................................... 12

Table 1.2 Normalized effective dose per dose-length product (DLP) for adults (standard

physique) and pediatric patients of various ages over various body regions. Conversion factor for

adult head and neck and pediatric patients assume use of the head CT dose phantom (16 cm). All

other conversion factors assume use of the 32-cm diameter CT body phantom3 .......................... 13

Table 6.1 Information about the GSF Family of Voxelized Models as described in Petoussi-

Henss, Zankl, et al.39


................................................................................. 83

Table 6.2 Mean CTDIvol normalized organ doses across scanners for each patient model for fully-

irradiated organs. Note that the gall bladder was not included in the Child patient model. .......... 92

Table 6.3 Results of exponential regression analysis describing as a function of perimeter

(cm) for fully-irradiated organs. .................................................................................................... 94

Table 6.4 Mean CTDIvol normalized organ doses across scanners ( ) for each patient model

for partially-irradiated organs. A dash indicates the organ was not included in the patient model.

....................................................................................................................................................... 94

Table 6.5 Percent coverage of each partially-irradiated organ (i.e. percentage of organ volume

located within the abdominal scan region). The last two columns report the average and standard

deviation across patient models. A dash indicates that the organ was not included for the given

patient model. ................................................................................................................................. 95

Table 6.6 Average and standard deviation of the percent coverage of each partially-irradiated

organ and the correlation coefficient resulting from the exponential regression relating to

perimeter. ....................................................................................................................................... 96

Table 6.7 Percent ratios of dose to each non-irradiated organ relative to average fully-irradiated

organ dose. The last two columns report the average and standard deviation across patient

models. A dash indicates that the non-irradiated organ was not included for the given patient

model. ............................................................................................................................................ 97

Table 7.1 CTDIvol normalized dose to the in-of-beam portion of each partially-irradiation organ.

(i.e. ). Note that the esophagus was not included in the Child model and the small

intestine was fully-irradiated in the Baby model. ........................................................................ 115

Table 7.2 CTDIvol normalized dose to the out-of-beam portion of each partially-irradiation organ.

(i.e. ). Note that the esophagus was not included in the Child model and the small

intestine was fully-irradiated in the Baby model. ........................................................................ 115

Table 7.3 Ratio (expressed as a percent) of CTDIvol normalized dose to the out-of-beam portion of

each partially-irradiation organ to the in-beam portion (i.e. ). Note that the

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esophagus was not included in the Child model and the small intestine was fully-irradiated in the

Baby model. ................................................................................................................................. 116

Table 7.4 Results of exponential regression analysis describing as a function of perimeter

(cm) for the in-beam segment of partially-irradiated organs. ...................................................... 118

Table 7.5 The percent coverage of each partially-irradiated organ for a typical abdomen scan to

each GSF patient model. .............................................................................................................. 119

Table 7.6 The average percent coverage for a typical abdomens scan of each partially-irradiated

organ across patients (αorgan) and the corresponding standard deviation. ..................................... 119

Table 7.7 Estimated values obtained using Equation 7.9 for the partially-irradiated organs

of each GSF patient model. .......................................................................................................... 120

Table 7.8 Percent errors of the estimates obtained with the method derived in this chapter

with respect to the simulated values obtained with simulation (Table 6.4). The average and

standard deviation of the absolute percent errors across patient models are in the last two

columns. ....................................................................................................................................... 120

Table 8.1 Results of the exponential regression analysis between from fixed tube current

scans and patient perimeter. For each organ the patient cohort, AO and BO coefficients, and

correlation coefficient (R2) is reported. ........................................................................................ 141

Table 8.2 Summary statistics for the percent errors of organ dose estimates calculated with

CTDIvol,Avg mAs with respect to doses obtained from TCM simulations, including: root mean

square, minimum error, and maximum error across patients in appropriate cohort. ................... 145

Table 8.3 Results of the linear regression analysis between kP,O and patient perimeter. For each

organ the patient cohort, CO and DO coefficients, and correlation coefficient (R2) is reported ... 147

Table 8.4 Summary statistics for the leave-one-out cross-validation analysis to quantify the

percent errors for estimated doses calculated using CTDIvol,Ref mAs and kP,O from Equation 8.7. . 148

Table 9.1 Theoretical HVL and QVL values for monoenergetic photon beams. ........................ 165

Table 9.2. Theoretical HVL and QVL values for polyenergetic photon beams. The kVp, tube

target material, and tube filtration material of the IEC beam quality reference spectrum is also

listed. ............................................................................................................................................ 166

Table 9.3 Results of HVL and QVL simulations for monoenergetic beams including the energy,

air kerma with and without the Al filter, their ratio, and percent error from the theoretical ratio.

..................................................................................................................................................... 166

Table 9.4 Results of HVL and QVL simulations for polyenergetic beams including the kVp, air

kerma with and without the Al filter, their ratio, and percent error from the theoretical ratio. ... 166

Table 9.5 Design specifications of the virtual scanner as defined by AAPM Task Group 195. .. 169

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Table 9.6 MCNPX simulated kerma for the axial segments of CTDI-like phantom from the

monoenergetic beam in keV/kg/NPS for the narrow and wide beam widths. ............................. 175


polyenergetic beam in keV/kg/NPS for the narrow and wide beam widths. ............................... 176

Table 9.8 MCNPX simulated kerma values for the center and peripheral CTDI rod-like volume

from each gantry angle in units of MeV/kG/NPS. The average kerma from angles 0 to 360 is

reported for the peripheral rod. .................................................................................................... 178

Table 9.9 The percent error of each CTDI100,center and CTDI100,periphery simulation. ............ 187

Table 9.10 The percent error of each HVL and QVL simulation. ............................................... 187

Table 9.11 The Root Mean Square percent error of each bowtie profile simulation. .................. 189

Table 9.12 The measured and simulated doses to the ionization chamber located on the surface of

the thorax phantom and the simulation percent error for each actual start angle. ........................ 198

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Acknowledgments

First and foremost I thank Dr. Mike McNitt-Gray who has been a strong and

supportive advisor throughout my graduate career. The greatest professional decision I

made during my time in graduate school was to jump at the opportunity of joining Mike‘s

lab group. Over the last four years I underwent a transformation from a typical physics

student with the ability to learn out of a book and do homework problems to a scientist

whose main goal is the development of new knowledge based on critical and creative

thinking. I fully attribute that transformation to the influence Mike has had on me. There

is no way to adequately express my gratitude for the lessons he bestowed upon me in

areas of medical physics, the academic world, and life in general.

I also would not have gotten to the point I‘m at today without Dr. Chris Cagnon.

Chris‘ ability to frame my work in the perspective of reality always reminded me that the

research being done by our group was groundbreaking and state of the art. He reminded

me that while most diagnostic medical physicists are satisfied with ―being within a factor

of 2‖ it is up to us to try harder and to raise the bar. His enthusiasm was infectious and I

always walked away from our conversations with a renewed sense of confidence that my

work was important and worthwhile. I would like to thank Chris for his friendship over

the years. From day one he treated me as a colleague, rather than just as a student, and I

will always appreciate that.

I am also extremely grateful for the time and effort devoted to my work by Dr.

John DeMarco. As the resident Monte Carlo guru, it was a pleasure and a privilege to

learn the ins and outs of the MCNPX Monte Carlo code from John. His knowledge of the

intricate details that go into the physical models used for radiation transport was an

inspiration. I always prepared for research meetings or presentations with the expectation

that he would ask me a complex question, and I know that made me a better all around

researcher. As I head into a radiation oncology residency program, John‘s expertise,

dedication, and work ethic will be the example I strive to achieve.

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I am pleased to thank Dr. Matt Brown for sitting on my Ph.D. committee and for

being an excellent role model over the past four years. I had the privilege of interacting

with him on a regular basis during the weekly MedQIA research/journal clubs. His

lessons on how to properly design and execute a scientific study played a large role in

how I went about my dissertation work. Also, as the co-founder and Chief Scientific

Officer of MedQIA, I thank him for the office space in the company headquarters that I

used for four long years.

While I did not get to directly work alongside Dr. David Satlzberg, I‘d like to

thank him for sitting on my Ph.D. committee. His very helpful advice and insightful

questions during my first oral examination helped me to sharpen the focus of my

dissertation projects. I also owe him a huge thank you for agreeing to attend my doctoral

defense on the afternoon after undergoing surgery. Not many committee members would

do that, especially for a student they don‘t know extremely well.

I will also take this opportunity to thank the entire MedQIA staff, especially Dr.

Jonathan Goldin for serving as an exemplary academic physician and contributing to my

training on how to break down and scrutinize scientific publications, Richie Pais for his

computer programming expertise and always being around for a friendly conversation,

and Laura Guzman and Kimberly Easter for helping me with administrative and work

related issues. Also, I thank Terry More and Reth Thach for all the assistance they

provided me with student affairs and issues related to the Biomedical Physics

Department. It was a pleasure working with all of you over the years.

Any success that I‘ve had during graduate school can be directly attributed to my

labmates that worked with me side by side. First, I thank Dr. Erin Angel very much for

her patience with me in the early days when I averaged two to three questions a minute. I

am convinced that without her tutelage, advice, and procrastination sessions I would have

been lost from the start and never found my way as I did. I also express my sincere

gratitude to Maryam Khatonabadi. Her ability to catch on and quickly understand

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advanced concepts that were thrown at her always impressed me. I appreciate all the help

with the projects we collaborated on over the past two years. Finally, I owe Di Zhang one

of the biggest thanks of all. Di and I entered the lab group around the same time and I

always considered him more of a partner than just a labmate. Di always seemed to have

the answer when I had questions (and I had a lot of them), but even more importantly,

was always willing to drop what he was doing for an impromptu white board session or

code review. I can only hope I was able to contribute to all of his success as much as he

contributed to mine. I am very proud to have worked alongside these three individuals

and to be able to call them good friends.

I would have never made it through graduate school without the help of my

friends that were always there to help me forget I was in graduate school in the first

place. I am especially grateful to Gabe Marcus and Jeff Wright for being excellent

roommates, softball teammates, and drinking buddies. You guys were my Los Angeles

support system and I can‘t thank you enough. I also would like to thank my good friends

in Phoenix, AZ who were always ready for a fun time during my frequent weekend visits,

especially Greg McNamee, Megan McNamee, Heather Nystedt, Travis Harris, and Matt

Gioseffi.

My family has always been my main source of support, encouragement, and

motivation. I thank my father, Gary Turner, for teaching me integrity, honesty, hard

work, and kindness. To my mother, Marilynn Turner, I express enormous gratitude for

instilling in me the concepts of love, compassion, and respect. There is no way to

adequately pay back all they have given me, but as a start, I dedicate this dissertation to

them. I also thank my little brother Nathan. I am proud of his hard work at the University

of Arizona during my time in graduate school. I see nothing but success in his future as I

know he will continue to Bear Down. Finally, I sincerely thank Mark and Donna Hebein

for their support over the past few years. I am honored to be joining their family in a few

months and can‘t thank them enough for helping Jenna and I travel back and forth

between Phoenix and Los Angeles.

xvi

I owe the biggest thank you to my fiancée Jenna Hebein. Since we met in

February of 2009 my life has had a true direction and purpose. Her undying support, even

during my most difficult periods of graduate school, gave me the extra motivation I

needed to succeed. I have had an amazing time exploring Los Angeles, Phoenix, Las

Vegas, and the various other cities we have visited together. I can‘t wait to begin our life

together in Tucson this summer and get married next fall. I am extremely thrilled and

tremendously excited to move on to the next stage with her as my partner. She has made

it all worth it and to her I say, I love you very much.

xvii

I would like to acknowledge the following grants and fellowships for funding portions of

this work:

UCLA Graduate Division Fellowship (2010-2011)

National Institute of Biomedical Imaging and Bioengineering - R01 EB004898

(2007-2010)

National Institute of Biomedical Imaging and Bioengineering – NIBIB Training

Grant T32EB002101 (2006-2007)

The following are chapter-specific acknowledgments:

Chapter 4 is based on the research published in the journal Medical Physics:

A. C. Turner, D. Zhang, H. J. Kim, J. J. DeMarco, C. H. Cagnon, E. Angel, D. D.

Cody, D. M. Stevens, A. N. Primak, C. H. McCollough, and M. F. McNitt-

Gray, ―A method to generate equivalent energy spectra and filtration models

based on measurement for multidetector CT Monte Carlo dosimetry

simulations,‖ Med. Phys. 36(6), 2154–2164 (2009).

Chapter 5 is based on research published in the journal Medical Physics and

presented at the Radiological Sciences of North America (RSNA) Annual Meeting in

Chicago, IL in December, 2008. This work was awarded the 2009 Norm Baily Award

from the Southern California Chapter of the American Association of Physicists in

Medicine (AAPM):

A. C. Turner, M. Zankl, J. J. DeMarco, C. H. Cagnon, D. Zhang, E. A. Angel, D. D.

Cody, D. M. Stevens, C. H. McCollough, and M. F. McNitt-Gray, ―The

feasibility of a scanner-independent technique to estimate organ dose from

MDCT scans: Using CTDIvol to account for differences between scanners,‖

Med. Phys. 37(4), 1816–1825 (2010).

A.C. Turner, E. Angel, D. Zhang, J.J. DeMarco, M. Zankl, M.F McNitt-Gray, C.H.

Cagnon, D.M. Stevens, A.N. Primak, D.D. Cody, and C.H. McCollough,

―Comparison of Organ Dose among 64 Detector MDCT Scanners from

Different Manufacturers: A Monte Carlo Simulation Study,‖ (abstr.) In:

Radiological Society of North America scientific assembly and annual meeting

program, Chicago, IL, SSJ23-03, 502 (2008).

Chapter 6 is based on the research published in the journal Medical Physics and

presented at the Radiological Sciences of North America (RSNA) Annual Meeting in

Chicago, IL in December, 2009:

xviii

A.C. Turner, D. Zhang, M. Khatonabadi, M. Zankl, J.J. DeMarco, C.H. Cagnon, D.D.

Cody, D.M. Stevens, C.H. McCollough, and M.F. McNitt-Gray, ―The feasibility

of patient size-corrected, scanner-independent organ dose estimates for

abdominal CT exams,‖ Med. Phys. 38(2), 820-829 (2011).

A.C. Turner, M. Zankl, J.J. DeMarco, E. Angel, C.H. Cagnon, D. Zhang, and M.F.

McNitt-Gray, ―A Method to Estimate Organ Doses from Multidetector Row CT

Abdominal Exams from Patient Sized Corrected CT Dose Index Values: A

Monte Carlo Study,‖ (abstr.) In: Radiological Society of North America

scientific assembly and annual meeting program, Chicago, IL, SSG19-04, 472

(2009).

Chapter 8 is based on the research presented at the Radiological Sciences of North

America (RSNA) Annual Meeting in Chicago, IL in December, 2010:

A.C. Turner, E. Angel, and M.F. McNitt-Gray, ―The Feasibility of Accounting for

Tube Current Modulation in Patient- and Scanner-Specific Organ Dose

Estimates from CT,‖ (abstr.) In: Radiological Society of North America

scientific assembly and annual meeting program, Chicago, IL, SSA20-04

(2010).

Chapter 9 is partially based on the research presented and the research that will be

presented at the following scientific meetings:

I. Sechopoulos, S. Abboud, E. Ali, A. Badal, A. Badano, S.S.J. Feng, I. Kyprianou,

M. McNitt-Gray, E. Samei, and A.C. Turner, ―Introduction to the AAPM Task

Group No. 195 - Monte Carlo Reference Data Sets for Imaging Research,‖

(abstr.) In. American Associate of Physicists in Medicine 53rd Annual Meeting,

Vancouver, BC, WE-G-110-6 (2011).

A.C. Turner and M.F. McNitt-Gray, ―A Proposed Approach for Validating Monte

Carlo Computed Tomography dosimetry simulations,‖ Poster, In: The First

International Conference on Image Formation in X-Ray Computed

Tomography, Salt Lake City, UT (2010).

A.C. Turner, M. Zankl, E. Angel, and M.F. McNitt-Gray, ―Evaluation of Different

Benchmark Measurements for Validating Monte Carlo MDCT Source Models

Used in Estimating Radiation Dose,‖ (abstr.) Poster. In: American Association

of Physicists in Medicine 52nd

Annual Meeting, Philadelphia, PA, SU-GG-I-39,

3110 (2010).

xix

VITA

September 12, 1983 Born, Phoenix, Arizona

2005 AAPM Undergraduate Summer Fellow

Memorial Sloan Kettering Cancer Center

New York, New York

2006 B.S., Physics

University of Arizona

Tucson, Arizona

2007-09 Graduate Student Researcher


Los Angeles, California

2009 Norm Baily Award for Best Student Paper

Southern California Chapter of the AAPM





2010 Greenfield Award for Excellence in Medical Imaging

UCLA Biomedical Physics Interdepartmental Graduate Program





xx

PUBLICATIONS AND PRESENTATIONS

E. Angel, N. Yaghmai, H. Kim, J. Demarco, C. Cagnon, A. Turner, D. Zhang, J. Goldin,

and M. McNitt-Gray, ―How Well Does CTDI Estimate Organ Dose to Patients From

Multidetector (MDCT) Imaging?,‖ oral presentation. (abstr.) In: American Association

of Physicists in Medicine 50th

Annual Meeting, Houston, TX, WE-D-332-03, (2008).

M. Khatonabadi, M.F. McNitt-Gray, A.C. Turner, D. Zhang, E. Angel, T. Hall, and I.

Boechat, ―The Effects of Incorrect Choice of Patient Size References (Adult/Child) On

Tube Current Modulation,‖ oral presentation. (abstr.) In: American Association of

Physicists in Medicine 52nd

Annual Meeting, Philadelphia, PA, MO-EE-A4-03, 3351

(2010).

M. Khatonabadi, E. Angel, M.F. McNitt-Gray, A.C. Turner, and D. Zhang, ―The

Accuracy of Organ Doses Estimated from Monte Carlo CT Simulations Utilizing

Approximations to the Tube Current Modulation Function,‖ oral presentation. (abstr.)

In: Radiological Society of North America scientific assembly and annual meeting

program, Chicago, IL, SSA20-01 (2010).

M. Khatonabadi, M.F. McNitt-Gray, E. Angel, A.C. Turner, and D. Zhang, ―The Effect

of Incorrect Selection of Reference Patient Size (Adult/Child) When Using Tube

Current Modulation (TCM) in CT,‖ oral presentation. oral presentation. (abstr.) In:



K. Mathieu, A. Turner, C. Cagnon, and D. Cody, ―kVp modulation schemes designed to

reduce breast dose,‖ oral presentation. (abstr.) In: Radiological Society of North

America scientific assembly and annual meeting program, Chicago, IL, SSA20-03

(2010).

M.F. McNitt-Gray, E. Angel, A.C. Turner, D.M. Stevens, A.N. Primak, C.H. Cagnon, et

al. ―CTDI Normalized to Measured Beam Width as an Accurate Predictor of Dose

Variations for Multidetector Row CT (MDCT) Scanners Across all Manufacturers,‖

oral presentation. (abstr.) In: Radiological Society of North America scientific

assembly and annual meeting program, Chicago, IL, SSJ23-04, 502 (2008).

M.F. McNitt-Gray, J.J. DeMarco, C.H. Cagnon, A.C. Turner, and D. Zhang, ―Monte-

Carlo Simulation Approach to Estimating Patient Radiation Dose from MDCT

Exams,‖ oral presentation. The First International Conference on Image Formation in

X-Ray Computed Tomography, Salt Lake City, UT (2010).

C. Morioka, A. Turner, M. McNitt-Gray, F. Meng, M. Zankl, and S. El-Saden,

―Development of a DICOM Structure Report to Track Patient‘s Radiation Dose to

Organs from Abdominal CT Exams,‖ poster presentation. American Medical

Informatics Association annual meeting, Washington D.C., (2010).

xxi

A.D. Sodickson, A.C. Turner, K. McGlamery, and M.F. McNitt-Gray, ―Variation in

Organ Dose from Abdomen Pelvis CT Exams Performed with Tube Current

Modulation (TCM): Evaluation of Patient Size Effects,‖ oral presentation. (abstr.) In:



A.C. Turner, C.J. Watchman, and R.J. Hamilton, "Probabilistic Analysis of

Radiation Induced Pneumonitis as a Function of Tumor and Margin Size,"

poster presentation. Int. Jour. Rad. Onc. Biol. Phys. Vol. 66 No. 3 Supplement

2006.

A.C. Turner, E. Angel, D. Zhang, J.J. DeMarco, C.H. Cagnon, and M.F. McNitt-Gray,

―The Relationship between Half Value Layer (HVL) and CTDI for Multidetector CT

(MDCT),‖ poster presentation. American Association of Physicists in Medicine 50th

Annual Meeting, Houston, TX, SU-GG-I-62 (2008).

A.C. Turner, E. Angel, D. Zhang, J.J. DeMarco, M. Zankl, M.F McNitt-Gray, C.H.

Cagnon, D.M. Stevens, A.N. Primak, D.D. Cody, and C.H. McCollough, ―Comparison

of Organ Dose among 64 Detector MDCT Scanners from Different Manufacturers: A

Monte Carlo Simulation Study,‖ oral presentation. (abstr.) In: Radiological Society of

North America scientific assembly and annual meeting program, Chicago, IL, SSJ23-

03, 502 (2008).

A.C. Turner, D. Zhang, H.J. Kim, J.J. DeMarco, C.H. Cagnon, E. Angel, D.D. Cody,

D.M. Stevens, A.N. Primak, C.H. McCollough, and M.F. McNitt-Gray, ―A method to

generate equivalent energy spectra and filtration models based on measurement for

multidetector CT Monte Carlo dosimetry simulations,‖ Med. Phys. 36(6), 2154-2164

(2009).

A.C. Turner, M. Zankl, E. Angel, and M.F. McNitt-Gray, ―Comparisons of Organ and

Effective Doses from ImPACT and DLP ED Methods to MDCT Specific Monte Carlo

Simulations,‖ poster presentation. American Association of Physicists in Medicine 51st

Annual Meeting, Anaheim, CA, SU-FF-I-53 (2009).

A.C. Turner, M. Zankl, J.J. DeMarco, E. Angel, C.H. Cagnon, D. Zhang, and M.F.

McNitt-Gray, ―A Method to Estimate Organ Doses from Multidetector Row CT

Abdominal Exams from Patient Sized Corrected CT Dose Index Values: A Monte

Carlo Study,‖ oral presentation. (abstr.) In: Radiological Society of North America

scientific assembly and annual meeting program, Chicago, IL, SSG19-04, 472 (2009).

A.C. Turner, M. Zankl, J.J. DeMarco, C.H. Cagnon, D. Zhang, E. Angel, D.D. Cody,

D.M. Stevens, C.H. McCollough, and M.F. McNitt-Gray, ―The feasibility of a

scanner-independent technique to estimate organ dose from MDCT scans: using

CTDIvol to account for differences between scanners,‖ Med. Phys. 37(4), 1816-1825

(2010).

xxii

A.C. Turner and M.F. McNitt-Gray, ―Scanner- and Patient-Specific Multidetector CT

Organ Dose Estimates from CTDI and Patient Size Measurements,‖ oral presentation.

The First International Conference on Image Formation in X-Ray Computed

Tomography, Salt Lake City, UT (2010).

A.C. Turner and M.F. McNitt-Gray, ―A Proposed Approach for Validating Monte Carlo

Computed Tomography dosimetry simulations,‖ poster presentation. The First

International Conference on Image Formation in X-Ray Computed Tomography, Salt

Lake City, UT (2010).

A.C. Turner and M.F. McNitt-Gray, ―Scanner-and Patient-Specific Multidetector CT

Organ Dose Estimates from CTDI and Patient Size Measurements,‖ poster

presentation. National Institute of Biomedical Imaging and Bioengineering Training

Grant Meeting, Bethesda, MD (2010).

A.C. Turner, M. Zankl, E. Angel, and M.F. McNitt-Gray, ―Evaluation of Different

Benchmark Measurements for Validating Monte Carlo MDCT Source Models Used in

Estimating Radiation Dose,‖ poster presentation. (abstr.) In: American Association of

Physicists in Medicine 52nd

Annual Meeting, Philadelphia, PA, SU-GG-I-39, 3110

(2010).

A.C. Turner, E. Angel, and M.F. McNitt-Gray, ―The Feasibility of Accounting for Tube

Current Modulation in Patient- and Scanner-Specific Organ Dose Estimates from CT,‖


assembly and annual meeting program, Chicago, IL, SSA20-04 (2010).

A.C. Turner, D. Zhang, M. Khatonabadi, M. Zankl, J.J. DeMarco, C.H. Cagnon, D.D.

Cody, D.M. Stevens, C.H. McCollough, and M.F. McNitt-Gray, ―The feasibility of

patient size-corrected, scanner-independent organ dose estimates for abdominal CT

exams,‖ Med. Phys. 38(2), 820–829 (2011).

D. Zhang, M. Zankl, J.J. DeMarco, C.H. Cagnon, E. Angel, A.C. Turner, and M.F.

McNitt-Gray, ―Reducing radiation dose to selected organs by selecting the tube start

angle in MDCT helical scans: a Monte Carlo based study,‖ Med. Phys. 36(12), 5654-

64 (2009).

D. Zhang, A.S. Savandi, J.J. DeMarco, C.H. Cagnon, E. Angel, A.C. Turner, D.D. Cody,

D.M. Stevens, A.N. Primak, C.H. McCollough, and M.F. McNitt-Gray, ―Variability of

surface and center position radiation dose in MDCT: Monte Carlo simulations using

CTDI and anthropomorphic phantoms,‖ Med. Phys. 36(3), 1025-1038 (2009).

D. Zhang, J.J. DeMarco, C.H. Cagnon, E. Angel, A.C. Turner, M. Zankl, and M.F.

McNitt-Gray, ―Reducing Dose to a Small Organ by Varying the Tube Start Angle in a

Helical CT Scan,‖ oral presentation. (abstr.) In: American Association of Physicists in

Medicine 51st Annual Meeting, Anaheim, CA, TU-C-304A-06, 2728 (2009).

xxiii

D. Zhang, A.C. Turner, C.H. Cagnon, J.J. DeMarco, and M.F. McNitt-Gray MF, ―Dose

from CT Brain Perfusion Examinations: a Monte-Carlo Study to Look into

Deterministic Effects,‖ oral presentation. The First International Conference on Image

Formation in X-Ray Computed Tomography, Salt Lake City, UT (2010).

D. Zhang, C.H. Cagnon, J.J. DeMarco, A.C. Turner, and M.F. McNitt-Gray, ―Novel

Strategies to Reduce Patient Organ Dose in CT without Reducing Tube Output,‖

poster presentation. The First International Conference on Image Formation in X-Ray

Computed Tomography, Salt Lake City, UT (2010).

D. Zhang, C.H. Cagnon, J.J. DeMarco, M. Zankl, A.C. Turner, M. Khatonabadi, and

M.F. McNitt-Gray, ―Estimating Dose to Eye Lens and Skin From Radiation Dose

From CT Brain Perfusion Examinations: Comparison to CTDIvol Values,‖ oral

presentation. (abstr.) In: American Association of Physicists in Medicine 52nd

Annual

Meeting, Philadelphia, PA, TU-A-201B-4, 3373 (2010).

D. Zhang, C.H. Cagnon, J.J. DeMarco, M. Zankl, A.C. Turner, M. Khatonabadi, and

M.F. McNitt-Gray, ―Reducing Eye Lens Dose During Brain Perfusion CT

Examinations by Moving the Scan Location or Tilting the Gantry Angle,‖ poster

presentation. (abstr.) In: American Association of Physicists in Medicine 52nd

Annual

Meeting, Philadelphia, PA, SU-GG-I-37, 3109 (2010).

D. Zhang, C.H. Cagnon, J.J. DeMarco, C.H. McCollough, D. Cody, M.F. McNitt-Gray,

A.C. Turner, and M. Khatonabadi, ―Estimating Radiation Dose to Eye Lens and Skin

from CT Brain Perfusion Examinations: A Monte Carlo Study,‖ oral presentation.

(abstr.) In: Radiological Society of North America scientific assembly and annual

meeting program, Chicago, IL, SSG14-01 (2010).

D. Zhang, C.H. Cagnon, J.J. DeMarco, C.H. McCollough, D. Cody, M.F. McNitt-Gray,

M. Zankl, A.C. Turner, and M. Khatonabadi, ―How Do CTDI and TG111 Small

Chamber Dose Perform in Estimating Radiation Dose to Eye Lens and Skin from CT

Brain Perfusion Examinations for Patients with Various Sizes: A Monte Carlo Study,‖


assembly and annual meeting program, Chicago, IL, SSM20-02 (2010).

xxiv

ABSTRACT OF THE DISSERTATION

The Development of Methods to Assess Radiation Dose to Organs

from Multidetector Computed Tomography Exams

Based on Detailed Monte Carlo Dosimetry Simulations

By


Doctor of Philosophy in Biomedical Physics

University of California, Los Angeles, 2011

Professor Michael McNitt-Gray, Chair

Computed Tomography (CT) has become an extremely valuable diagnostic

imaging modality, however, its widespread utilization has lead to a considerable increase

in its contribution to the collective radiation dose from medical procedures. It has been

suggested that the most appropriate quantity for assessing the risk of carcinogenesis from

diagnostic imaging procedures is the radiation dose to individual organs. The current

paradigm to assess dose from CT exams (i.e. the CT Dose Index) involves measuring

xxv

dose to homogenous, cylindrical phantoms and therefore does not directly quantify the

dose to any particular patient or organ. The overall goal of the work presented in this

dissertation is to develop a comprehensive methodology to accurately estimate the

radiation dose absorbed by individual organs in patients undergoing CT examinations.

In this dissertation, a Monte Carlo based modeling package that simulated the

delivery of radiation from modern multidetector CT (MDCT) scanners was used to

determine the radiation dose to organs segmented in detailed patient models. In order to

simulate the x-ray source characteristics from any MDCT scanner, the validity of a

method to generate a photon energy spectrum and filtration description (including the

bowtie filter) based only on scanner-specific measurements was demonstrated.

The range of doses from different scanners was investigated by obtaining organ

doses to a single patient model with Monte Carlo simulations for a range of patients from

MDCT scanners from the four major scanner manufacturers. This work revealed that

there is considerable variation across scanners in both CTDIvol and organ dose values.

However, because these variations are similar, the difference of organ doses normalized

by CTDIvol across scanners is considerably smaller. This confirms that, for a given

patient, it is possible to generate a set of organ-specific, scanner-independent CTDIvol-to-

organ dose conversion coefficients.

The influence of patient size was investigated by performing Monte Carlo

simulations using a cohort of eight patient models including both genders and that ranged

in size from infant to large adult. This work revealed that for fully-irradiated organs,

xxvi

CTDIvol-to-organ dose conversion coefficients have a strong decreasing exponential

correlation with patient perimeter. The doses to organs completely outside the scan were

essentially negligible. A follow up study revealed that CTDIvol-to-organ dose conversion

coefficients for organs partially-irradiated can also be predicted based on patient

perimeter and an estimate of the percent of the organ included in the scan region.

Additionally, it was shown that the dose reduction effects of tube current modulation

(TCM) can be taken into account based on patient-specific correction factors.

This work demonstrated the feasibility of a comprehensive methodology to

estimate organ dose to patients undergoing CT exams. This method results in patient-and

exam-specific CTDIvol-to-organ dose conversion coefficients that can be used with the

CTDIvol reported by the scanner to calculate absolute dose values. In conclusion, it is

possible to obtain accurate estimates of organ dose to any patient from any scanner,

which represents a significant improvement over current conventional CT dosimetry

practices.

1

Chapter 1 Background and Motivation

X-ray computed tomography (CT) has become an integral diagnostic imaging

modality and is now routinely used within many areas of the medical community. The

use of CT has become the preferred alternative to traditional two-dimensional projection-

based imaging (such as radiography) for a large number of applications because of its

ability to distinguish between overlapping structures that would otherwise be subject to

superposition in the final image.1 Additionally, CT scanners employ a geometry and

filtration design that limits the detection of scattered photons, resulting in its inherently

high contrast resolution.1 In addition to these advantageous, the excellent isotropic spatial

resolution and image quality of modern scanners makes CT an excellent modality for

diagnosing tumors, calcifications, etc. and is regularly used to study structures in the

head, chest, abdomen, and pelvis.

Figure 1.1 CT images of various anatomical regions including A) abdomen, B) chest, C)

head.

The fundamental principles of CT are based on the concept of the Radon

transform: that it is possible to produce a two-dimensional image of an unknown object

from series of one-dimensional projections through that object.1 During a CT exam,

2

projections are obtained by rotating an x-ray source around a patient and continuously

detecting the portion of the radiation that is not attenuated. A simple diagram of a third-

generation CT scanner is shown in Figure 1.2.A. The patient lies on a bed that moves

either incrementally (axial CT) or continuously (helical CT) as the radiation source

rotates (Figure 1.2.B illustrates the source motion of a helical scan). Modern CT scanners

use fan-beams and multiple rows of solid-state detectors (multidetector row CT or

MDCT) to measure the individual x-ray projections from each source position.

Computers are then used to reconstruct multiple two-dimensional axial images, typically

through filtered backprojection algorithms. The final images represent maps of material-

specific mass attenuation coefficients, and thus display detailed representations of the

patient‘s anatomy.

Figure 1.2 A) Diagram of a third-generation CT scanner including the rotating x-ray

source, rotating detector array, and the translating table. B) Illustration of the x-ray source

path for a helical CT scan1.

3

The radiation used for CT exams is generated by x-ray tubes that accelerate

electrons produced by thermionic emission from a filament heated by an electric current

(the cathode) towards a tungsten anode with tube voltages that range from 80 to 140 kV.

Accelerated electrons interact with the tungsten anode causing them to slow down and

emit bremsstrahlung photons with an energy range from ~0 keV up to the peak

kilovoltage (kVp) of the x-ray tube (i.e. 80-140 keV). Low energy photons are typically

reabsorbed by the tungsten anode. Additionally, the tungsten atoms can be ionized due to

electrostatic forces resulting in inner-shell vacancies and, subsequently, characteristic x-

ray emission. A typical tungsten anode spectrum is shown in Figure 1.3.

Figure 1.3 Probability distribution function (PDF) of a typical tungsten anode x-ray tube

spectrum for a tube voltage of 140 kV (i.e. 140 kVp) used for CT scanners.

The fluence of photons in the beam is a function of two factors: a) the kVp and b)

the tube current time product. The ratio of the fluence between two different kVp values

is proportional to the square of the ratios of the kVp values. The fluence is linearly

proportional to the product of the current passing through the cathode filament (mA) and

4

the time the current is applied (s), which is denoted the tube current time product and has

units of mAs. CT x-ray tubes employ filtration material to harden the beam in order to

reduce the number of low energy photons that would have no chance to pass through the

patient. Additionally, specially shaped filters, called bowtie filters, are used to shape the

fan-beam so that more photons pass through the thicker portion of the patient relative to

the thinner part of the patient (this ensures a more even fluence distribution at the

detectors).

1.1 Radiation Risks from CT Exams

CT exams expose patients to ionizing x-ray radiation and therefore result in a

non-trivial increase in the risk of carcinogenesis in adults and particularly in children2-7

The absorbed dose is the metric used to quantify the amount of energy imparted to a

patient or phantom (in Joules) per unit mass (in kilograms). 3,4

The unit for absorbed dose,

or just dose, is the Gray, where 1 Gray = 1 J/kg. While the doses associated with CT are

typically not large enough to result in immediate cell death, the x-rays are energetic

enough to ionize atoms via photoelectric or Compton scattering interactions.6 This

ionization process can lead to DNA strand breaks or base pair damages, either by the

direct ionization of DNA atoms or, more commonly, from the interaction of DNA with

nearby ionized atoms (most notably hydroxyl radicals resulting from ionized water

molecules). Cellular repair mechanisms are usually able to either correctly repair single

or double strand breaks or initiate apoptosis, however, it is possible that DNA will be

repaired incorrectly but the cell will continue to proliferate despite genetic mutations.

5

This results in subsequent replication of incorrect DNA and this is the basic mechanism

for carcinogenesis.

The accurate quantification of the relatively small risks associated with the dose

levels typical of CT exams through epidemiological studies is difficult due to the large

number of subjects required to derive meaningful statistics.7 The most widely studied

cohort of patients for radiation-induced cancer is the survivors of the atomic bombs

dropped on Japan in 1945. It should be noted that these subjects received a single dose of

whole-body radiation which differs from the heterogeneous dose distributions delivered

by individual CT exams. Despite these differences, studies of the atomic bomb survivors

have shown that there is a statistically significant increased risk of carcinogenesis from

the radiation dose levels associated with CT exams and that this risk decreases with age

(less time for cancer to manifest).6 More importantly, it has been shown that the most

appropriate metric for assessing the risk due to diagnostic imaging procedures is the

radiation dose to individual organs.2-6

Conversion factors to calculate the probability of

cancer induction or mortality based on organ doses have been published in the National

Research Council‘s report on the Biological Effects of Ionizing Radiation (BEIR VII –

Phase 2, Tables 12D-1 and 12D-2) for a number of different radiosensitive organs in

males and females with ages ranging from 0 to 80 years.2

Recent studies report that from 1993 to 2006 the number of CT imaging

procedures increased at an annual rate of over 10% in the United States, leading to a

considerable increase in the collective radiation dose from CT.8 Specifically, CT exams

6

now constitute 15% of the total number of radiological imaging procedures, but

contribute more than 50% of the population‘s medical radiation exposure.8 Typically,

patients only receive a single exam, however, some individuals, such as those being

treated for cancer, can receive multiple scans in a short period of time. Regardless,

because the risks associated with CT scans are stochastic in nature and there is no known

threshold dose for carcinogenesis, it is imperative to ensure that the benefits of every CT

scan outweigh the risk. These concerns suggest that it is necessary to properly assess and

monitor the radiation doses being delivered to patients from CT, specifically, the

radiation doses to individual organs.

1.2 Routine Clinical CT Dosimetry Assessment (CTDI and DLP)

The CT dose index (CTDI), introduced by Shope et al.9 in 1991, has become the

standard metric for measuring the radiation dose from a multiple detector row CT

(MDCT) scan.3,10,11

The CTDI is defined as the average dose in the longitudinal center of

a cylindrical phantom from a contiguous axial exam with a scan length much greater than

the width of the x-ray beam. The average dose from a contiguous axial exam to a

cylindrical slab at center of the phantom with a thickness equal to the beam width

(denoted multiple scan average dose or MSAD) is given by:

Eq. 1.1

7

where D(z) is the total dose profile (dose envelope in Figure 1.4), which is the sum of the

dose profiles from each individual rotation, and I is the width of the beam. Shope et al.

demonstrated that, when the distance between each consecutive tube rotation is the same

as the width of the beam, the integral in Equation 1.1 is equivalent to the infinite integral

of the dose profile from a single rotation .9 The beam width for multidetector CT

(MDCT) scanner is the product of the number of detector rows (N) and the width of each

detector (T), so the CTDI is defined as:

Eq. 1.2

where Dsingle(z) is the dose profile along the longitudinal (z) axis from a single axial scan

(single rotation with no table movement).

Figure 1.4 The longitudinal dose profile from a contiguous axial exam. The profile for each

rotation and the summation is shown. Reprinted from C.H. McCollough, et al.11

8

Based on its definition, the CTDI is a theoretical value that cannot be directly

obtained since it is not possible to measure the infinite dose profile. However, since the

dose profile for a single rotation scan for 64-slice MDCT scanners approaches zero when

z=±50 mm, even for the widest collimations, the CTDI can be closely approximated by

measuring the exposure with a 100 mm pencil ionization chamber and electrometer and

then converting to dose.10

This is the fundamental CTDI measurement, denoted CTDI100,

and is described by Equation 1.3:

Eq. 1.3

where f is the conversion factor from exposure to a dose in air (0.87 rad/R), C is the

calibration factor for the electrometer, E is the measured value of exposure in Roentgens

and L is the active length of the ionization chamber (100 mm).

CTDI phantoms are homogenous and constructed of polymethyl methacrylate

(PMMA). Standard CTDI phantoms come in two sizes, a 16 cm diameter ―head‖

phantom and a 32 cm diameter ―body‖ phantom. Head and body CTDI phantoms come

with pre-drilled holes along the longitudinal axis that accept either the pencil ionization

chamber or a PMMA insert, with one hole along the axial center and four along

peripheral positions, as shown in Figure 1.5. The phantoms are positioned with the

central hole at the scanner isocenter and the peripheral holes at 0, 90, 180, and 270

degrees in the gantry. CTDI100 values can be calculated with exposure values measured in

either the center (CTDI100,center) or any of the periphery holes (CTDI100,periphery).

9

Figure 1.5 16 cm diameter “head” and 32 cm diameter “body” CTDI phantoms composed

of PMMA and containing pre-drilled holes at center and four periphery positions.

There are several variants of the CTDI metric that are meant to account for the

heterogeneous dose distributions from CT scans.3,10

The weighted CTDI (CTDIW)

represents the weighted average of the dose at the center and periphery for the central

axial plane of the phantom, as is defined as:

. Eq. 1.4

The volume CTDI (CTDIvol) was defined to account for the dose from non-contiguous

scans, such as helical scans with a pitch not equal to 1, and is defined as:

Eq. 1.5

where pitch is the table movement for each rotation divided by the nominal collimation

(NT). All major scanner manufacturers report the CTDIvol for each scan in a particular

exam on their 64-slice MDCT scanner models. CT dose reports also commonly include

the Dose Length Product (DLP) for the exam, where DLP is defined as:

10

Eq. 1.6

1.3. Limitations of the CTDI

The measurement techniques used to obtain exposure values required to calculate

the CTDI100 and, subsequently, the other CTDI metrics are based on the assumption that

the 100 mm ionization chamber are sufficient for detecting the entire longitudinal beam

profile. As discussed above, this assumption is suitable for 64-slice MDCT scanners

which maximum longitudinal beam widths of 40 mm. Recently, commercial cone beam

CT (CBCT) systems with beam widths wide enough to cover a significant anatomical

length (50-160 mm) in a single axial rotation (e.g., for cardiac CT) have been developed

and are rapidly proliferating in the clinic. The larger beam widths employed by these

CBCT scanners result in significant scatter tails scatter tails (and in some cases, primary

radiation) well outside the detection range of a 100 mm ionization chamber, thus routine

CTDI measurement techniques are not adequate for assessing CBCT dose.12

To address this problem, the American Association of Physicists in Medicine

(AAPM) Task Group 111 has described a new paradigm for assessing CT dose.13

For CT

protocols that involve table translation it is still necessary to measure the dose profile

integral. According to the Task Group 111 report, this measurement should be obtained

by performing the prescribed scan and measuring exposure using a small volume

ionization chamber or a calibrated solid state detector centered in a 45 cm long PMMA

cylindrical phantom.13

AAPM Task Group 200 is currently producing a report to

11

standardize the implementation of this measurement, including the specifics of a new CT

dosimetry phantom.

It is very important to emphasize that both CTDI and AAPM Task Group 111-

type metrics are specifically defined to quantify the dose to simple, homogenous

phantoms. Despite the fact that these metrics are (and will remain) the most common

clinical measurement techniques to assess CT dose and are typically included in patient

dose reports, these values are not meant to be interpreted as actual dose to a particular

patient, or more specifically, to any particular organ.14

The sizes, shapes, and material

compositions of actual patients are considerably different than cylindrical PMMA CTDI

phantoms and only recently has there been an attempt to correct CTDI values for patient

size. AAPM Task Group 204 is currently developing correction factors which are

functions of both age and patient dimensions that can be used to convert CTDIvol values

for 32 and 16 cm diameter PMMA phantoms to pediatric scale water-equivalent doses15

.

Part of Task Group 204‘s results will be based on the results presented in Chapter 6 of

this dissertation.

Instead, the CTDI should be regarded as an index of a scanner‘s radiation output.

As a result, it is a useful tool for dose comparisons between different CT scan protocols

or scanner designs.16

1.4 Effective Dose from CT Exams and its Limitations

12

Effective dose (ED) was introduced as a health physics concept by the

International Commission on Radiation Protection (ICRP) to account for the various

radiosensitivities of the tissues that absorb energy from radiation.2-5,10,11

This quantity is

defined as an estimate of the whole-body radiation dose that would result in an equivalent

stochastic risk as the partial-body imaging procedure, and is mathematically defined as a

weighted average of the dose to several radiosensitive tissues (DT):

Eq. 1.7

where ωT is a tissue-specific radiosensitivity factor whose value is specified by the ICRP

based on epidemiological studies (the ICRP Publication 103 tissue weighting factors5 are

listed in Table 1.1) and ωR is a radiation weighting factor that account for the relative

biological damage imparted from the energy deposition of different types of particles (ωR

for photons is equal to 1. Effective dose is measured in units denoted Sieverts (Sv).

Table 1.1 ICRP Publication 103 recommended tissue weighting factors.5

Tissue ωT Σ ωT

Bone-marrow (red), Colon, Lung, Stomach, Breast, Remainder tissues* 0.12 0.72

Gonads 0.08 0.08

Bladder, Esophagus, Liver, Thyroid 0.04 0.16

Bone Surface, Brain, Salivary glands, Skin 0.01 0.04

Total 1.00

* Remainder tissues: Adrenals, Extrathoracic region, Gall bladder, Heart, Kidneys, Lymphatic

nodes, Muscle, Oral mucosa, Pancreas, Prostate (♂), Small intestine, Spleen, Thymus,

Uterus/cervix (♀).

A method to convert DLP values from CT scans to effective dose using anatomic

region-specific conversion factors (k-factors) was summarized in a report by the AAPM

Task Group 23.3,17,18

The k-factors are listed in Table 1.2. Originally, these k-factors were

13

only derived for a single geometrical patient model, namely the MIRD phantom meant to

represent the ―standard man‖. Despite subsequent work to adapt the factors for different

age groups and patient size ranges, effective dose estimates from k-factors do not take

patient-specific sizes or body habitus into account and therefore are only rough estimates.

It should be noted that effective doses provide only an approximate estimate of the true

risk. As stated above, doses to individual organs is the preferred quantity for optimal risk.

Table 1.2 Normalized effective dose per dose-length product (DLP) for adults (standard

physique) and pediatric patients of various ages over various body regions. Conversion

factor for adult head and neck and pediatric patients assume use of the head CT dose

phantom (16 cm). All other conversion factors assume use of the 32-cm diameter CT body

phantom3

Body Region k (mSv mGy

-1 cm

-1)

0 year old 1 year old 5 year old 10 year old Adult

Head and neck 0.013 0.0085 0.0057 0.0042 0.0031

Head 0.011 0.0067 0.0040 0.0032 0.0021

Neck 0.017 0.012 0.011 0.0079 0.0059

Chest 0.039 0.026 0.018 0.013 0.014

Abdomen ≈&Pelvis 0.049 0.030 0.020 0.015 0.015

Trunk 0.044 0.028 0.019 0.014 0.015

1.5. Existing Organ Dose Estimation Methods

In order to address the limitations of the CTDI, several techniques to quantify

organ doses have been reported. These methods typically involve either (a) physical

measurements in anthropomorphic phantoms or (b) simulations using computational

patient models. There are advantageous and disadvantages to each of these types of

studies.

1.5.1. Physical Phantom Studies

14

Physical dosimetry measurements allow the actual CT scanners and scanning

protocols of interest to be directly evaluated with detectors such as ionization chambers,

Thermoluminescence Detectors (TLD), Metal Oxide-silicon Semiconductor Field Effect

Transistor (MOSFET) detectors, or Optically Simulated Luminescence (OSL) detectors.

The majority of studies employ anthropomorphic phantoms with tissue-equivalent

materials to model the attenuation properties of actual patients.19-26

A number of these

types of phantoms are commercially available in different sizes to model various age

groups and they allow detectors to be placed inside in order to measure point doses.27

There are a number of limitations for studies that use physical measurements to

represent organ doses from CT exams. First, the available anthropomorphic phantoms do

not adequately represent the considerable variations in patient size, habitus, and

composition seen in actual patients (e.g. there is only one adult male sized phantom).

Also, the axial and longitudinal dose distributions from CT exams, especially at the

surface of patients, have considerable variability due to the helical path of the CT source

around the patient (Zhang showed variations up to 50% at the surface of

anthropomorphic phantoms when pitch is 1.5).28

Thus it is not valid to assume that a

point dose measurement within an organ is representative of the actual dose to the entire

organ volume.

Even more important is that, except for air ionization chambers, the majority of

detectors used in physical phantom studies exhibit a significant dependence on energy at

the relatively low x-ray energies of diagnostic imaging. CT x-ray beams are characterized

15

by distinct energy spectra shapes and high fluence. These factors make it difficult to

properly calibrate the energy dependent response of thermoluminescent detectors

(TLD‘s), optically stimulated luminescence detectors (OSL‘s), metal–oxide–

semiconductor field-effect transistor (MOSFET‘s), or other solid state-type detectors.

Making this problem even worse is that that the shape of the energy spectrum changes as

the beam is attenuated so a calibration factor obtained in air may be even worse for

measuring dose in phantom. Ionization chambers do not have a significant energy

dependence, however, they are difficult to imbed in a phantom because they are relatively

large and require an electrical connection to an electrometer.

1.5.2. Monte Carlo Dosimetry Simulations

The use of Monte Carlo radiation transport codes in computer packages that

simulate the delivery of radiation from CT scanners to patient models has become a

popular method of investigating organ dose.29-37

Typically, these codes take into account

scanner-specific characteristics such as x-ray energy spectra, filtration designs, beam

collimation, fan-angle, and pitch. Conventional Monte Carlo radiation transport

techniques are used to track the path of simulated photons through a computational

anthropomorphic phantom and tally the dose deposited in regions of interest.

The Monte Carlo simulation approach was used in the early 1990‘s for dosimetry

studies of single detector row, non-helical CT scanners performed by both the National

Radiation Protection Board (NRPB, Chilton, U.K.)29

and the GSF (National Research

Center for Environment and Health, Institute of Radiation Protection, Neuherberg,

16

Germany)30

. These initial studies simulated dose to the organs in very crude

mathematical phantoms meant to represent the standard human, such as the

hermaphroditic MIRD mathematical phantom (Figure 1.6.A). The organ dose results

reported by the NRPB have been incorporated into the widely used ImPACT CT Patient

Dosimetry Calculator (ImPACT, London, England).38

Methods to extend the results to

current, commercially available helical CT scanners have been developed, for example,

by matching new scanners to those originally simulated based on physical measurements

(such as CTDI). While these methods exist to estimate organ dose, differences between

the NRPB mathematical phantoms and actual patient models as well as inaccuracies

resulting from approximating doses to helical scanners from axial scanners using scanner

matching techniques may result in inaccurate dose estimates.

Figure 1.6 A) Screen shot from the ImPACT Dosimetry Calculator showing the MIRD

mathematical phantom used by the NRPB Monte Carlo Study. B) Adult females from GSF

Family of Voxelized Models.

17

Since these initial studies, a number of different techniques have been employed

by different research groups In order to develop detailed Monte Carlo CT dosimetry

packages that model specific multidetector CT (MDCT) scanners.31-37

These modern

codes typically utilize voxelized patient models that feature detailed organ definitions,

typically generated directly from patient images, either by manual segmentation or by

threshold algorithms based on CT numbers. Examples of detailed voxelized models, the

adult females from the GSF Family of Voxliezed Phantoms39-41

, are shown in Figure

1.6.B. The disparities between the different packages range from fundamental radiation

transport techniques to advanced aspects of modeling MDCT scanners. For example, it is

common to base simulation packages on well-validated, general purpose radiation

transport codes such as the Monte Carlo N-Particle (MCNP) code from Los Alamos

National Laboratory (e.g. the UCLA CT Dose Group31,42

); however, some groups have

created radiation transport code from scratch37

. Also, the methods used to model the

delivery of radiation from CT scanners can be quite different. On an even higher level,

the data sets used to simulate a specific scanner, such as x-ray energy spectrum or

filtration design, can vary across different codes.

CT organ dose studies based on Monte Carlo methods address many of the

limitations of physical measurement studies and have the potential to report very accurate

organ dose results from a wide range of CT scanners and protocols. However, it is

necessary to adequately validate the accuracy of simulations designed to model the dose

from particular CT scanners to specific patient models. Most Monte Carlo modeling

18

publications include descriptions of benchmark experiments carried out to validate the

code. Commonly, simple phantom measurements limited to simple homogeneous

cylindrical objects (such as CTDI) are compared to analogous simulations.

1.6. Discussion

In conclusion, it is clear that while CT is an extremely beneficial and widely used

diagnostic imaging modality it has introduced a non-trivial risk of carcinogenesis to the

population. The current state of CT dosimetry involves measuring the dose to two

different sized homogenous, cylindrical reference phantoms (CTDI with head and body

phantoms) and therefore does not directly assess patient dose14

. Even with attempts to

better characterize newer scanners (AAPM Task Group 111)13

or adjust CTDI

measurements to account for patient size (AAPM Task Group 204)15

, there is still a need

to develop methods of estimating the dose to patient‘s organs. These estimation methods

must account for the variation in dose due to MDCT scanner differences, the dependence

of dose on patient, and how commonly used dose reduction methods, such as Tube

Current Modulation (TCM) effects organ dose values. The overall purpose of this

dissertation is to address these needs by developing and validating a comprehensive

technique to estimate organ doses to any patient from any scanner that has the capability

of accounting for the effects of various scan protocols, including the use of TCM.

19

Chapter 2 Specific Aims

This research is meant to address the limitations of the current MDCT dosimetry

evaluation paradigm by developing novel methods to obtain accurate and meaningful

patient dose estimates. Despite the inherent advantages of Monte Carlo simulation

methods, it is currently not feasible to assess doses to patients on a routine basis in the

clinic. Therefore, in order to move beyond basic phantom dose measurements, the overall

goal of this work was to derive a more generalizable organ dose estimation method that

could be applied to patients undergoing exams on any 64-slice MDCT scanner. This was

done by first developing a method to accurately model the x-ray source characteristics of

any scanner for use in scanner-specific Monte Carlo simulations. Then, these simulation

models were used to show the feasibility of using the CTDI metric as an index to estimate

dose to a given patient from any scanner. Next, the influence of patient size was

investigated in order to extend the estimation method to predict dose to any patient.

Finally, the estimation method was extended to take into account the effects of tube

current modulation (TCM), a common dose reduction technique. The specific aims of this

work were:

Specific Aim 1: To address the limitations of using manufacturer provided source

information, such as photon energy spectrum and filtration designs, (which is often

proprietary), by presenting a method to derive ―equivalent source models‖ that only

require physical measurements obtained on the scanner of interest. The predictive

accuracy of MDCT Monte Carlo simulations using the equivalent source model were

20

assessed and compared to those using manufacturer provided source models. Specific

Aim 1 is the focus of Chapter 4.

Specific Aim 2: To investigate the feasibility of a scanner-independent technique to

estimate organ doses that utilizes the CTDI as an index of scanner tube output. The use of

universal CTDI-to-organ dose conversion coefficients were evaluated in order to predict

dose for a single patient model. Chapter 5 describes the work used to address Specific

Aim 2.

Specific Aim 3: To account for the effect of patient size on CTDI-to-organ dose

conversion coefficients in order to extend the scanner-independent organ dose estimation

method to any patient. Chapters 6 and 7 cover the studies used to investigate Specific

Aim 3.

Specific Aim 4: To evaluate the effect of tube current modulation (TCM) dose reduction

techniques on organ dose values for a large number of patients. Then, the feasibility of

accounting for TCM effects in the calculation of CTDI-to-organ dose conversion

coefficients was assessed. Specific Aim 4 is addressed in Chapter 8.

Specific Aim 5: To address the limitations of commonly used Monte Carlo validation

techniques and present more advanced benchmarking methods. The preliminary work to

assess Specific Aim 5 is described in Chapter 9.

21

Chapter 3 UCLA Monte Carlo MDCT Dosimetry Package

All MDCT dosimetry simulations discussed in this dissertation were performed

using the UCLA Monte Carlo MDCT dosimetry package31,42

. This package is built on the

MCNPX (MCNP eXtended v2.7.a) Monte Carlo radiation transport code developed at

Los Alamos National Laboratory43,44

. As described in detail below, the MCNPX code

was modified to simulate the delivery of x-ray radiation from specific MDCT scanners.

This package was designed to tally doses in patients or phantoms that are specified using

either simple, geometrical descriptions or detailed, voxelized models.

3.1 Radiation Transport Methods

MCNP is a general-purpose Monte Carlo N-Particle code originally designed for

neutron, photon, electron, or coupled neutron/photon/electron transport. Since then, the

MCNPX code has been developed, which tracks nearly all particles at nearly all

energies.44

This code utilizes a Markov Chain Monte Carlo algorithm which simulates the

passage of one particle at a time through a specified geometry until the particle either

leaves the geometry or falls below a preset energy cutoff. This process is repeated a very

large number of times and each particle is unaffected by the behavior of particles

previously simulated

The MCNPX software package consists of a large number of text files that

contain FORTRAN code segments which, together, facilitate statistical particle transport

calculations. MCNPX problems are defined by user-supplied input files that specify the

22

geometry to transport through and tally various quantities in (including the material and

density descriptions), the types and initial conditions of the particles to transport, and the

desired type of tallies (e.g. fluence or energy deposition). Tallies results are calculated on

a simulated photon basis and are reported along with the relative error of the tally

corresponding to one standard deviation. According to the MCNPX User‘s Manual,

results with errors less than 10% are generally (but not always) reliable.44

3.2 Modifications to Model MDCT Scanners

In order to model the delivery of radiation from MDCT scanners the UCLA CT

dose research group created a subroutine, denoted source.f, to specify the source

characteristics for MCPNX MDCT simulations.31,42

The ultimate goal of source.f is to

establish the energy, initial position and trajectory for each simulated x-ray photon. These

values are all randomly selected from scanner-specific probability distributions. The

methods to obtain scanner-specific energy spectra and filtration descriptions will be

discussed in detail in Chapter 4.

The initial three dimensional position of each photon is selected from continuous

sinusoidal functions describing either single axial, translating axial, or helical paths that

depend on the geometry of the scanner (i.e. source to isocenter distance), starting

longitudinal position, starting gantry angle, nominal collimation width, and pitch for

helical scans. The initial trajectory is specified as a three dimensional unit vector

randomly selected based on the starting position, the scanner‘s fan-angle, and actual

beam width (as opposed to the nominal collimation). The actual beam width was obtained

23

by measuring the longitudinal beam profile for a single axial scan at isocenter using OSL

strips and calculating the full width half max value for each scanner and collimation

combination of interest. The energy of each simulated photon is obtained by randomly

sampling from the probability distribution function describing the photon energy

spectrum of the scanner being simulated.

This method of randomly selecting positions and trajectories from continuous

distributions makes it impossible to explicitly define scanner filtration, which varies as a

function of each photon‘s initial conditions. Instead, attenuation due to filtration

(including the bowtie filter) is modeled using the MCNPX source weight feature. The

source weight is a factor that each particle is multiplied by as it is accepted for

transport.44

For each photon, the source weight is calculated by first using the filtration

description for the particular scanner and bowtie filter setting being simulated to

determine the distance the photon travels through the filter based on the photon‘s

trajectory. Then, the resulting attenuation factor is calculated by assuming exponential

attenuation and using the photon mass attenuation coefficient (μ/ρ) of the filtration

material, published by Hubbell and Seltzer45

and applied as the MCNPX source weight

factor. The source weight factor is also multiplied by a factor to account for the inverse

square intensity drop off of a point source of radiation.

All MCNPX simulations were performed in photon mode with a low-energy

cutoff of 1 keV. In this mode photoelectrons are ignored and all deposited energy is

absorbed at the photon interaction site. This assumption satisfies the condition of charged

24

particle equilibrium (CPE) for which the collision kerma (kinetic energy released in

matter from photoelectrons) is equal to absorbed dose and has shown to be valid for the

diagnostic x-ray energy range.31

Thus, the dose to a volume of interest is given by:

Eq. 3.1

where ψE is the total particle fluence for a given energy in the volume, E is the particle

energy (the product of ψE and E is denoted the energy fluence), and (μen/ρ)E,material is the

energy- and material-dependent mass energy absorption coefficient. For each particle, a

*F4 MCNPX tally type is used to score the energy fluence and the MCNPX dose energy

(DE) and dose function (DF) cards are used to multiply the flux by the (μen/ρ)E,material

values, also published by Hubbell and Seltzer45

.

3.3 Post Simulation Processing

The MCNPX simulations described above return dose values that are normalized

on per simulated photon basis. Furthermore, these simulations do not account for the

specific photon fluence for a given nominal collimation on the MDCT scanner of interest.

As a result, an exposure normalization factor is necessary to both convert MCNPX tally

values from dose/source particle to an absolute dose and to take into account the

dependence of beam collimation on photon fluence.

As defined by Jarry, et al.42

, for a given kVp and nominal collimation (NT),

normalization factors are the ratio of measured CTDIair values (dose from a single

25

rotation measured with a 100 mm ionization chamber positioned in air at isocenter,

normalized by the nominal collimation) to analogous CTDIair simulations:

Eq. 3.2

where the measured CTDIair value is normalized on a per total mAs basis (mGy/total

mAs) and the simulated CTDIair is in units of mGy/simulated photons. The resulting ratio

is in units of simulated photons/total mAs and thus serves as a factor to convert MCNPX

results in mGy/simulated photons to values in mGy/total mAs. Note that the total mAs is

the cumulative mAs value over the entire scan, not the mAs value typically quoted by the

scan protocol which refers to mAs/rotation, so:

Eq. 3.3

Therefore, MCNPX simulation results are converted to absolute dose (in mGy) by the

following expression:

Eq. 3.4

where the last term gives the total number of rotations in the scan.

3.4 Validation of Dose Simulations

The validity of MDCT scanner-specific simulations using the Monte Carlo

package described above depends on the accuracy of a) the radiation transport code

(MCNPX), b) the modeling of the source motion and particle trajectory (source.f), c) the

scanner-specific inputs, such as the geometry specifications, the photon energy spectrum,

26

and the filtration description, and d) the precision of the phantom or patient model. Due

to the difficulties in obtaining an absolute dose measurement in anthropomorphic

phantom discussed in Chapter 1, CTDI100 measurements were used to benchmark the

scanner-specific simulation models described in this dissertation. This was done by first

obtaining exposure measurements for center and peripheral CTDI100 values for both the

32 cm diameter and 16 cm diameter CTDI phantoms for 64-slice MDCT scanners from

the four major scanner manufacturers, including: The LightSpeed VCT (General Electric

Medical Systems, Waukesha, WI), SOMATOM Sensation 64 (Siemens Medical

Solutions, Inc, Forcheim, Germany), Philips Brilliance CT 64 (Philips Medical Systems,

Cleveland, Ohio), and Toshiba Aquilion 64 (Toshiba Medical Systems, Inc., Otawara-shi,

Japan). These measurements were obtained on a per mAs basis for all available kVp

values for a number of nominal collimation settings on each scanner. Next, analogous

CTDI100 simulations were performed using the source models for each scanner.

The source models and accuracy of the corresponding CTDI100 benchmark

simulations will be described in detail in Chapter 4. Furthermore, since the CTDI is dose

to a simple, homogenous phantom it is limited in evaluating the accuracy of detailed

patient simulations, so more advanced validation methods will be discussed in Chapter 9.

27

Chapter 4 A Method to Generate Equivalent MDCT Source Models Based on

Measurements†

4.1 Introduction

An accurate MDCT Monte Carlo simulation typically requires a detailed

description of the scanner under investigation, including specifications of the photon

energy spectrum, the bowtie and inherent filtration design, and the geometry of the

scanner (e.g. focal spot to isocenter distance, fan angle, z-axis collimation, cone angle

settings, etc.). It is usually possible to ascertain the necessary geometry from

documentation of scanner specifications. However, scanner-specific source descriptions

that include filtration designs and spectra are typically proprietary, so vendor cooperation

through non-disclosure agreements (or equivalent) has been required to obtain this

information. While in some cases published generalized tungsten anode energy spectra,

either from empirically measured or theoretical models, have been used in Monte Carlo

simulations46

, there is no such published data on the design of bowtie and inherent

filtration, which may vary considerably from scanner to scanner. As a consequence,

† This chapter is based on the following publication:

A. C. Turner, D. Zhang, H. J. Kim, J. J. DeMarco, C. H. Cagnon, E. Angel, D. D. Cody, D. M.

Stevens, A. N. Primak, C. H. McCollough, and M. F. McNitt-Gray, ―A method to generate

equivalent energy spectra and filtration models based on measurement for multidetector CT

Monte Carlo dosimetry simulations,‖ Med. Phys. 36(6), 2154–2164 (2009).

28

MDCT Monte Carlo dosimetry simulations have been performed by a limited number of

researchers who normally can only investigate a small subset of existing scanners for

which they have obtained confidential information to build their source models.

In order to overcome such restrictions the purpose of this work is to introduce a

method to construct source models that only requires physical measurements and

calculations. The goal of this method is to generate an ―equivalent‖ source model that

consists of two parts. The first part is an equivalent energy spectrum, defined as ―an

idealized energy spectrum which results in identical attenuation properties as the actual

spectrum of a 47

‖. The second part is an equivalent filter description, defined as an

idealized filter that attenuates the equivalent spectrum in the same manner that the actual

filter attenuates the actual spectrum (including bowtie filtration and its variation across

the fan angle). Such an approach obviates the need for obtaining proprietary information

and allows the generation of source models to characterize any given scanner. Since this

method is designed to require only measured data taken from the scanner of interest it

should result in more accurate scanner-specific Monte Carlo dosimetry simulations

compared to those that use generic source models.

In this study, first the scanner measurements and calculations necessary to

generate equivalent source models are presented. Then, the predictive accuracy of

equivalent source model MDCT Monte Carlo simulations will be assessed by comparing

the results of multiple CT dose index (CTDI) simulations performed using equivalent

source models with a previously presented Monte Carlo software package31,42

to

29

physically measured CTDI values. Finally, equivalent source model simulations will be

evaluated relative to conventional manufacturer-based source model simulations, first by

comparing the accuracy of CTDI simulations using each type of source model and then

through an analysis of variance to determine if these source models produce statistically

different simulation results.

4.2 Methods

4.2.A. CT Scanner Models

4.2.A.1. The CT Scanners

To investigate the robustness of the proposed method, 64-slice CT scanners from

four major CT scanner manufacturers were included in this study: the LightSpeed VCT

(General Electric Medical Systems, Waukesha, WI), SOMATOM Sensation 64 (Siemens

Medical Solutions, Inc, Forcheim, Germany), Philips Brilliance CT 64 (Philips Medical

Systems, Cleveland, Ohio), and Toshiba Aquilion 64 (Toshiba Medical Systems, Inc.,

Otawara-shi, Japan). Each of these is a third generation, multidetector row CT scanner

that supports multiple nominal beam collimation settings as well as multiple beam

energies. Each scanner is equipped with x-ray beam filtration that includes from one to

three bowtie filter combinations. For this study each different scanner and bowtie filter

combination was assessed separately (the GE LightSpeed VCT has three bowtie filter

settings, the Toshiba Aquilion 64 has two, while the Siemens Sensation 64 and Philips

Brilliance 64 each have one, resulting in seven unique scanner/bowtie filter

30

combinations). Each of the scanner/bowtie filter combinations was randomly assigned a

reference letter, either A, B, C, D, E, F, or G and will be referred to by their assigned

letter from this point on.

4.2.A.2. Source Models based on Manufacturer-Provided Information

Data describing the x-ray source for each scanner described in 4.2.A.1 was

obtained from the manufacturers under a non-disclosure agreement. Each manufacturer

provided a description of the x-ray energy spectra representing the relative number of

photons at each energy level for each available kVp setting. Additionally, they provided

specifications of scanner filtration by specifying the dimensions and materials of all

available bowtie filters as well as the design of any other inherent filtration. The scanner

geometry necessary for the Monte Carlo simulations, namely the focal spot to isocenter

distance and fan angle, were also obtained directly from the manufacturers; however, this

information is usually available in user manuals or specification sheets included in CT

scanner documentation.

4.2.B. Measurements to Generate Equivalent Source Models

4.2.B.1 Overview of Physical Measurements Used to Generate Equivalent Source

Models

The scanner measurements required of this method are generally not part of

routine medical physics measurements for CT, but can be performed reasonably quickly

and efficiently with commonly used equipment. It should be noted that some scanners

31

must be put into service mode because these measurements are performed with a non-

rotating (stationary) gantry. For each scanner\bowtie filter combination, two types of

measurements were obtained: (a) half and quarter value layers (HVL and QVL, note that

these will be referred to as HVL measurements) and (b) bowtie filter attenuation profiles.

Each requires a set of exposure measurements which were performed with a standard 100

mm pencil ionization chamber (ion chamber) and calibrated electrometer.

4.2.B.2. Half Value Layer Measurements

The method used to measure MDCT HVL values is similar to standard HVL

measurements used for conventional radiograph machines. The gantry was parked so that

the x-ray tube remained stationary at the 6 o‘clock position. The ion chamber was fixed

along the central ray (directly above the stationary x-ray tube), ensuring the table was not

in the x-ray beam path, at a distance above the source sufficient to establish good

measurement geometry (for all measurements the ion chamber was positioned at or above

the scanner isocenter). An initial exposure value was taken using a particular kVp, mAs,

and collimation setting. Additional exposure measurements were obtained using the same

settings, adding thin slabs (0.5 mm – 2.0 mm) of type 1100 alloy aluminum in the beam

path until the resulting exposure was less than half the initial value to obtain the HVL and

less than a quarter of the initial value to obtain the QVL. The experimental set up is

illustrated in Figure 4.1. For scanner/bowtie filter combinations A-G, measurements were

performed to determine the HVL and QVL for all available beam energies.

32

Figure 4.1 Diagram of HVL measurement set up that utilizes a stationary (non-rotating) x-

ray source.

4.2.B.3. Bowtie Profile Measurements

Bowtie profile measurements were performed to characterize the attenuation of

the actual spectrum across the fan beam due to the scanner‘s bowtie and inherent

filtration. The gantry was parked so that the x-ray tube was fixed at the 3 o‘clock

position. The ion chamber was clamped to a ring stand which was placed so that the

active portion of the chamber was not directly above the patient table. The table was

adjusted so that the ion chamber was initially centered at the scanner isocenter. Using 120

kVp, 300 mAs, and a fixed collimation setting (a single beam energy, tube current, and

collimation was sufficient for this method), exposure measurements were incrementally

obtained by moving the table in 5-10 mm intervals in the +y direction in order to profile

the exposure attenuation from the upper half of the bowtie filter. A diagram of this set up

is shown in Figure 4.2. Because the range of the table‘s vertical motion was usually

33

insufficient to sample the entire upper half of the fan beam, the necessary data was

acquired by (1) initially clamping the ion chamber to the base of the ring stand, then (2)

incrementing the table position vertically to its limit, then (3) sliding the chamber a

known vertical distance in the +y direction along the ring stand and lowering the table by

the same distance, and finally (4) continuing the vertical table incrementation in the +y

direction until the entire upper half of the axial plane (i.e. the half fan angle) was

sampled. It is assumed that the attenuation profile in the axial plane is symmetric about

the central ray (θi = 0 in Figure 4.2), so only measuring the upper half of the bowtie‘s

attenuation is sufficient.

Figure 4.2 Diagram of bowtie profile measurements that characterize the attenuation across

the fan beam.

The value of the angle for a given measurement, θi in Figure 4.2, was calculated

using the manufacturer-provided focal spot to isocenter distance, L, and the vertical

distance the ion chamber was moved from isocenter, li:

34

Eq. 4.1

This procedure was carried out to obtain bowtie profile measurements for scanner/bowtie

filter combinations A-G using the scan protocol described above.

4.2.C. Computational Methods to Generate the Equivalent Source Models

4.2.C.1. Overview of Equivalent Spectrum Generation Algorithm

The goal of the first part of the source generation algorithm is to produce an

equivalent spectrum for a given scanner, bowtie filter setting, and beam energy

characterized by HVL values similar to those physically measured. This approach does

not assume prior knowledge of the scanner‘s actual spectrum or filtration scheme. Three

inputs are necessary for this algorithm: a) the HVL measurements for the scanner of

interest, b) an initially soft (low average energy and therefore small HVL) tungsten anode

x-ray energy spectrum, and c) an arbitrarily defined description of the material and

central ray thickness of a corresponding equivalent bowtie filter (which will remain

constant throughout this process). Specifically, this approach assumes an equivalent

bowtie filter composed of aluminum with a central ray thickness of 0.5 mm. While this

may not be the actual material or central ray thickness for any actual bowtie filter, this

assumption will be shown to be reasonably robust for this methodology. The general

algorithm is outlined in this section and details are provided in subsequent sections.

The following steps, illustrated in Figure 4.3, are used to obtain the equivalent

spectrum: 1) the input soft tungsten anode spectrum (represented by the upper probability

35

density function [PDF] in Figure 4.3) is transmitted through a very thin, uniform sheet of

an arbitrarily defined ―hardening‖ material and the number of remaining x-ray photons at

each energy is calculated, assuming exponential attenuation, producing a ―candidate‖

spectrum (represented by the lower PDF in Figure 4.3), then, 2) the spectrum resulting

from transmitting the candidate spectrum through the central ray of the bowtie is

calculated and the associated kerma in air is subsequently computed by summing the

product of the energy fluence and the mass energy-absorption coefficient for air over all

energies, next, 3) the spectrum resulting from transmitting the candidate spectrum

through the central ray of the bowtie plus a very thin, uniform sheet of aluminum is

calculated and the kerma in air is again computed, then, 4) Step 3 is repeated while

incrementally increasing the thickness of aluminum by 1.0 μm until the calculated kerma

in air is a factor of two and then a factor of four less than the initial kerma in air obtained

in Step 2. Since kerma in air is directly proportional to exposure these thicknesses of

aluminum represent the HVL and QVL of the candidate spectrum. Steps 1-4 are repeated

while incrementally increasing the thickness of the hardening material (thus increasing

the HVL values of the candidate spectrum) by 10.0 μm until the difference between the

candidate spectrum‘s calculated HVL values and the measured values are minimized.

Since this method assumes the exact material and design of the filtration is unknown, the

entire process is repeated using various hardening materials that are often used in scanner

construction, namely aluminum, graphite, lead, and titanium. The candidate spectrum

36

with calculated HVL values that best match the measured HVL values, regardless of the

hardening material type, is deemed the equivalent spectrum.

Figure 4.3 Illustration of method for generating equivalent spectrum from measured.

The initial tungsten spectrum referred to in step 1 was obtained using Boone and

Seibert‘s tungsten anode spectral model using interpolating polynomials (TASMIP).48

Siewerdsen, et al., created SPEKTR, a MatLab (the MathWorks, Natick, MA) tool that

allows a user to obtain TASMIP spectra with an energy reslution of 1.0 keV from the

TASMIP library while specifying the beam energy (kVp), percent voltage ripple, and any

beam filtration.49

For each kVp setting available on the scanners described in 4.2.A.1 a

soft tungsten spectrum was obtained via the SPEKTR tool using no added filtration and

25% voltage ripple. In each instance this created an initial spectrum with sufficiently low

average beam energy and thus initial HVL values less than any of the measured HVL

37

values. The exponential attenuation and kerma in air calculations were performed using

the photon mass attenuation coefficients (μ/ρ) and mass energy-absorption coefficients

(μen/ρ) for air, reported by Hubbell and Seltzer45

, respectively.

4.2.C.2. Equivalent Spectrum Generation Algorithm using both HVL and QVL

The QVL is the highest order descriptor of a particular x-ray beam obtained in the

measurements described in 4.2.B.2, so equivalent spectra were first generated to match

measured QVL values. Specifically, the algorithm described in the previous section was

carried out to produce candidate spectra for each hardening material type which had

calculated QVL values approximately equal to measured QVL values. For each specific

hardening material and thickness that yields the best estimate of QVL, the HVL was also

calculated. The equivalent spectrum was chosen as the candidate spectrum that both

matched QVL and simultaneously had a calculated HVL that best matched the measured

HVL. For scanner\bowtie filter combinations A-G, equivalent spectra were generated

using HVL and QVL measurements for all available beam energies using routines coded

in MatLab. Source models using spectrum resulting from this algorithm will be denoted

as the HVL&QVL source models.

4.2.C.3. Equivalent Spectrum Generation Algorithm using only HVL

Another set of equivalent spectra were generated in a similar manner to that

described in Section 4.2.C.2 with the exception only the measured HVL was considered.

These alternative forms of equivalent spectra allowed us to investigate the necessity of

38

measuring the QVL, which can be cumbersome. Again, the algorithm described in

Section 4.2.C.1 was carried out to produce a candidate spectrum for each of the

hardening material types, but in this case the spectrum was generated so that its

calculated HVL approximately matched the measured HVL. The equivalent spectrum

was then determined by simply selecting the candidate spectrum whose calculated HVL

had the best agreement with the measured value. For scanner\bowtie filter combinations

A-G, equivalent spectra were generated using only HVL values for all available beam

energies using routines coded in MatLab. Source models using spectrum resulting from

this algorithm will be denoted as the HVL source models.

4.2.C.4. Equivalent Bowtie Filter Generation Algorithm

The second part of the source generation algorithm is performed after acquiring

an equivalent spectrum. The goal of this part is to obtain a description of an equivalent

bowtie filter (filtration pathlength as a function of θ in Figure 2) that attenuates the

equivalent spectrum in the same manner that the actual bowtie filter attenuates the actual

x-ray beam across the entire fan angle. For this part of the algorithm the following inputs

are necessary: a) the equivalent spectrum (generated using the methods described in

either Section 4.2.C.2 or 4.2.C.3) for the scanner\bowtie filter combination and beam

energy of interest, and b) the bowtie profile measurements made for the same

scanner\bowtie filter combination. As stated in Section 4.2.C.1, the equivalent bowtie

material is arbitrarily defined to be aluminum with a central ray thickness of 0.5 mm.

39

Again utilizing the fact that exposure is directly proportional to kerma in air, the

equivalent pathlength of aluminum for a given bowtie profile measurement angle, θi, is

generated from the following steps, 1) using the bowtie profile measurement data, the

ratio of the measured exposure at θi to the measured central ray exposure is computed,

then, 2) the equivalent spectrum is numerically transmitted through the center portion of

the equivalent bowtie, again assuming exponential attenuation, and the subsequent kerma

in air is calculated as described in 4.2.C.1, next, 3) the equivalent spectrum is transmitted

through a very thin, uniform sheet of aluminum and the subsequent kerma in air is

calculated, then, 4) the ratio of the kerma in air obtained in Step 3 to the kerma in air

from Step 2 is computed, 5) Steps 3 and 4 are repeated while incrementally increasing the

thickness of aluminum by 1.0 μm until the difference between the values obtained in Step

1 (measured exposure ratio) and Step 4 (calculated exposure ratio) is minimized. The

resulting thickness of aluminum is deemed the equivalent pathlength for θi. This process

is repeated for each measurement angle sampled in the bowtie profile measurements

producing the equivalent bowtie filter description.

The method to iteratively determine the aluminum bowtie filter pathlength for

each measured angle was implemented using routines coded in MatLab. This algorithm

was carried out using each of the equivalent spectra generated in Sections 4.2.C.2 and

4.2.C.3. The result was a complete set of equivalent source models (e.g. spectrum and

bowtie description) based on both HVL and QVL measurements (HVL&QVL models) as

well as a complete set of equivalent source models based solely on HVL measurements

40

(HVL models) for each beam energy available on scanner/bowtie filter combinations A-

G.

4.2.D. Monte Carlo Simulations

All simulations were performed using the UCLA MDCT Monte Carlo dosimetry

package described in Chapter 3. Simulations were performed in order to validate the

accuracy of doses calculated using the equivalent source model variants described above.

Specifically, simulations of scanner- and collimation-specific CTDI100 values were

obtained for comparison with measurement. As a result, only single axial scans were

simulated for this work.

4.2.E. CTDI100 Phantom and Pencil Ion Chamber Models

Conventional CTDI100 experiments using the standard head (16 cm diameter) and

body (32 cm diameter) phantoms were used for validation and comparison purposes.10

As

introduced in Chapter 1, these phantoms are PMMA cylinders that are 15 cm in length.

The phantoms, PMMA inserts, and the pencil ionization chamber were all simulated

using standard MCNPX geometry and material descriptions. The ionization chamber was

explicitly modeled as two concentric cylinders, with a 1.6 mm thick outside cylindrical

shell consisting of C552 to model the chamber wall and an inner cylinder of air that is 3.4

cm in diameter and 10 cm in length to represent the active portion of the chamber.

4.2.F. CTDI100 Simulation and Measurement Experiments

41

Using a particular scanner/bowtie filter combination and beam energy, a series of

CTDI100 measurements (in mGy/mAs) were obtained at both the center and periphery (12

o‘clock) positions for both CTDI phantoms. Analogous CTDI100 simulations were

performed using three different source model types: those based on information provided

by the manufacturer described in Section 4.2.A.2, the HVL equivalent source models

described in Section 4.2.C.3, and the HVL&QVL equivalent source models described in

Section 4.2.C.2. MCNP tally results are were converted to dose values in units of

mGy/mAs using the normalization process discussed in Section 3.3.

CTDI100,center and CTDI100,periphery measurements and simulations were performed for

scanner\bowtie filter combinations A-G at each available beam energy. The possible kVp

settings varied among the scanner manufacturers. Four of the scanner\bowtie filter

combinations (A, C, E, and G) allow 80, 100, 120, or 140 kVp scans, two (B and F) allow

80, 100, 120, or 135 kVp scans, and the last (D) allows 80, 120, or 140 kVp scans. This

resulted in 108 unique possible measurement conditions (6 scanner/bowtie combinations

x 4 kVp settings x 2 phantoms x 2 positions + 1 scanner/bowtie combination x 3 kVp

settings x 2 phantoms x 2 positions). All 108 conditions were simulated using both

equivalent source model types. Only 120 kVp source specifications were supplied by the

manufacturer of scanner/bowtie filter combination D so only 100 of the measurement

conditions could be simulated using the manufacturer-based source models (noting that

direct comparisons to simulations utilizing manufacturer‘s data could only be done at 120

kVp for this particular combination).

42

Each CTDI100 measurement and corresponding simulation was performed using one

available nominal collimation, which also varied among scanner manufacturers.

Depending on the manufacturer, the nominal collimation value used for each experiment

was one of the following: 4 x 5 mm (20 mm) with a FWHM of 21.5 mm, 20 x 1.2 mm

(24 mm) with a FWHM of 27.9 mm, 4 x 8 mm (32 mm) with a FWHM of 36.9 mm, or

64 x 0.625 mm (40 mm) with a FWHM of 43.7 mm.

4.2.G. Evaluation of the Source Models

4.2.G.1. Comparison of CTDI Simulations to Measured Results

The results of the CTDI100 simulations using each of the three source model types

described above were separately compared to the analogous measured CTDI100 values.

The percent error between each simulation and measurement result was calculated to

evaluate the accuracy of the simulations performed with each individual source model

type. Then, for each source model type, the root mean square (RMS) of the percent error

values were calculated across all kVp values for each scanner/bowtie filter combination

using the results of both the center and 12 o‘clock measurement positions on both the

head and body CTDI phantoms. These RMS values serve as metrics to independently

evaluate the predictive accuracy of the three source model types for each individual

scanner/bowtie filter combination.

4.2.G.2. Comparison of Equivalent and Manufacturer Source Models

43

The percent agreement between simulated and measured CTDI100 results were

used to compare the performance of the different source model types under investigation

based on (a) a scanner/bowtie filter combination basis and (b) pooling all scanner/bowtie

filter combinations. Part (b) of this analysis provides a metric to determine if simulations

using source models based on manufacturer-provided data, HVL equivalent source

models, or HVL&QVL equivalent source models have the best overall performance in

terms of accurately predicting the measured CTDI100 value.

Analysis of variance (ANOVA) tests were performed to determine whether the three

types of source models produce simulation results that are statistically different from each

other. First, all results underwent log-transformation to satisfy the normality assumption

in the ANOVA test. ANOVA methods were then used to compare the results from the

three source model types, taking into account the seven scanner/bowtie categories, all

kVp‘s, both sized phantoms, and both chamber positions. If there was a significant

difference between the results for a given scanner/bowtie filter combination for the

different source model types, pair-wise tests were used to compare the three methods on a

stratified scanner/bowtie filter combination basis. For each stratified scanner/bowtie

category, ANOVA analyses were used to compare the three types of source models. A

Bonferroni adjustment was used as post-estimation of multiple comparisons if there was

significant difference among the three methods.

Finally, another analysis was used to determine whether the overall performance

of the three source model types were statistically similar to each other at varying levels of

44

desired accuracy. To do this, a categorical variable was used which was the level of

agreement between measured and simulated results with values of: 1 – indicating

outstanding agreement of within +/- 1%, 2 – representing excellent agreement of greater

than +/- 1% but within +/- 2%, 3- representing very good agreement of greater than +/-

2% but within +/- 5%, 4- representing good agreement of greater than +/- 5% but within

+/- 10% and 5- representing agreement that is greater than +/- 10%. For the various

agreement thresholds, a Generalized Estimating Equations (GEE) population-averaged

model was performed to compare the accuracy of the simulations employing the three

source model types using compound correlation structure as levels of agreement (i.e. 1%.

2%, 5%, and 10%) in binomial family with logit link. Furthermore, multivariate logistic

was performed at each level of agreement to compare the individual source model

simulation results. This analysis reveals whether each type of source model produces

statistically different simulation results from those produced by the other types of source

models for a specific level of predictive accuracy; therefore, the results will determine the

level of accuracy at which the HVL models provide statistically different simulation

results than the HVL&QVL source models. This will help answer the question of whether

HVL and QVL measurements are both necessary when employing the proposed

equivalent model source model generation method.

4.3 Results

The measured HVL values (HVL and QVL) described in Section 4.2.B.2 are

presented in Tables 4.1 and 4.2, respectively. These values include measurements for

45

each of the seven scanner/bowtie combinations at each of the available beam energies.

The mean value is presented along with a summary of the minimum and maximum HVL

values to illustrate the range for a given beam energy value across different

scanner/bowtie filter combinations.

Table 4.1 – Measured first half value layers (HVL) in mm Al for each scanner/bowtie

combination at each available beam energy.

Beam

Energy

(kVp)

Scanner/Bowtie Filter Combination

A B C D E F G Mean Minimum Maximum

80 6.0 4.7 5.4 6.4 4.5 3.5 4.5 5.0 3.5 6.4

100 7.4 5.8 6.6 -- 5.6 4.5 5.6 5.9 4.5 7.4

120 8.5 7.1 7.8 8.9 6.6 5.5 6.6 7.3 5.5 8.9

135 -- 7.9 -- -- -- 6.1 -- 7.0 6.1 7.9

140 9.5 -- 8.8 9.8 7.6 -- 7.6 8.6 7.6 9.8

Table 4.2 – Measured second half value layers (QVL) in mm Al for each scanner/bowtie

combination at each available beam energy.

Beam

Energy

(kVp)

Scanner/Bowtie Filter Combination

A B C D E F G Mean Minimum Maximum

80 13.3 11.0 12.2 13.7 10.6 8.4 10.7 11.4 8.4 13.7

100 16.2 13.5 15.2 -- 13.2 10.7 13.2 13.7 10.7 16.2

120 18.8 15.5 17.0 19.6 16.0 13.6 16.1 16.7 13.6 18.8

135 -- 17.3 -- -- -- 14.7 -- 16.0 14.7 17.3

140 21.0 -- 19.8 22.0 18.3 -- 17.8 19.8 17.8 21.0

The results for each of the CTDI100 measurement and simulation experiments

described in Section 4.2.F are presented for each scanner/bowtie filter combination in

Tables 4.3-4.9 in Appendix A. These tables also display the percent difference between

each measurement and corresponding simulation for the three source model types. The

agreement between simulation and measurement was within 10% for 103 of the 108

experiments using HVL& QVL source models. Similarly, agreements were within 10%

46

for 102 of 108 experiments using HVL source models. Only 49 of 100 simulations using

manufacturer-based source models were within 10% of the analogous measurement. For

the HVL&QVL and the HVL methods, the only beam energy setting for which

simulations and measurements disagreed by > 10% was 80 kVp.

The root mean square (RMS) of the percent error value across all beam energies

(kVp‘s), both CTDI phantoms, and both measurement positions for simulations using each

type of source model is shown in Table 4.10 for each scanner/bowtie filter combination.

For five out of the seven scanner/bowtie filter combinations (A, C, D, E, and G) the source

models obtained using the HVL& QVL method resulted in smaller RMS percent error

values than did the other two source models. For combinations B and F simulations the

HVL method‘s source models had the smallest associated RMS value. The bottom row of

Table 4.10 displays the mean RMS percent error values across all scanner/bowtie filter

combination for each source model. This pooled RMS value for HVL&QVL method is

slightly less than that of the HVL method and substantially less than that of the

manufacturer-based source model simulations.

47

Table 4.10 – Root Mean Squared (RMS) percent error for each scanner/kVp/bowtie

combination as well as pooled across all scanner/bowtie combinations.

Scanner/Bowtie

Combination

Manufacturer-

based source model HVL source model

HVL&QVL source

model

A 5.50 5.38 4.14

B 10.62 6.25 7.18

C 12.60 5.39 4.02

D 2.56 2.76 2.52

E 11.83 4.31 3.80

F 20.18 7.40 7.72

G 9.51 3.89 3.37

Pooled 12.50 5.34 5.11

The results of the ANOVA test described in Section 4.2.G.2 are summarized in

Table 4.11. Combinations A and D showed no significant difference between any of the

source model types. The remaining combinations did show significant difference, thus

the results of each individual source model type were compared to each of the others on a

stratified scanner/bowtie category basis. For these analyses Bonferroni adjusted p-values

were used for the comparisons. For each combination (B, C, E, F, and G), the source

models based on manufacturer-provided data produced significantly different results than

either the HVL or HVL&QVL equivalent source models while the HVL method‘s results

were not significantly different than those of the HVL&QVL method.

48

Table 4.11 –ANOVA analyses results. If significant differences were found amongst the

three methods the pair-wise ANOVA results are shown individually. Bonferroni adjustment

was used as post-estimation of multiple comparisons if there was significant difference

among the methods.

Scanner/Bowtie

Combination

Manufacturer-provided

vs. HVL

Manufacturer-provided

vs. HVL&QVL

HVL

vs. HVL&QVL

A Not different

p = 0.1738

B Different

p < 0.0001

Different

p < 0.0001

Not different

p = 0.1000

C Different

p < 0.0001

Different

p < 0.0001

Not different

p = 0.6500

D Not different

p = 0.7379

E Different

p < 0.0001

Different

p < 0.0001

Not different

p=0.1421

F Different

p < 0.0001

Different

p < 0.0001

Not different

p = 0.8533

G Different

p < 0.0001

Different

p < 0.0001

Not different

p = 0.6363

A plot of the number of cases at each level of agreement curves described in

Section 4.2.G.2 is shown in Figure 4.4. The five categorical variables used in this analysis

(1-5) represent absolute levels of agreement between simulation and measurement with

values of < 1%, < 2%, < 5%, < 10%, and > 10%, respectively. For each source model, the

plot shows the cumulative percentage of total simulations that fall into each level of

agreement category. The GEE population-averaged model performed to compare the three

methods discussed in Section 4.2.G.2 resulted in four p-values < 0.05 indicating there are

overall differences among the three source model types. The multivariate logistic analyses

performed to separately compare each of the source model types with each of the others at

each individual level of agreement revealed the manufacturer-based source models

produced significantly different results, at each categorical level of agreement, from both

49

the HVL source models (all p-values < 0.0001) and the HVL&QVL source models (all p-

values < 0.0001). The HVL and HVL&QVL source model comparisons resulted in p-

values indicating no significant difference between the two methods for all levels of

agreement (p=0.1976 for 1%, 0.1003 for 2%, 0.6284 for 5%, and 0.7405 for 10%

agreement).

Figure 4.4 The cumulative percentage of CTDI100 simulations that are characterized by the

level agreement with measured CTDI100 values specified by each category: (1: ≤±1% 2:

>±1% but ≤±2% 3: >±2% but ≤±5% 4: >±5% but ≤±10% 5: >±10).

4.4 Discussion

The goal of this study was to present a method to obtain CT scanner source models

based only on measured values and validate their use for Monte Carlo dosimetry

simulations. These source models partially consist of an equivalent x-ray spectrum

generated to match measured HVL values. A method to obtain HVL values was described

that requires parking the scanner gantry, which may require the scanner to be switched

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5

Cu

mu

lati

ve

Per

cen

tag

e

Category of Agreement

Source Model

based on

Manufacturer

Information

HVL1

Equivalent

Source Model

HVL1&HVL2

Equivalent

Source Model

HVL&QVL

Equivalent

Source

Model

HVL

Equivalent

Source

Model

50

into service mode. Alternative techniques to measure HVL values for CT scanners that do

not require a stationary source have been proposed, but typically require special

equipment.50,51

The second part of the proposed source model is an equivalent filtration

description that is generated to attenuate the equivalent spectrum across the fan angle in

the same manner that the actual filtration attenuates the actual spectrum, as measured by

the bowtie profile measurements.

Two different types of equivalent source models, those based only on HVL

measurements and those based on both HVL and QVL measurements, along with

manufacturer provided-source models were evaluated in this study. Validation

experiments were performed by assessing the accuracy of multiple CTDI100 simulations

using all three source model types for each of the available beam energies on the

scanner/bowtie filter combinations described in the methods section. Inspection of Tables

4.3-4.9 show that simulation results agreed with measurements to within 10% for 103 of

the 108 (95.4%) and 104 of the 108 (96.3%) experiments performed using the HVL and

HVL&QVL source models, respectively. Simulations utilizing manufacturer-based source

models only achieved agreement ≤ 10% for 49 out of 100 simulations. Thus, assuming an

acceptable agreement level of 10%, it is apparent that simulations using the equivalent

source models attained the necessary accuracy for validation much more frequently than

simulations using source models based on manufacturer data.

The RMS of the percent error values reported in the bottom rows of Tables 4.3-4.9

and summarized in Table 4.10 serve as a metric to compare the simulation accuracy for a

51

particular source model on a scanner/bowtie combination basis. Analyzing the RMS value

across all beam energies (and across both CTDI phantoms and measurement positions)

provides a thorough evaluation of a source model‘s overall performance for a particular

scanner/bowtie filter combination. The HVL&QVL source model simulations resulted in

better predictive accuracy (smaller RMS percent error values) than the other two types of

source models for five of the seven scanner/bowtie filter combinations, while the HVL

source models simulations had a lower RMS value for the remaining two combinations. It

should be noted that for some scanner/bowtie filter combinations, similar performance

was observed between all three source model types (i.e. combinations A and D) and some

combinations resulted in substantial differences between the manufacturer source models

and the equivalent source models (i.e. combinations B, C, E, F, and G). However, for all

combinations the two equivalent source model types had relatively small differences in

their predictive accuracy.

The pooled RMS values across all scanner/bowtie filter combinations presented in

the bottom row of Table 4.10 indicate that the overall performance, on a whole, of the

HVL&QVL was slightly better than that of the HVL source model and that both of the

equivalent source models were superior to the source models based on manufacturer-

provided data. Both of the equivalent source types had associated mean RMS values

across all combinations < 6% (5.34% and 5.11% for the HVL and HVL&QVL source

models, respectively) while the manufacturer-based source model simulations exceeded

10% (12.50%).

52

Tables 4.4 and 4.8 illustrate that for scanner/bowtie filter combinations B and F the

80 kVp CTDI percent error values are relatively large for the equivalent source models

(these simulations accounted for most of the equivalent source simulations that did not

meet the 10% difference metric). One reason for this might be that, for diagnostic energy

ranges, exposure is approximately proportional the square of the kVp (when keeping the

tube current fixed). As a result, if the mAs setting for 80 kVp HVL and bowtie profile

measurements are too low the exposure values may have inherent error from quantum

noise due to insufficient tube output. This is especially true for source models generated

based partly on QVL since the large amount of aluminum filtration used to obtain the

necessary exposure values significantly reduces the number of photons detected by the ion

chamber. This hypothesis could not be immediately tested due the limited accessibility of

the scanners employed for this study, however additional work should be done to

investigate the effect of the tube current setting on 80 kVp equivalent source simulation

results and possibly determine a minimum mAs threshold setting to obtain simulation

results with the desired accuracy level. This could be especially relevant for estimating

dose to pediatric patients, where 80 kVp scans are more commonly used.

The CTDI percent error values shown in Table 4.6 for scanner/bowtie filter

combination D reveal that the manufacturer-based source models resulted in a relatively

small RMS value (2.56%) compared to the other scanner/bowtie filter combinations. As

noted in the methods section, for this scanner/bowtie filter combination it was only

possible to evaluate 120 kVp manufacturer-based source model simulations therefore the

53

reported RMS value only takes one beam energy setting into account. It is unclear whether

a full data set from the manufacturer would improve or worsen the RMS value. However,

the lack of available data suggests another advantage to using the equivalent source

method. Since the proposed method is based strictly on measurements it is possible to

obtain source models for any kVp and bowtie filter combination available on the scanner

of interest.

The ANOVA analyses performed on a scanner/bowtie filter combination basis

showed that for five out of the seven scanner/bowtie filter combinations both the HVL and

HVL&QVL equivalent source model simulations produced significantly different results

than did the manufacturer-based source model simulations in terms of predictive accuracy.

Since it has already been established that the equivalent source model simulations were

more accurate in predicting measured CTDI values, this analysis demonstrates that there is

a statistically proven benefit to using either the HVL or HVL&QVL equivalent source

methods rather than manufacturer-based source models. In the other two cases

(combinations A and D) the manufacturer-provided data proved to produce simulation

accuracy that was statistically similar to the equivalent source simulations.

Finally, the overall performance of the three types of source models were

statistically compared by utilizing categorical variables indicating level of agreement

between simulated and measured values. These analyses allowed comparisons to be made

at individual levels of accuracy. The results showed that at each assigned level of

agreement (< 1%, < 2%, <5%, and < 10%) the equivalent source model simulations

54

significantly outperformed the manufacturer-based source model simulations.

Comparisons of the HVL&QVL method with the HVL method proved there is no

statistical difference between the two types of equivalent source models at any level of

agreement. This suggests that, for any desired level of accuracy, it is not necessary to

measure QVL, which can be a time consuming, cumbersome task.

In order to encourage the use of this method a goal of this study was to present a

simple, heuristic approach to generating equivalent source models rather than proposing

more sophisticated optimization algorithms. It is possible that applying stricter

requirements when generating the equivalent spectra, such as requiring the candidate

spectrum optimization function to simultaneously take into account both HVL and QVL

measurements, might result in simulations with greater accuracy. It should also be noted

that while a numerical spectrum generation algorithm was presented in this work, a well-

validated analytical method exists to determine spectra from measured attenuation curves

via the Laplace transform.52,53

Improved results might also be obtained by using some

optimal combination of material types for the equivalent bowtie filter or hardening

materials. Further exploration of such alternative source model generation techniques

should be encouraged; however, the easy-to-implement method proposed in this work has

been shown to result in accurate and robust simulations across an extremely wide range of

validation experiments. To encourage further investigations, a full set of required

measurement data (HVL values and bowtie profile) and the resulting equivalent source

55

models (HVL and HVL&QVL) for one scanner/bowtie filter combination has been made

available at http://medqia.org/~mcnitt/Equivalent_Source/.

The use of equivalent source models generated by the proposed method has

considerable advantages over the use of manufacturer-provided data. In addition to

obviating the need to obtain confidential information via some type of non-disclosure

agreement, this method produced simulation results that more accurately matched

physical measurements. Data supplied by the manufacturer is usually provided for a

specific combination of x-ray tube, bowtie filter, and even software version for a

particular scanner. Subsequent models of the same scanner may not feature the same

combination of attributes (e.g. different software version, different x-ray tube, etc) and

thus any previously supplied data may not exactly characterize the actual scanner being

evaluated. These apparently minor differences are very difficult to discern and could

partly explain why the manufacturer-based models did not perform as well as the

equivalent source models. On the other hand, equivalent source models are based on

scanner-specific measurements. Since any scanner modifications may alter the HVL

and/or bowtie profile measurements, the equivalent source method will naturally factor

them into the resulting source models accordingly.

In this study we have described a novel method to generate source models using

only measured values for MDCT Monte Carlo dosimetry simulations and have

demonstrated their ability to produce highly accurate simulations over a wide range of

scanners and bowtie filter combinations. These equivalent source models consist of unique

56

spectrum and filtration combinations based on scanner-specific measurements, which

might seem to imply that equivalent source model simulations apply only to the particular

scanner on which measurements were obtained. The generalizability of these equivalent

source models will be investigated in future studies that will focus on the range of

measurement values for scanners of the same make and model (small measurement

variations would result in similar equivalent source models and thus similar dosimetry

simulations), and variations in dose characteristics of scanners of different makes and

models. The latter of these studies will specifically involve performing equivalent source

model simulations to estimate organ doses from a wide range of commercially available

scanners for a number of different patient models in order to determine the optimal means

for calculating and reporting CT dose values.

57

Chapter 5 The Feasibility of Scanner-Independent CTDIvol-to-Organ Dose

Coefficients†

5.1 Introduction

An approach for estimating radiation dose from CT, based on CTDI to dose

conversion coefficients, was first suggested by Shrimpton54

. This approach was

predicated on his observation that the normalization of effective doses from the NRPB

Monte Carlo data sets by weighted CTDI (specifically CTDIw) accounted for scanner

differences that contributed to dose disparities among axial CT scanner models.54

According to Shrimpton, these results suggest the feasibility of scanner-independent

CTDIw-to-organ dose conversion coefficients for estimating doses from any axial scanner

in a standardized fashion. This would be similar to the use of region-specific k-factors

(effective dose per DLP) for estimating effective dose, as described in AAPM Report 963,

but would allow specific organ dose estimates to be obtained.

The development of Monte Carlo dosimetry packages with the capability of

calculating organ doses to detailed patient models from modern MDCT scanners


A. C. Turner, M. Zankl, J. J. DeMarco, C. H. Cagnon, D. Zhang, E. A. Angel, D. D. Cody, D. M.

Stevens, C. H. McCollough, and M. F. McNitt-Gray, ―The feasibility of a scanner-

independent technique to estimate organ dose from MDCT scans: Using CTDIvol to account

for differences between scanners,‖ Med. Phys. 37(4), 1816–1825 (2010).

58

introduced the possibility of investigating coefficients to convert CTDI values to organ

doses. However, as discussed in Chapter 4, the proprietary nature of scanner-specific x-

ray source information made it difficult to conduct a comprehensive study of organ dose

values for a number of different MDCT scanners in order to assess cross-scanner dose

variations. In order to overcome this limitation, the method to obtain scanner-specific

equivalent source models described in Chapter 4 was developed. As a result, it is possible

to obtain accurate organ dose values from 64-slice MDCT scanners from any

manufacturer.

The purpose of this study was to investigate the feasibility of a technique to

estimate organ doses that would be scanner-independent. This was accomplished by first

carrying out Monte Carlo dosimetry simulations of multiple 64-slice MDCT scanners on

a single patient model to acquire organ doses. Then, for each scanner, standard CTDIvol

values were measured and used as normalization factors for the simulated organ doses.

Finally, the variations across scanners of CTDIvol values, un-normalized organ doses, and

CTDIvol normalized organ doses were computed. The results will allow conclusions to be

drawn regarding the utility of using CTDIvol to account for scanner differences

influencing organ dose and ultimately assess the feasibility of generating scanner-

independent CTDIvol to organ dose conversion coefficients for MDCT scanners.

5.2 Methods

5.2.A. The CT Scanners

59

This study included 64-slice MDCT scanners from four major CT scanner

manufacturers: the LightSpeed VCT (General Electric Medical Systems, Waukesha, WI),

SOMATOM Sensation 64 (Siemens Medical Solutions, Inc, Forcheim, Germany),

Brilliance CT 64 (Philips Medical Systems, Cleveland, Ohio), and Aquilion 64 (Toshiba

Medical Systems, Inc., Otawara-shi, Japan). Each of these is a third generation MDCT

scanner that supports multiple nominal beam collimation settings as well as multiple

beam energies. All scanners are equipped with x-ray beam filtration that includes from

one to three available bowtie filters. For this work, all experiments were carried out with

a tube voltage of 120 kVp and the bowtie filter designed for the adult body. In order to

select comparable collimation widths, the widest available collimation setting for each

scanner was used for all experiments; it should be noted that the widest available

collimation typically has the largest dose efficiency (highest ratio of nominal total

collimated beam width to actual measured beam width). Therefore the selected nominal

collimation settings used were 40 mm (i.e. 64 x 0.625mm), for the LightSpeed VCT and

Brilliance CT 64, 32 mm (i.e. 64 x 0.5mm) for the Aquilion 64 and 28.8 mm (i.e.

24x1.2mm) for the Sensation 64 scanners, respectively. The organ dose simulations

described below were performed for helical scans with a pitch value of 1 (even if the

scanner cannot actually perform a scan of pitch 1). Each scanner was randomly assigned

an index number, either 1, 2, 3, or 4, and will be referred to by its assigned index from

this point on.

5.2.B. CTDI Measurements

60

Conventional CTDI measurements were performed to obtain CTDI100 and CTDIvol

values, for Scanners 1-4. All measurements were made with a standard 100 mm pencil

ionization chamber (ion chamber) and a calibrated electrometer. The CTDI100 values were

obtained at both the center and periphery (12 o‘clock) positions in a 32 cm diameter

(body) CTDI phantom using the scanner settings described in Section 5.2.A. Each

CTDI100 measurement was acquired using a sufficiently high mAs value (ranging from

200-300 mAs/rotation) and was reported on a per mAs value. Specifically, scanner-

specific CTDI100, denoted CTDI100,S, was obtained by measuring the exposure (E) from a

single axial scan and calculated (in mGy/mAs) using Equation 5.1:

Eq. 5.1

where f is the conversion factor from exposure to a dose in air (8.7 mGy/R), C is the

calibration factor for the electrometer, L is the active length of the ionization chamber

(100 mm), NT is the nominal collimation width (in mm), and β is the actual mAs/rotation

value used for the measurement. The corresponding CTDIvol,S, also in mGy/mAs,

pertaining to a helical scan with a pitch of 1 was then determined for Scanners 1-4 as

described by McNitt-Gray9.

5.2.C. Patient Model

For this work a single patient model was used for organ dose simulations based on

―Irene‖, a member of the GSF family of voxelized phantoms39,40

. The Irene data set

consists of a three-dimensional matrix (262 columns x 132 rows x 348 slices) of organ

61

identification numbers (e.g. organ codes) with voxel dimensions of 1.875 x 1.875 x 5.0

mm segmented from CT data of a patient with a height of 163 cm and a weight of 51

kg.40

Each voxel was assigned a specific elemental composition and density within

MCNPX based on its GSF organ code. An illustration of Irene is shown in Figure 5.1.

Figure 5.1 of Irene from the GSF Family of Voxelized Models. Note the individual

segmentation of radiosensitive organs.

Twenty distinct materials, including various anatomical tissues defined by the

ICRU Report 44 composition of body tissue tables55

, air, and graphite (for the patient

bed) were used in this work. For each material, the mass energy-absorption coefficients,

(μen/ρ)material, necessary for the dose calculation described in Chapter 3 and in Section

5.2.D.1. were generated for energies ranging from 1 keV to 120 keV. The (μen/ρ)material

values were each calculated as weighted averages of the elemental mass energy-

absorption coefficients, (μen/ρ)element, for each element comprising the material, using the

(μen/ρ)element values published by Hubbell and Seltzer45

and weights defined as the

62

material‘s elemental percent composition given by either the ICRU report 44 tables55

(for

anatomical tissue) or by Hubbell and Seltzer45

(for air).

5.2.D. Organ Dose Simulations

All simulations were performed using the UCLA MDCT Monte Carlo dosimetry

package described in Chapter 3. Simulations were performed to tally the dose to

segmented radiosensitive organs in the Irene patient model due to helical scans from the

four MDCT scanners described in Section 5.2.A. For all simulations performed in this

study, the number of photon histories was selected to ensure statistical simulation errors

less than 1% for all tallies.

5.2.D.1. Skeletal Tissue Doses

Red bone marrow (RBM) and bone surface (endosteal tissue) were not explicitly

segmented in the Irene model, but homogeneous bone voxels were identified. As a result,

it was not possible to directly obtain RBM and bone surface dose. Instead, the

homogeneous bone (HB) composition and density (1.4 g/cm3) of the adult ORNL

phantoms (Oak Ridge, TN: Oak Ridge National Laboratory)56

were used to describe all

voxels designated as bone or skeleton. The dose to bone surface was approximated as the

dose to the homogenous bone (DHB), which was calculated under the assumption of CPE

on a per photon basis as the product of the energy fluence, ψHB, in the skeleton voxel and

the (μen/ρ) value for HB (obtained using the weighted average method described in

Section 5.2.C with the ORNL elemental composition serving as the weights). A method

63

similar to that proposed by Rosenstein57

was used to calculate dose to RBM. This

approach estimates the deposited energy in RBM (ERBM) by assuming:

Eq. 5.2

where EHB is the energy deposited in HB, and mRBM and mHB are the total masses of

RBM and HB in the phantom. By dividing both sides by mRBM and noting that dose is the

deposited energy divided by mass it can be seen that:

Eq. 5.3

As previously discussed, DHB is calculated as the product of energy fluence in the

skeleton voxel (ψHB) and (μen/ρ)HB, so dose to RBM was calculated on a per photon basis

by:

Eq. 5.3

5.2.D.2. Organ Dose Simulation Experiments

For Scanners 1-4, Monte Carlo simulations were performed using the Irene patient

model and the equivalent source scanner models to obtain absorbed doses to the ICRP

Publication 103 radiosensitive organs5 from helical scans that utilized the scanning

protocol described in Section 5.2.A. For this feasibility study, the entire patient model

(from top of head to bottom of feet) was included in the scan range. The scan length was

64

determined by multiplying the longitudinal length of the voxels (5 mm) by the total

number of slices (348), resulting in a 174 cm scan. This created a condition where each

organ is completely encompassed in the scan region (and hence fully-irradiated).

Because the Irene model was constructed with arms at her side and because most

scans are performed with the patient‘s arms moved out of the field of view, all voxels

belonging to the arms were set to air, effectively removing the arms from the scan. This

results in a patient model condition that is obviously artificial, (especially when tallying

dose to bone, bone marrow, skin and muscle) but does allow the thorax, abdomen and

pelvic regions to undergo simulated scans without having the beam attenuated by arm

tissue before reaching organs in the scan region.

For each simulation 109 photon histories were performed to ensure statistical

simulation errors less than 1% for all organs. Dose was separately tallied in the 14 major

and 11 remainder organs; it should be noted that the lymphatic nodes and oral mucosa

(which are remainder organs) were not segmented in this GSF model.

The dose tally results from MCNPX were converted to absolute dose using the

scanner- and collimation-specific normalization factors described in Chapter 3. Then,

organ dose per mAs (where mAs refers to the value in the scan protocol which is actually

mAs/rotation) were obtained by multiplying each organ dose per total mAs by the total

number of rotations (given by the scan length divided by the scanner-specific nominal

collimation width). In addition, the effective dose was calculated in mSv/mAs using the

ICRP Publication 103 definition5 in order to explore the variation of effective dose for a

65

single patient model across scanners and investigate their normalization with measured

CTDIvol values.

5.2.E. Analysis of Organ Dose Values

5.2.E.1. Absorbed Organ and Effective Doses

The Monte Carlo simulations resulted in unique absorbed dose values (in

mGy/mAs) for each scanner and organ combination as well as effective doses (in

mSv/mAs) for each scanner. These scanner-specific organ and effective dose values will

be referred to as and . For each organ, the mean absorbed dose across the four

scanners, (where

), was calculated along with the standard

deviation. Similarly, the mean effective dose across scanners, (where

) and the standard deviation were also computed. Finally, the coefficient of

variation (CoV = standard deviation/mean) of the values for each organ as well as the

values across scanners were calculated and expressed as a percentage.

5.2.E.2. Exploring the Relationship between CTDI and Organ (and Effective) Doses

Because CTDIvol and organ dose values appeared to vary in a similar fashion

across scanners, the feasibility of reducing inter-scanner variability by normalizing organ

doses by CTDIvol was explored. If successful, this would suggest the feasibility of an

approach to estimating organ dose values across different scanners for a given patient

based primarily on CTDIvol values.

66

To do this, each of the simulated organ dose values, , were normalized by the

measured value of the scanner being simulated. This resulted in a unitless

quantity for each scanner and organ combination, referred to as (where

). Then, for each organ, the mean was calculated across scanners

and denoted as (where

). Similarly, the normalized effective

dose values for each scanner, (where , and the mean

across scanners, (where

were obtained. Finally, the

CoVs of the values for each organ as well as the values across scanners

were calculated and expressed as a percentage.

5.3 Results

The CTDI measurements obtained with the 32 cm (body) CTDI phantom using a

tube voltage of 120 kVp and the widest possible scanner collimation for each scanner are

reported in Table 5.1 on a per mAs basis. For Scanners 1-4 the scanner-specific center

and periphery measurements are shown in the first two columns and the

values for pitch 1 are displayed in the last column. This table shows that there

is considerable variation between scanners in terms of ; Scanner 4 has a

value that is nearly twice that of scanners 1 and 2 and Scanner 3 is nearly 50%

higher than scanners 1 and 2. This table also shows the mean, standard deviation and

Coefficient of Variation (CoV expressed as a percentage) for each CTDI value across

67

scanners. Specifically for , the mean, standard deviation and CoV are 0.084

mGy/mAs, 0.029 mGy/mAs, and 34.1%, respectively.

Table 5.1 – CTDI measurements for Scanners 1-4. All values in mGy/mAs.

CTDI100,S

Scanner Center Periphery CTDIvol,S

1 0.040 0.074 0.063

2 0.037 0.075 0.062

3 0.051 0.107 0.089

4 0.069 0.150 0.123

Mean 0.049 0.102 0.084

Standard deviation 0.014 0.036 0.029

CoV (%) 28.8% 35.4% 34.1%

The organ doses, (in mGy/mAs) and effective doses, (in mSv/mAs)

described in Section 5.2.D.2 for Scanners 1-4 are plotted in Figure 5.2 and displayed in

Table 5.2 (the table explicitly lists doses for all ICRP Publication 103 radiosensitive

organs while the plot displays doses for the 14 major organs and the average dose of the

11 remainder organs). It can be seen from Figure 1 that, for most organs, there is a

considerable difference in dose values between some of the different scanners. For

example, the dose to most organs from Scanner 4 is approximately twice that of Scanner

2. With the exception of Scanners 1 and 2, this relatively large variation appears fairly

consistent for other pairwise scanner comparisons across most organs. Table 5.2

quantifies this variation by reporting the mean organ doses ( and , the standard

deviation, the and CoV across scanners. The minimum variation was approximately

26.7% (for the adrenals) and the maximum was approximately 37.7% (for the thyroid),

68

with a mean CoV of about 31.6%. In addition, the table shows that the mean effective

dose across scanners is 0.15 mSv/mAs with a CoV of 31.5%.

Figure 5.2 Organ dose (DS,O), in mGy, and effective dose (DS,ED), in mSv, for a 100 mAs/rot

scan for scanners 1–4.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Org

an

dose

(m

Gy/m

As)

an

d e

ffec

tiv

e d

ose

(m

Sv

/mA

s)

Scanner 1 Scanner 2 Scanner 3 Scanner 4

69

Table 5.2. Organ dose ( ) in mGy/mAs and effective dose ( ), in mSv/mAs for

Scanners 1-4. Columns 6-8 display the mean, standard deviation, and coefficient of

variation (CoV) across scanners. The bottom three rows display the mean, maximum, and

minimum of the CoV across organs.

Scanners

Organ 1 2 3 4 Mean Dose Standard

Deviation CoV

Red Bone Marrow 0.09 0.08 0.11 0.15 0.11 0.03 28.5%

Colon 0.12 0.11 0.16 0.22 0.16 0.05 32.0%

Lungs 0.12 0.11 0.15 0.21 0.15 0.05 31.0%

Stomach 0.13 0.11 0.16 0.22 0.16 0.05 31.2%

Breast (glandular) 0.10 0.10 0.14 0.19 0.13 0.04 32.0%

Ovaries 0.09 0.09 0.12 0.17 0.12 0.04 31.6%

Bladder 0.13 0.11 0.16 0.24 0.16 0.06 34.5%

Esophagus 0.12 0.11 0.15 0.22 0.15 0.05 31.4%

Liver 0.12 0.11 0.16 0.21 0.15 0.05 30.8%

Thyroid 0.17 0.15 0.22 0.34 0.22 0.08 37.7%

Bone Surface 0.24 0.22 0.33 0.45 0.31 0.11 34.2%

Brain 0.12 0.11 0.15 0.21 0.15 0.05 30.8%

Salivary Glands 0.17 0.15 0.22 0.32 0.21 0.08 35.1%

Skin 0.11 0.10 0.15 0.21 0.14 0.05 34.8%

Adrenals 0.11 0.10 0.14 0.19 0.14 0.04 26.7%

Extrathoracic region 0.13 0.12 0.17 0.24 0.16 0.05 31.8%

Gall Bladder 0.13 0.12 0.17 0.24 0.16 0.05 31.8%

Heart 0.14 0.12 0.17 0.24 0.17 0.05 32.2%

Kidney 0.11 0.11 0.15 0.20 0.14 0.04 29.2%

Muscle 0.11 0.10 0.15 0.20 0.14 0.04 31.8%

Pancreas 0.11 0.10 0.14 0.19 0.13 0.04 28.6%

Small Intestine 0.12 0.11 0.15 0.22 0.15 0.05 32.1%

Spleen 0.12 0.11 0.15 0.21 0.15 0.04 30.5%

Thymus 0.14 0.12 0.18 0.26 0.17 0.06 34.3%

Uterus 0.11 0.10 0.14 0.19 0.14 0.04 30.2%

Effective Dose 0.12 0.11 0.15 0.21 0.15 0.05 31.5%

Mean CoV: 31.6%

Max. CoV: 37.7%

Min. CoV: 26.7%

70

The CTDIvol normalized organ ( ) and effective doses ( ) for Scanners

1-4 ( and normalized by as described in Section 5.2.E.2) are plotted

in Figure 5.3 and displayed in Table 5.3 (the table explicitly lists values for all

ICRP Publication 103 radiosensitive organs while the plot displays values for the

14 major organs and the average value of the 11 remainder organs). Unlike the results in

previous sections, Table 5.3 and Figure 5.3 shows very little difference in CTDIvol

normalized dose values between different scanners. For example, the CTDIvol normalized

dose to most organs from Scanner 4 is within 10-15% of those of all other scanners.

Table 5.3 quantifies this reduced variation by reporting the mean CTDIvol normalized

organ ( ) or effective doses ( ), the standard deviation, and the coefficient of

variation across scanners. The bottom three rows of Table 5.3 display the mean,

maximum, and minimum coefficient of variation across all organs of the CTDIvol

normalized dose values. The mean variation was approximately 5.2%, with a minimum

of approximately 2.4% (for skin tissue) and a maximum of approximately 8.5% (for the

adrenals).

71

Figure 5.3 CTDIvol, S normalized organ (nDS,O), and effective (nDS,ED) doses for scanners 1–4.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Org

an

dose

s an

d e

ffec

tiv

e d

ose

no

rma

lize

d b

y m

easu

red

CT

DI v

ol

Scanner 1 Scanner 2 Scanner 3 Scanner 4

72

Table 5.3: CTDIvol normalized organ ( ) and effective ( ) dose values for Scanners

1-4. Columns 6-8 display the mean, standard deviation, and coefficient of variation (CoV)

across scanners. The bottom three rows display the mean, maximum, and minimum of the

CoV across organs.

Scanners

Organ 1 2 3 4 Mean Dose Standard

Deviation CoV

Red Bone Marrow 1.43 1.33 1.29 1.24 1.32 0.08 6.2%

Colon 1.97 1.80 1.82 1.81 1.85 0.08 4.3%

Lungs 1.88 1.75 1.75 1.70 1.77 0.08 4.3%

Stomach 2.01 1.82 1.81 1.81 1.86 0.10 5.4%

Breast (glandular) 1.63 1.58 1.62 1.54 1.59 0.04 2.5%

Ovaries 1.48 1.40 1.41 1.37 1.42 0.05 7.4%

Bladder 2.04 1.81 1.82 1.92 1.90 0.11 5.7%

Esophagus 1.99 1.77 1.73 1.77 1.82 0.12 6.6%

Liver 1.92 1.79 1.78 1.74 1.81 0.08 4.4%

Thyroid 2.75 2.45 2.50 2.75 2.61 0.16 6.2%

Bone Surface 3.81 3.57 3.68 3.69 3.69 0.10 2.7%

Brain 1.96 1.76 1.75 1.74 1.80 0.11 5.9%

Salivary Glands 2.71 2.42 2.45 2.59 2.54 0.13 5.3%

Skin 1.72 1.62 1.67 1.69 1.68 0.04 2.4%

Adrenals 1.83 1.65 1.59 1.50 1.64 0.14 8.5%

Extrathoracic region 2.09 1.92 1.93 1.91 1.96 0.09 4.3%

Gall Bladder 2.13 1.91 1.89 1.92 1.96 0.11 5.8%

Heart 2.20 1.96 1.94 1.99 2.02 0.12 5.9%

Kidney 1.83 1.70 1.68 1.61 1.71 0.09 5.5%

Muscle 1.74 1.63 1.64 1.61 1.65 0.06 3.4%

Pancreas 1.78 1.59 1.54 1.51 1.60 0.12 7.8%

Small Intestine 1.93 1.73 1.74 1.75 1.79 0.09 5.3%

Spleen 1.84 1.73 1.73 1.67 1.74 0.07 4.1%

Thymus 2.25 1.97 2.00 2.10 2.08 0.13 6.0%

Uterus 1.78 1.60 1.55 1.56 1.62 0.11 6.7%

Effective Dose 1.88 1.73 1.73 1.71 1.76 0.08 4.6%

Mean CoV: 5.2%

Max. CoV: 8.5%

Min. CoV: 2.4%

73

A quantitative comparison of the last column of Table 5.3 with that of Table 5.2

indicates that for all organs the variations of the CTDIvol normalized dose values across

scanners are much smaller than those of the un-normalized doses. Specifically, it can be

seen that for all organs the CoV values across scanners of the CTDIvol normalized doses

are less than those of the un-normalized values. Comparison of the summary statistics in

the bottom three rows of Tables 5.2 and 5.3 further illustrates that organ doses

normalized by CTDIvol have a smaller variation across scanners than do un-normalized

dose values. Furthermore, the relatively small variance of the CTDIvol normalized doses

(the maximum CoV was 8.5%) indicates that, for any organ, the mean value ( ) is a

good approximation of the value for any individual 64-slice MDCT scanner ( ).

Therefore, since the product of the generic and a particular scanner‘s measured

CTDIvol will result in a scanner-specific dose, these findings demonstrate the feasibility

of a scanner-independent technique to estimate organ dose based on standard CTDIvol to

dose conversion coefficients.

5.4 Discussion

The purpose of this study was to investigate the feasibility of a method to estimate

organ doses that is scanner-independent by assessing the ability of CTDIvol measurements

to account for differences in MDCT scanners that lead to organ dose differences. In the

first set of results, Table 5.1 showed large variations in CTDIvol between scanners, with a

CoV of 34.1%. In the simulation experiments, the analysis of the un-normalized organ

and effective doses ( and ) from Scanners 1-4 demonstrated differences across

74

scanners that were very similar to those observed in the CTDIvol values. The results in

Table 5.2 and the plot in Figure 5.2 definitively illustrate this variation. Scanner 4

delivered the highest doses, by a relatively large margin, for all the radiosensitive organs

used in this study. Scanner 3‘s dose values were typically 65-75% of Scanner 4‘s while

Scanners 1 and 2, which actually resulted in similar doses, were on the order of 45-60%

of Scanner 4‘s doses. Overall, the CoV across scanners for a given organ ranged between

26.7% (for the adrenals) to 37.7% (for the thyroid), with a mean 31.6% across all organs.

It should be emphasized that both CTDIvol and absolute organ doses were reported

on a per mAs basis. As a result, dose differences can attributed to differences in filtration

designs including bowtie filter thickness, composition, and shape (which results in

differences in x-ray output characteristics). Furthermore, calculating organ doses on a per

mAs basis did not allow organ dose comparisons to be made for exams with equivalent

image quality. The actual mAs values necessary to achieve comparable image quality

will almost certainly vary depending on the scanner. Instead, this work was carried out in

order to consider the feasibility of normalizing out organ dose differences on a per mAs

basis between scanners via CTDIvol measurements.

Because both CTDIvol and organ doses exhibited similar cross-scanner variations,

the normalization of the organ and effective doses by CTDIvol,S measurements were

investigated. The resulting normalized values, and , were presented in

Figure 5.3 and Table 5.3. The and values had much less variation across

scanners relative to the un-normalized dose values. This point is emphasized by the

75

noticeable convergence of points in Figure 5.3, compared to the spread of the points in

Figure 1 and is indeed consistent with the observations of Shrimpton in his comparisons

of effective dose normalized by CTDIw using older scanners54

. The CoV across scanners

for a given organ ranged from 2.4% (for skin tissue) to 8.5% (for the adrenals), with a

mean across all organs of 5.2%. This is a drastic reduction compared to the mean CoV of

31.6% seen for the un-normalized doses. These results indicate that the characteristics of

a scanner that influence organ dose, such as filtration designs, influence CTDIvol values in

a similar fashion and that that normalizing by CTDIvol effectively accounts for these

differences across scanners. Specifically, for any organ, the CTDIvol normalized dose for

a particular 64-slice MDCT scanner will be within approximately 10% of the mean value

across all 64-slice MDCT scanners (i.e. ).

The relatively small variance of the organ dose normalized by CTDIvol values

suggests that, for a given patient, anatomical scan region, and scan protocol (i.e. tube

voltage and bowtie size), it is feasible to estimate organ doses from any 64-slice scanner

based on a single set of scanner-independent CTDIvol to dose conversion coefficients.

Quantitatively, the CoV of 5.2% indicates that multiplying the or values by

the scanner-specific CTDIvol value (in mGy/mAs) and the relevant mAs used clinically, it

is possible, on average, to estimate absolute organ or effective doses to within

approximately 10% accuracy for any 64-slice MDCT scanner.

This study was meant to demonstrate that scanner-specific dependencies are

accounted for when CTDIvol measurements are used as normalization factors for organ

76

doses. The results suggest the feasibility that scanner-independent organ dose conversion

coefficients can be generated for patient- and protocol-specific scans. In this work organs

were fully-irradiated with no extra attenuation from arm tissue in order to mimic the

primary x-ray fluence conditions of a typical CT exam (i.e. moving the arms up for a

chest or abdomen scan). Head to toe scans were performed to ensure the conclusions of

this work applied to all radiosensitive organs in the body.

The results show that for all organs the values have little variation across

scanners; however, it should be emphasized that the values reported in this study are

not intended to serve as actual CTDIvol to organ dose coefficients. The fact that the arms

were removed indicates that the results are not applicable for a true full-body exam where

the values would be larger for tissues found in the arms, such as skin, muscle, RBM,

and bone surface, and smaller for organs that would receive less radiation due to arm

attenuation. Furthermore, the reported values may not be appropriate even for fully-

irradiated organs in partial-body exams (i.e. stomach in an abdomen scan) as scatter from

distant anatomy that would not be irradiated for a partial-body scan is included in these

simulations. Finally, the results of this work are limited to the particular patient model

and scan protocol (tube voltage, bowtie filter, collimation, and pitch) used in the

simulations. These limitations will all be addressed in future studies to extend the

CTDIvol to organ dose estimation method proposed here.

Another limitation is that the patient model used in this study, Irene of the GSF

family of models, did not include separately segmented RBM and bone surface

77

(endosteal layer) anatomy, so the dose to these skeletal tissues could not be directly

simulated. The two-term mass-energy absorption coefficient method used to approximate

skeletal doses, described in Section 5.2.D.1, was evaluated by Lee, et al. and found to

overestimate RBM dose at the energies used in this study.58

Therefore, CTDIvol to dose

conversion coefficients for skeletal tissue will be investigated in future studies using

voxelized phantoms with explicitly segmented cortical bone and spongiosa regions59

along with bone- and bone region-specific photon fluence-to-dose response functions60

.

Future Monte Carlo studies will be conducted using multiple computational

anthropomorphic phantoms representing a range of different patients in order to examine

the effect of size, body habitus, and gender on CTDIvol to dose conversion coefficients

and develop methods to account for these effects. Partial-body scans will be performed

using several different sized patient models. This will help identify potential

complications for generating CTDIvol to dose conversion coefficients, including the

effects on organs not fully encompassed in the scan region (i.e. those that are partially-

irradiated such as the lower intestine in an abdominal scan). Additionally, methods to

take into account variations in the scanning protocol, such as different pitch, bowtie filter

sizes, and collimation settings, will be investigated. Finally, since the majority of current

clinical exams use tube current modulation (TCM) schemes in order to reduce dose

levels, the effect of TCM will be explored in a manner similar to that reported by Angel,

et al.61,62

in order to devise an approach to account for the resulting organ dose reduction.

If these issues can be resolved, it should be feasible to produce a truly universal set of

78

patient- and scanner-independent CTDIvol to organ dose conversion coefficients for a

range of scan protocols that can be implemented to quickly and accurately estimate

patient dose from any CT exam. In addition, there have been discussions concerning the

revision of standardized CT dosimetry measurements, especially for exams performed

with wider beams (40 - 180 mm)12

. When developed, these revised index values will be

investigated as organ dose normalization factors for scanners and exams that CTDI may

not adequately characterize.

While the focus of this work is on assessing radiation dose from CT, it should be

pointed out that CT scans are a very important tool for diagnosis and assessment of

response to treatment in the practice of medicine. Technical developments have led to an

expanding list of applications that have supplanted less accurate or more invasive

diagnostic tests59

(such as exploratory surgery) which in turn has led to a dramatic

increase in the use of body CT8. The detailed assessment of anatomy and function that

CT imaging provides does require the use of x-rays, which do result in some small, but

not zero, risk to patients. In the vast majority of cases, the benefits do significantly

outweigh the risks in having a CT exam performed.

The pertinent conclusions from this work are that: (a) there is considerable

variation amongst modern MDCT scanners when considering both organ and effective

dose (on the order of ~200% in some cases), and (b) this variation can be mostly

accounted for by using scanner-specific CTDIvol measurements as a normalization factor.

The first of these conclusions implies the difficulty of applying absolute dose values from

79

Monte Carlo studies performed for a particular scanner model to other scanners.

However, the second conclusion suggests that by normalizing organ doses by measured

CTDIvol values, the characteristics that differentiate the simulated scanner from other

scanners can be accounted for, producing a normalized organ dose that can be applied to

a range of MDCT scanners. Future MDCT organ dose studies should utilize this finding

by reporting organ and effective doses on a per measured CTDIvol basis. This work

represents the first step in establishing a universal organ dose framework for MDCT

scanners which utilizes CTDIvol to account for the scanner-specific dependencies of organ

and effective dose. The future studies discussed above will expand this framework to

include the effects of patient size, pitch, and scan region considerations with the ultimate

goal of estimating organ dose to any patient from any scanner through the use of

universal CTDIvol to dose conversion coefficients.

80

Chapter 6 Size Dependence of CTDIvol-to-Organ Dose Coefficients†

6.1 Introduction

The work presented in Chapter 5 demonstrated the feasibility of using CTDIvol

values to account for differences among 64-slice multidetector CT (MDCT) scanners

from various manufacturers when estimating organ doses in patients. It was shown that

CTDIvol values vary across scanners in a similar fashion as organ doses obtained from

scanner-specific Monte Carlo simulations. As a result, when organ doses from each

scanner were normalized by the corresponding CTDIvol, the variation across scanners

reduced from 31.5% (without normalization) to 5.2% (after normalization with CTDIvol),

on average across all radiosensitive organs. This confirmed the feasibility of generating

scanner-independent CTDIvol-to-organ dose conversion coefficients for each organ that

could be used to estimate organ doses from a full-body scan for any scanner to within

approximately 10% of the dose values obtained through detailed Monte Carlo

simulations.


A.C. Turner, D. Zhang, M. Khatonabadi, M. Zankl, J.J. DeMarco, C.H. Cagnon, D.D. Cody,

D.M. Stevens, C.H. McCollough, and M.F. McNitt-Gray, ―The feasibility of patient size-

corrected, scanner-independent organ dose estimates for abdominal CT exams,‖ Med. Phys.

38(2), 820-829 (2011).

81

That study was performed by simulating 120 kVp full-body (head to toe) helical

exams using a single patient model, namely an adult female (Irene) from the GSF family

of voxelized phantoms40

. As a result, the reported CTDIvol-to-organ dose conversion

coefficients were valid only for that specific scan protocol and patient model. Several

investigators have demonstrated that patient size has a significant effect on the absorbed

dose and, even more specifically, on organ dose, for a specific scanner output (e.g.

CTDIvol) 63-67

. These reports have all shown that, for the same exposure conditions (i.e.

same technical parameter settings), organs in smaller patients (including pediatric

patients) receive higher radiation doses than those in larger patients.

Therefore, the purpose of this study was to extend the work presented in Chapter

5 by determining the effects of patient size on CTDIvol-to-organ dose conversion

coefficients. Specifically, this study focused on abdominal scans using a cohort of eight

voxelized patient models that represented a range of sizes from infant to large adult that

included males and females. Since an abdominal exam does not cover the entire body,

some organs will be fully included in the scan region (fully-irradiated, such as kidney and

liver), some will be only partly located within the scan region (partially-irradiated, such

as colon), and some will be fully outside the scan region (non-irradiated, such as thyroid).

The primary focus of this investigation is on the radiation dose to those organs that are

fully-irradiated during the abdominal scan. The radiation dose to partially- and non-

irradiated organs is also considered, but is not the primary focus.

6.2 Methods

82

6.2.A. Patient Models

The patient models used to obtain organ dose values for this work were the GSF

family of voxelized phantoms39,40

. These voxel-based models were created from high

resolution CT or magnetic resonance images using automated, semi-automated, and

manual segmentation techniques. Each patient model is comprised of a three-dimensional

matrix of numbers, each of which corresponds to a different organ or non-anatomic

material (such as air or the patient bed). For this study, eight different models were used

(shown in Figure 1) that included two pediatric models (Baby and Child), three adult

males (Golem, Frank, and Visible Human), and three adult females (Irene, Donna, and

Helga). Illustrations of the GSF Family of Voxelized Phantoms are shown in Figure 6.1

and additional information about each model is provided in Table 6.1. While some

members of the GSF family are not whole body models, each model included the full

abdominal region along with a similar set of contoured abdominal organs and thus was

appropriate for simulations of typical abdominal CT exams.

83

Table 6.1 Information about the GSF Family of Voxelized Models as described in Petoussi-

Henss, Zankl, et al.39


Name Gender Age Phantom Type Weight

(kg)

Height

(cm)

Scan Length

(cm)b

Perimeter

(cm)c

Baby Female 8 weeks Whole body 4.2 57 15.2 36.3

Child Female 7 years Whole body 21.7 115 24.8 59.7

Golem Male 38 years Whole body 68.9 176 31.2 87.4

Frank Male 48 years Torso and head (65.4)a (96.5)

a 26.0 124.5

Visible Human Male 38 years From knees

upwards

103.2

(87.8)a

180

(125)a

33.0 102.9

Irene Female 32 years Whole body 51 163 25.5 66.5

Donna Female 40 years Whole body 79 170 29.0 95.0

Helga Female 26 years From mid thigh

upwards

81

(76.8)a

170

(114)a

33.0 106.2

a Data in parentheses refers to the weight or height of the voxelized phantom; data not in

parenthesis refers to the weight or height of the actual patient whose images were used to

generate the model. b

Refers to abdominal scan length defined as ~1 cm superior to the top of the

diaphragm to ~1 cm inferior to the illiosacral joint. c Refers to perimeter of the phantom taken

from the central slice of the scan region

Figure 6.1 Fig. 1. Illustrations of the GSF Family of Voxelized Phantoms as described in

Petoussi-Henss, Zankl, et al.39


. Additional information in Table 6.1.

84

It has been suggested that organ dose values can be characterized using patient

perimeter as a metric for patient size61,62

. Therefore, the perimeter of the central slice of

the scan region for each patient was determined (in cm) and is included in Table 6.1.

Perimeter values were obtained using a graphics software package that featured a semi-

automated segmentation tool. A contour was placed around the outside of the patient and

its length was recorded.

Each patient model provided by the GSF was converted into a standardized data

format for use with the Monte Carlo simulation package described below. Twenty

distinct materials, including various anatomical tissues whose composition and density

were defined by ICRU Report 4455

, air, and graphite (for the patient bed) were used in

this work. For each material, the mass energy-absorption coefficient (μen/ρ) were

generated based on the values reported by Hubbell and Seltzer45

for energies ranging

from 1 keV to 120 keV using the method described in Chapter 3.

The GSF patient models were originally constructed with their arms down at their

sides. In the majority of abdominal CT exams, the patient‘s arms are positioned up and

out of the scan region. Because this study focuses on abdominal scans, it is desirable to

avoid extra beam attenuation due to arm tissue that typically would not be present in an

actual exam. Since it was not possible to alter the placement of the GSF arm tissue, all

voxels belonging to the arms were set to air, effectively removing the arms from the scan

region. This was done for all patient models except for the Baby model, since it is

common to allow an infant‘s arms to remain down in actual exams.

85

6.2.B. The CT Scanners and Exam Protocols

This study included a third generation, 64-slice MDCT scanner from each of the

four major CT scanner manufacturers: the LightSpeed VCT (GE Healthcare, Waukesha,

WI), Brilliance CT 64 (Philips Medical Systems, Cleveland, Ohio), SOMATOM

Sensation 64 (Siemens Medical Solutions, Forcheim, Germany), and Aquilion 64

(Toshiba Medical Systems, Inc., Otawara-shi, Japan). All scanners are equipped with x-

ray beam filtration that includes from one to three available bowtie filters.

In order to ensure that the dosimetry simulations performed for this study were as

comparable as possible across scanner models, all simulations were carried out using a

tube voltage of 120 kVp, the bowtie filter designed for the adult body, and the widest

available collimation setting for each scanner. Consequently, the selected bowtie filter

was kept constant for each scanner, even when smaller sized patient models (including

pediatric) were being simulated. While it is recognized that this may not be how some

scanners would be used in a clinical setting, keeping the bowtie filter selection constant

across patient models allowed the effect of patient size to be isolated under constant

source conditions. The selected nominal beam width and detector configuration settings

were 40 mm (i.e. 64 x 0.625 mm) for the LightSpeed VCT, 40 mm (i.e. 64 x 0.625 mm)

for the Brilliance CT 64, 28.8 mm (i.e. 24 x 1.2 mm) for the Sensation 64 scanners, and

32 mm (i.e. 64 x 0.5 mm) for the Aquilion 64. The simulation package described below

models the actual longitudinal beam width of each scanner (defined as the FWHM of the

longitudinal dose profile measured with Optically Stimulated Luminescence strips) which

86

are 42.4 mm for the LightSpeed VCT, 43.7 mm for the Brilliance CT 64, 32.2 mm for the

Sensation 64, and 36.9 mm for the Aquilion 64. All organ dose simulations were

performed as helical scans with a pitch value of 1, even if the scanner cannot actually

perform a scan of pitch 1. Each scanner was randomly assigned an index number, either

1, 2, 3, or 4, and will be referred to by its assigned index from this point on.

6.2.C. Physically Measured CTDI Values

Conventional techniques were performed to measure exposure and calculate

CTDIvol values for Scanners 1-49. All exposure measurements were acquired with a

standard 100 mm pencil ionization chamber and a calibrated electrometer using a 1

second rotation time and a sufficiently high mAs value (ranging from 200-300

mAs/rotation) to ensure reproducible measurements. For this work, CTDIvol values were

obtained using a 32 cm diameter (body) CTDI phantom using the tube potential, beam

collimation, and bowtie filter settings described in Section 6.2.B. The resulting CTDIvol

values were recorded on a per mAs/rotation basis (denoted mGy/mAs).


6.2.D.1. Overview of Monte Carlo Simulation Techniques

All organ doses were obtained using the MDCT simulation package described in

Chapter 3. Simulations were performed to tally the dose to segmented radiosensitive

organs segmented in each of the GSF patient models described in Table 6.1. For each

patient, doses from helical scans from the four MDCT scanners described in Section

87

6.2.B. were obtained. For all simulations performed in this study, the number of photon

histories was selected to ensure statistical simulation errors less than 1% for all tallies.

6.2.D.2. Abdominal Exam Simulations

For Scanners 1-4, Monte Carlo simulations of helical exams that utilized the

scanning protocol described in Section 6.2.B were performed using each of the GSF

patient models described in Section 6.2.A. For each patient model, the abdominal scan

region was defined as approximately 1 cm superior to the top of the diaphragm to

approximately 1 cm inferior to the illiosacral joint. It should be noted that this is the

region over which the x-ray source is turned on, not just the usual extent of image data

and, therefore, is meant to include the effect of overscan that typically occurs for these

MDCT scanners on organ doses33

. The resulting scan length for each patient model is

reported in Table 6.1. Using the simulation process outlined in Section 6.2.D.1, doses (in

mGy/total mAs) were tallied for each of the ICRP Publication 1035 radiosensitive organs

included in each patient model. Finally, in order to account for the differences in total

mAs across scanners due to the variation in the number of rotations necessary to traverse

the scan length, organ dose values were converted into units of mGy/mAs (where mAs

refers to mAs/rotation) by multiplying each mGy/total mAs value by the total number of

rotations used in the corresponding helical scan simulation (from mGy/total mAs to

mGy/mAs).

6.2.E. Data Analysis

88

6.2.E.1. CTDIvol Normalized Organ Doses

Each simulated abdominal helical scan resulted in a unique organ dose value for

each patient and scanner combination. Adopting the convention introduced in Chapter 5,

these organ dose values will be denoted as , where P refers to the patient model, S

to scanner, and O to organ. Each dose value, , was normalized by the measured

CTDIvol value (also in mGy/mAs) corresponding to the simulated scanner, resulting in a

unitless value, denoted . It should be emphasized that organ doses for all patient

models, including pediatric patients, were normalized by the CTDIvol measured with the

32 cm diameter (body) phantom in order to hold all study parameters constant except for

patient model. For each patient and organ combination, the average was

calculated across scanners and denoted (where

).

6.2.E.2. Organ Coverage Analysis

In a scan of the abdomen, there are several ICRP Publication 1035 radiosensitive

organs that are expected to be completely contained within the anatomically defined scan

region (e.g. stomach, liver, kidney), while others may only be partially encompassed by

the scan (e.g. colon, lung, breast), and still others that are entirely outside of the scan

region‘s boundaries (e.g. testis, brain, thyroid). The majority of dose to anatomy located

within the scan region is due to direct radiation from the CT source and, conversely, any

dose to anatomy outside the scan region can be attributed to scattered radiation.

89

For each patient model, the fraction of each organ‘s volume that was included in

the scan region was calculated (denoted percent coverage). Based on the value of its

percent coverage, each organ was classified as either ―fully-irradiated‖ (i.e. percent

coverage of 100% for all patient models), ―partially-irradiated‖ (percent coverage greater

than 0% in at least one patient model and less than 100% in at least one patient model), or

―non-irradiated‖ (percent coverage of 0% for all patient models; these organs are

expected to receive only scattered radiation as they are outside the scan region). Not all

radiosensitive organs were found in each patient as some organs are gender-specific and

others were not explicitly contoured in one or more models.

Seven organs were fully-irradiated in all of the patients, including the liver,

stomach, adrenals, kidney, pancreas, spleen, and gall bladder. Thirteen organs were

identified as partially-irradiated including the colon, small intestine, heart, ovaries,

uterus, lung, esophagus, glandular breast tissue, skin, muscle tissue, red bone marrow,

bone surface (endosteal tissue), and bladder. Finally, seven organs were identified as

being completely absent from the scan region for all patient models, including the testis,

thyroid, brain, salivary glands, extrathoracic region, prostate, and thymus.

6.2.E.3. Fully-Irradiated Organ Analysis

First, in order to demonstrate the validity of scanner-independent CTDIvol-to-

organ dose conversion coefficients (as reported in Chapter 5 for one patient model) for all

the patient models used in this study, the Coefficients of Variation (CoV) across scanners

90

of the values were determined for fully-irradiated organs in all eight of the patient

models. The CoV across scanners was calculated as the standard deviation of

values divided by the mean (i.e. )

Then, for each fully-irradiated organ, the relationship between values and

patient size was investigated. For each fully-irradiated organ, was plotted as a

function of patient perimeter to determine if a correlation exists. Based on the plots,

exponential regression equations were obtained in the form of:

Eq. 6.1

where unique and values (denoted size coefficients) exist for each organ. The

correlation coefficient (R2) of the exponential fit was also obtained for each organ.

6.2.E.4. Partially-Irradiated Organ Analysis

The GSF models described above were generated from actual patient models and

thus reflect realistic variations in the placement, shape, and size of organs. As a result, the

fraction of each partially-irradiated organ‘s volume located within the abdominal scan

region (denoted percent coverage) is expected to differ across all patient models. In order

to quantify this variation, the average and standard deviation of percent coverage was

determined for each partially-irradiated organ across patients.

In order to determine if a size dependency exists between values and

patient size for partially-irradiated organs, a similar regression analysis as described in

91

Section 6.2.D.3 was performed. Correlation coefficients (R2) were determined for each

organ to assess the association between and patient perimeter.

6.2.E.5. Non-Irradiated Organ Analysis

Organs that were not directly exposed to primary x-ray radiation received the

majority of their dose from scattered x-rays, and, therefore, were expected to have very

low associated dose values relative to directly exposed organs. In order to perform a

quantitative comparison, the ratio of each non-irradiated organ‘s value to the mean

across the fully-irradiated organs was calculated and expressed as a percentage.

6.3 Results

6.3.A. Fully-Irradiated Organ Results

The CoV across scanners of values, expressed as a percentage, were less

than 10% for all fully-irradiated organs in all patients. Specifically, the CoV values

ranged from 3.2% to 9.8% across all patients and organ combinations. These results

verify that, for all fully-irradiated organs, the mean CTDIvol normalized dose across

scanners is a sufficient approximation of the value specific to any particular scanner (i.e.:

, for any S). This analysis agrees with the results presented in Chapter 5

which was performed for a single patient model. This demonstrates that values can

serve as scanner-independent CTDIvol-to-organ dose conversion coefficients for each

fully-irradiated organ for all the patient models used in this study.

92

The values for fully-irradiated organs are presented in Table 6.2 and a plot

of values as a function of patient perimeter is shown in Figure 6.2. This plot

indicates a decreasing exponential relationship (the exponential regression line and

equation for stomach is displayed as an example). Exponential regression equations, as

described by Equation 6.1, were obtained for each fully-irradiated organ. The size

coefficients (AO and BO), along with the correlation coefficient of the exponential

regression analysis (R2), are displayed in Table 6.3. The correlation coefficients are all ≥

0.95, indicating that perimeter is an excellent predictor of values for fully-

irradiated organs.

Table 6.2 Mean CTDIvol normalized organ doses across scanners for each patient model for

fully-irradiated organs. Note that the gall bladder was not included in the Child patient

model.

Baby Child Golem Frank Visible

Human Irene Donna Helga

Stomach 2.57 2.05 1.41 0.99 1.11 1.64 1.33 1.10

Liver 2.53 1.98 1.33 0.95 1.07 1.66 1.15 1.03

Adrenals 2.77 1.88 1.40 0.91 0.97 1.50 1.25 1.00

Gall Bladder 2.66 - 1.50 1.01 1.34 1.90 1.19 1.08

Kidney 2.62 1.91 1.41 0.88 1.09 1.62 1.17 1.04

Pancreas 2.54 1.83 1.31 0.87 0.99 1.47 1.19 1.01

Spleen 2.52 1.86 1.26 0.99 1.04 1.54 1.31 1.03

93

Figure 6.2 Mean CTDIvol normalized organ doses across scanners as a function of patient

perimeter (in cm). The exponential regression curve, equation, and correlation coefficient

for stomach is shown as an example.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

25 50 75 100 125 150

Mea

n o

rgan

do

se/C

TD

I vola

cro

ss s

can

ner

s

Patient Perimeter (cm)

Stomach Liver Adrenals Gall Bladder Kidney Pancreas Spleen

Baby

Irene

Child

Golem

Donna

Visible

Human

HelgaFrank

nDP,Stomach= 3.780 exp(-0.0113 x Perimeter)

R2 = 0.970

94

Table 6.3 Results of exponential regression analysis describing as a function of

perimeter (cm) for fully-irradiated organs.

Organs Exponential Regression Coefficients

Correlation

Coefficient

AO BO R2

Liver 3.824 -0.0120 0.98

Stomach 3.780 -0.0113 0.97

Adrenals 4.029 -0.0128 0.95

Kidney 3.969 -0.0124 0.99

Pancreas 3.715 -0.0122 0.97

Spleen 3.514 -0.0111 0.95

Gall Bladder 3.994 -0.0115 0.95

6.3.B. Partially-Irradiated Organs

The values for each partially-irradiated organ are presented in Table 6.4.

Table 6.4 Mean CTDIvol normalized organ doses across scanners ( ) for each patient

model for partially-irradiated organs. A dash indicates the organ was not included in the

patient model.



Colon 2.31 1.83 1.32 0.91 1.00 1.43 1.13 1.16

S. Intestine 2.60 1.93 1.22 0.68 1.11 0.79 0.82 1.02

Heart 1.59 1.55 0.85 0.59 0.66 0.52 0.73 0.69

Ovaries 2.14 1.70 - - - 0.08 0.12 0.16

Uterus 2.05 1.24 - - - 0.05 0.07 0.09

Lung 1.28 1.07 0.56 0.44 0.45 0.40 0.50 0.55

Esophagus 1.14 - 0.43 0.43 0.40 0.26 0.41 0.43

Breast 0.27 - - 0.09 - 0.26 0.87 0.57

Skin 0.92 0.46 0.29 0.36 0.34 0.27 0.27 0.48

Muscle 0.85 0.59 0.30 0.35 0.28 0.35 0.29 0.34

RBM 0.59 0.32 0.20 0.16 0.20 0.21 0.18 0.25

Bone Surface 1.68 0.93 0.58 0.47 0.60 0.60 0.52 0.74

Bladder 1.50 0.54 0.13 0.10 0.07 0.06 0.07 0.08

The percent coverage of each partially-irradiated organ is reported in Table 6.5

for each patient model. A large portion of organs such as colon and small intestine were

included for almost all patient models. Other organs (i.e. lung, esophagus, skin, etc) had

95

50% or less of their volume encompassed by the scan for all patient models. Finally, a

number of organs, such as ovaries, uterus, glandular breast tissue, and bladder, were fully

or partially scanned in some patient models, while not irradiated at all in others. It should

be noted that for patients models that were not whole-body (Frank, Visible Human, and

Helga) the percent coverage values are artificially high for truncated organs, such as skin,

muscle, and bone, relative to their values if they were whole body models.

Table 6.5 Percent coverage of each partially-irradiated organ (i.e. percentage of organ

volume located within the abdominal scan region). The last two columns report the average

and standard deviation across patient models. A dash indicates that the organ was not

included for the given patient model.


Human Irene Donna Helga Avg SD

Colon 87% 91% 83% 80% 76% 76% 81% 98% 84% 8%

S. Intestine 100% 98% 81% 65% 90% 39% 58% 87% 77% 21%

Heart 59% 86% 53% 50% 50% 15% 51% 61% 53% 19%

Ovaries 100% 100% - - - 0% 0% 0% 40% 55%

Uterus 100% 95% - - - 0% 0% 0% 39% 53%

Lung 45% 50% 32% 30% 30% 16% 29% 43% 34% 11%

Esophagus 40% - 33% 42% 37% 8% 29% 39% 32% 12%

Breast 0% - - 0% - 6% 88% 61% 31% 41%

Skin 38% 23% 20% 31% 27% 16% 19% 38% 26% 9%

Muscle 31% 28% 19% 30% 22% 19% 20% 26% 24% 5%

RBM 26% 21% 18% 19% 24% 16% 18% 28% 21% 4%

Bone Surf 27% 22% 19% 20% 25% 17% 19% 29% 22% 4%

Bladder 54% 17% 0% 0% 0% 0% 0% 0% 9% 19%

An exponential regression analysis to determine how varied as a function

of patient perimeter was performed for the partially-irradiated organs. Table 6.6 shows

the correlation coefficient of the regression analysis along with the average and standard

deviation of the percent coverage of each organ (last two columns in Table 6.5). For

almost all of the partially-irradiated organs, a strong exponential correlation does not

96

exist between and patient perimeter. The only exception is the colon, which had a

relatively high percent coverage (average across patients of 84%) with a relatively low

standard deviation across patient models (8%) compared to other organs. The correlation

coefficients did not appear to be directly related to either the average percent coverage or

the standard deviation of the percent coverage across patient models.

Table 6.6 Average and standard deviation of the percent coverage of each partially-

irradiated organ and the correlation coefficient resulting from the exponential regression

relating to perimeter.

Organ Correlation

Coefficient

Average Percent

Coverage

Standard Deviation of

Percent Coverage

Colon 0.94 84% 8%

Small Intestine 0.62 77% 21%

Heart 0.51 53% 19%

Ovaries 0.52 40% 55%

Uterus 0.60 39% 53%

Lung 0.56 34% 11%

Esophagus 0.29 32% 12%

Glandular breast tissue 0.02 31% 41%

Skin 0.29 26% 9%

Muscle Tissue 0.63 24% 5%

Red Bone Marrow 0.70 21% 4%

Bone Surface 0.67 22% 4%

Bladder 0.56 9% 19%

6.3.C. Non-Irradiated Organs

In order to evaluate the magnitudes of the doses received by non-irradiated organs

their values were compared to those of the fully-irradiated organs. Table 6.7

reports the percent ratio of each non-irradiated organ‘s value to that of the average

value across all fully-irradiated organs. It can be seen that, on average across

patients, the dose to almost all non-irradiated organs is less than 5% of the mean dose to

97

fully-irradiated organs. Therefore, from a practical standpoint it may be acceptable to

consider the doses to most organs absent from the scan region as negligible.

The thymus, a relatively small organ located near the superior boundary of the

abdominal scan region, was the only exception. On average, the thymus received a dose

of 14.9% of that to the fully-irradiated organs. The standard deviation across patients was

also larger (6.1%) as the exact size and proximity to the abdominal scan region of this

organ had appreciable variations across patient models.

Table 6.7 Percent ratios of dose to each non-irradiated organ relative to average fully-

irradiated organ dose. The last two columns report the average and standard deviation

across patient models. A dash indicates that the non-irradiated organ was not included for

the given patient model.



Testis 8.1% 2.2% 0.3% - 0.8% - - - 2.9% 3.1%

Thyroid 5.7% 6.7% 3.4 2.8% 4.3 1.6% 3.8% 7.1% 4.4% 1.8%

Brain 0.6% 0.4% 0.1 0.1% 0.1 0.1% 0.2% 0.4% 0.2% 0.2%

Salivary

Glands - - - 0.5% 0.9 0.4% - 1.9% 0.9% 0.6%

ET - - - 0.2% 0.6 0.3% 0.6% 1.2% 0.6% 0.3%

Prostate - - 0.0 4.1% 2.6 - - - 2.2% 1.7%

Thymus 17.1% 16.4% 7.8 22.3% 6.0 9.2% 16.9% 23.4% 14.9% 6.1%

6.4 Discussion

This study demonstrated the dependence of CTDIvol-normalized organ doses on

patient size for typical abdominal CT exams using a wide range of patient models.

Detailed, scanner-specific Monte Carlo simulations were performed using eight different

voxelized patient models in order to obtain accurate organ dose values. For each patient,

organ doses were normalized by the CTDIvol corresponding to the simulated scanner at

98

the operating conditions described (120 kVp, body bowtie, widest available beam width,

and a pitch of 1.0) and the mean across scanners was obtained for each organ. The

analysis of this data was separated into three categories, organs fully encompassed in the

scan region (fully-irradiated), organs partially encompassed (partially-irradiated), and

organs that were not directly irradiated (non-irradiated).

The analysis of fully-irradiated organ data was performed to extend the

methodology to accurately estimate organ doses from CT introduced in Chapter 3. In that

previous work it was shown that, for a single patient model (Irene from the GSF family

of voxelized models), normalizing organ doses by CTDIvol resulted in values that varied

by less than 10% across different 64-slice MDCT scanners for all fully-irradiated organs.

A similar analysis was performed for each of the patient models used in this study. These

results verify that, for every fully-irradiated organ in any patient model, the CoV across

scanners is less than 10%. Thus, extending the work presented in Chapter 3, it is feasible

to estimate organ dose for fully-irradiated organs simply by multiplying the patient-

specific by the reported CTDIvol, regardless of the scanner model.

This study demonstrates that the CTDIvol obtained with a 32 cm (body) CTDI

phantom can be utilized to account for organ dose disparities from different scanners,

even for small adults and pediatric patients. Also, for this study, all abdominal scan

simulations were performed with the bowtie filter that would be used for an adult

abdomen. This was done in order to isolate the effects of the size of the patient model on

the results under a specific set of operating conditions. Future studies should be

99

performed to determine the regression coefficients for other parameters (such as bowtie

filter, kVp and collimation) settings, especially for predicting dose to smaller patients

since, it is likely that a smaller bowtie filter would be used for scanners that feature

multiple filtration options.

In order to devise a method to estimate patient-specific CTDIvol-to-organ dose

conversion coefficients ( ) for any fully-irradiated organ in any patient, the

dependence of values on patient size was investigated. It was demonstrated that

values have a strong dependence on patient perimeter. As shown by the plot in

Figure 6.2, there was a declining exponential relationship between and patient

perimeter (in cm) obtained from the central slice of the scan region. The exponential

regression analysis resulted in correlation coefficients greater than 0.95 for all seven

fully-irradiated organs. The organ-specific regression coefficients, AO and BO from

Equation 6.1, are displayed in Table 6.3. The strong correlations indicate that the reported

size coefficients can be used to calculate for most patients using Equation 6.1

(these results have not been verified for patients with perimeters much greater than those

examined in this work or for very large patients with tissue outside the scan‘s field of

view). Then, as described above, patient-, scanner-, and exam-specific organ dose

estimates can be obtained by multiplying by the scanner‘s reported CTDIvol (which

takes the technique of the exam into account). Thus, doses for a specific scanner, patient,

and organ can be estimated using Equation 6.2:

100

Eq. 6.2

A schematic description of this proposed dose estimation process is presented in Figure

6.3. It must be emphasized that in order to carry out this process with the size coefficients

reported in Table 6.3 the CTDIvol value should refer to the 32 cm (body) CTDI phantom,

which is not always the case with the value reported by the scanner for pediatric

abdominal exams protocols.

While the primary focus of this chapter was on fully-irradiated organs, results of

radiation dose to partially-irradiated organs were presented as well. This analysis

indicates that the proposed method of size adjustment may be limited in its ability to

estimate dose to organs not fully-encompassed in the scan region. As indicated in Table

6.5, there was considerable variability in the percent coverage for most partially-

irradiated organs across different patient models. This variability was due to the fact that

the relative position of organs, with respect to the anatomical landmarks used to define

the scan region, differed between the patient models. As a result, values for most

partially-irradiated organs did not correlate well with patient size. The results in Table 6.6

show that the correlation coefficients from the exponential regression analysis ranged

from 0.29 to 0.70 for all organs except the colon. The colon was almost fully covered in

the majority of the patient scans and appeared to have a similar size dependency as the

fully-irradiated organs. Furthermore, there was not an obvious relationship between the

average percent coverage or the standard deviation across patients and the exponential

101

regression correlation of with patient size. Partially-irradiated organs will be

further investigated in Chapter 7.

The analysis of the non-irradiated organs showed that they receive small doses

compared to directly-irradiated organs. This can be attributed to the fact that doses due to

only scattered radiation are expected to be much lower than doses from primary radiation

directly from the source, especially for organs located a considerable distance from the

scan region. This study showed that for typical abdominal exams, the majority of non-

irradiated organs were located a sufficient distance outside of the scan region so that they

received very little scattered radiation. Doses to organs such as the thyroid, brain, salivary

glands, extrathoracic region, testis, and prostate were effectively zero.

The thymus, a relatively small non-irradiated organ situated in the center of the

upper chest, was close enough to the superior boundary of the exam region that its

value was ~20% of the average across fully-irradiated organs for some patients. This is a

good example of how small organs just outside of the scan may receive a non-trivial

dose. The dose levels to adjacent non-irradiated organs, such as the thymus for abdominal

exams, appear to be a function of both the organ‘s size and its proximity to the scan

region. For a given patient, the latter is a function of the exact start and stop location (i.e.

where the x-ray beam is turned on and off) relative to the organ‘s exact position. A

conservative approach to estimating doses to small organs near the scan region might be

to assign a dose value equal to some percentage of the average dose to fully-irradiated

organs (e.g. 20% of the fully-irradiated organs to the thymus for abdominal exams). Of

102

course, these organs and their assigned dose percentages will be different for exams of

other body regions. In future studies that focus on estimating dose from other typical

clinical exams (i.e. chest, pelvis, head, etc), those non-irradiated organs that receive a

significant dose will be identified and recommendations for assigning dose values based

on dose to fully-irradiated organs will be established.

Patient perimeter was the metric used for patient size for this study. The

correlation between perimeter and CTDIvol-to-organ dose conversion coefficients for

fully-irradiated organs proved to be very strong. However, this work focused on the

abdomen in which perimeter does not typically fluctuate much over the scan region for a

given patient. In other anatomical regions it might be difficult to determine the best

location at which to obtain a representative perimeter measurement. Furthermore, the

software necessary to obtain perimeter measurements from patient images (as done in this

study) may not be supported by the current scanners‘ image analysis packages. Future

studies will be performed to evaluate other metrics that may correlate with CTDIvol-

normalized organ doses. For example, a metric that utilizes patient attenuation data

throughout the scan region would be advantageous since this information is directly

measured by the CT scanner and reflects patient morphology and composition in addition

to size.

It should be emphasized that the size coefficients (AO and BO) presented in this

work are only appropriate for abdominal CT exams and only for those performed with a

fixed tube current. Coefficients for other scan regions, such as chest, pelvis, and head

103

scans, will need to be generated in future studies. For some of these regions, such as chest

and pelvis, it may be necessary to create gender- and age-specific patient cohorts since,

unlike the abdomen, significant anatomical differences exist between these groups. The

GSF family of voxelized models consists of two pediatric, three adult male, and three

adult female models and, as displayed in Tables 6.2, 6.4, and 6.6, there are several organs

that are not contoured in one or more models. In order to determine accurate size

coefficients for other scan regions, additional patient models may therefore be necessary.

Additionally, investigations into the effects of tube current modulation (TCM) are

underway. TCM is used routinely in abdominal scans53,61,62,67

and, depending on the type

of TCM used, the scheme may adjust the tube current for patient size as well as modulate

along the z-axis and within the x-y plane. Therefore, techniques to account for TCM

similar to those described by Angel, et al.61,62

will be investigated and presented in

Chapter 8.

This study was conducted to investigate the feasibility of accounting for patient

size when determining scanner-independent CTDIvol-to-organ dose conversion

coefficients. It was shown that for fully-irradiated organs, there was a strong correlation

with patient perimeter. The exponential size coefficients presented in Table 6.3 could

thus be used to calculate CTDIvol-to-organ dose conversion coefficients for most patients,

based only on their measured perimeter. Then, with knowledge of the scanner‘s CTDIvol

for the given exam protocol, an accurate estimate of organ dose can be obtained using the

method outlined in Figure 6.3. This approach makes it possible to prospectively or

104

retrospectively estimate organ doses individual patients and introduces the potential to

calculate patient-specific risk estimates based on the organ dose-dependent calculations

outlined in the BEIR VII Report2.

Figure 6.3 The proposed method to estimate patient-, scanner-, and exam-specific organ

dose using the size coefficients (AO, BO), patient perimeter (in cm), and the CTDIvol reported

by the scanner.

Future work is needed to investigate several aspects not fully covered in this

manuscript, including: (a) the effects of Tube Current Modulation and (b) the

development of methods to estimate radiation dose to non-and partially-irradiated organs;

for the latter, developments will have to take into account not only the effects of patient

size, but also other relevant factors including the beam on and beam off location and the

percent of organ irradiated during the scan. These two issues will be the subjects of

Chapters 7 and 8, respectively.

Patient-,

Scanner-,

Exam-specific

Organ Dose

(mGy)

Size Coefficients

(AO, BO)

Patient Perimeter

(p)

Exam-specific

CTDIvol

(body phantom)

Patient-specific

CTDIvol-to-organ

dose conversion

coefficient

105

Chapter 7 Estimating Dose to Partially-Irradiated Organs using CTDIvol -to-Organ

Dose Coefficients

7.1 Introduction

The feasibility of estimating organ doses using patient size corrected CTDIvol-to-

organ dose conversion coefficients for organs fully encompassed in the scan region (i.e.

fully-irradiated) was demonstrated in Chapter 6. Specifically, it was shown that the

values of dose to fully-irradiated organs normalized by the CTDIvol had a strong

exponential correlation with patient perimeter. Additionally, that study indicated that the

dose to organs located completely out of the scan regions (i.e. non-irradiated) was, on

average, less than 5% of the mean dose to fully-irradiated organs. These doses are small

enough that it is reasonable to ignore their contributions to the overall dose concern.

It was also shown that the doses to organs only partially encompassed in the scan

region (i.e. partially-irradiated) were large enough to be non-trivial but did not show a

correlation with patient size. The purpose of this study is to further investigate the doses

to partially-irradiated organs in order to either extend the CTDIvol-to-organ dose

estimation method or to determine another method of estimating their average dose. In

this work, partially-irradiated organs will be subdivided into ―in-beam‖ and ―out-of-

beam‖ portions and dose to each of these individual segments will be obtained with

Monte Carlo simulations.

106

A number of basic assumptions will be utilized in order to derive an expression

for estimating dose to partially-irradiated organs. First, since Chapter 6 showed that

organs not directly exposed to primary radiation received very low doses from scattered

x-rays, Assumption A is that the dose to the out-of-beam segment of the organ is very

small compared to the dose to the in-beam segment. Next, because it is possible to

estimate dose to organs fully encompassed in the scan region, Assumption B is that a

predictive model exists to estimate dose for the in-beam segment of the organ.

The total dose to an organ broken into two segments is given by the sum of the

energy deposited in each segment divided by the sum of the masses of each section (note

that the subscripts ―in‖ and ―out‖ will be used for the in-beam portion and out-of-beam

portion of the partially-irradiated organ, respectively):

Eq. 7.1

Assumption A states that the dose to the out-of-beam segment is much less than the dose

to the in-beam segment and, since dose is proportional to the deposited energy, the sum

of Ein and Eout is approximately equal to Ein (i.e. Ein >> Eout thus Ein + Eout ≈ Ein):

Eq. 7.2

Since the term outside the parenthesis is equal to dose to the in-beam portion and mass is

the product of the tissue density and volume of the segment:

107

Eq. 7.3

Now, because the two segments of the partially-irradiated organ are composed of the

same tissue they have the same density (i.e. ρin = ρout), so:

Eq. 7.4

The total organ volume (Vorgan) is given by the sum of the in-beam and out-of-beam

volumes. If we introduce a term, αorgan, to represent the fraction of the total organ volume

consisting of the in-beam portion of the organ (i.e. Vin = αorganVorgan and Vout = (1-

αorgan)Vorgan):

Eq. 7.5

Thus, it will be assumed that, for each organ, the percent coverage (αorgan) for a typical

abdomen exam does not vary much across patients (Assumption C). Simplification of this

expression reveals that:

Eq. 7.6

This illustrates that, if it is possible to accurately estimate both the dose to the in-beam

portion of the partially-irradiated organ and the percentage coverage of the organ, a

simple expression exists to estimate the total organ dose.

The goal of this study will be to first test the accuracy of Assumptions A, B, and

C) in order to assess the validity of Equation 7.6. First, the dose to the out-of-beam

108

segments will be compared to dose in-beam segments in order to evaluate Assumption A.

Next, Assumption B will be examined by determining the feasibility of accurately

estimating dose to the in-beam segments based on patient size information. Then, the

variance of the percent coverage of each partially-irradiated organ across patient models

will be calculated in order to assess Assumption C.

It is recognized that the assumptions discussed above may not be appropriate for

all organs and patients. However, the doses to partially-irradiated organs calculated using

Equation 7.6, which rely on these approximations, still probably represent a better

estimate than current dose assessment methods (such as CTDI metrics). In order to

quantify the magnitude of error the accuracy of the dose estimates calculated with

Equation 7.6 will be evaluated by comparing them to the simulated values presented in

Table 6.5. This analysis will indicate the magnitude of errors in the dose estimates due to

the inaccuracies introduced by assumptions A and B and from using a fixed αorgan value

as outlined above.

7.2 Methods

The methods used in this study were very similar to those described in Section

6.2. This study used the same set of eight patient models from the GSF Family of

Voxelized phantoms which included two pediatric models (Baby and Child), three adult

males (Golem, Frank, and Visible Human), and three adult females (Irene, Donna, and

Helga). Table 6.1 lists several characteristics of these patient models, including their

gender, age, height, weight, and perimeter of the central slice of a typical abdomen CT

109

exam. As described in Chapter 6, the arms were also removed for this study.

Furthermore, this study also simulated typical abdomen CT exams. The scan length for

each model is also included in Table 6.1.

This work focused only on partially-irradiated organs. Since the GSF patient set is

small (8 patients) and consists of males and females, a number of gender-specific organs

(such as gonads and glandular breast tissue) were omitted for this study in order to obtain

the best possible statistics. A subset of nine of the partially-irradiated organs listed in

Table 6.3 was thus utilized in this study. These organs include red bone marrow (RBM),

colon, lungs, esophagus, bone surface, skin, heart, muscle, and small intestine.

7.2.A. Partial-Organ Subdivisions

Each GSF model consists of a three-dimensional matrix where each entry

represents the material or organ codes for each voxel. Software (written in C) was created

to classify each matrix entry that corresponded to a partially-irradiated as either in-beam

or out-of-beam. The in-beam voxels were those that were located within the longitudinal

range of the abdomen exam. The out-of-beam voxels were those located out of the scan

range. Then, for each partially-irradiated organ, all of the voxels classified as in-beam for

a given organ were assigned a unique code that varied from the out-of-beam voxel code.

Different in-beam organ codes were used for each organ. The results of this process are

illustrated in Figure 7.1.

110

Figure 7.1 Illustration of the process to segment partially-irradiated organs into "in-beam"

and "out-of-beam" segments.

In this figure, Organ A represents a small, local organ, such as the heart or lungs, which

happens to straddle one edge of the scan region. Organ B represents a larger, non-local

organ that traverses a large longitudinal portion of the body, such as skin, muscle, and

skeletal tissues.

This code also recorded the total number of voxels for each segment of each

partially-irradiated organ (i.e. total number of in-beam voxels and out-of-beam voxels for

each organ). The total volume of each segment was obtained by multiplying the voxel

volume by the number of voxels. The same procedure was performed in Chapter 6 but the

results will be re-reported in this chapter to facilitate the analysis in the Discussion

section.

7.2.B. The CT Scanners and Exam Protocols

111

This study utilized the same set of CT scanners and CT exam protocols described

in Section 6.2.B. These scanners include the LightSpeed VCT, Brilliance CT 64,

SOMATOM Sensation 64, and Aquilion 64. Again, in order to ensure that the dosimetry

simulations performed for this study were as comparable as possible across scanner

models, all simulations were carried out using a tube voltage of 120 kVp, a pitch of 1, the

bowtie filter designed for the adult body, and the widest available collimation setting for

each scanner. The selected nominal beam width and detector configuration settings were

40 mm for the LightSpeed VCT, for the Brilliance CT 64, 28.8 mm for the Sensation 64

scanners, and 32 mm for the Aquilion 64. In order to anonymize the results, a random

number was assigned to each scanner, either 1, 2, 3, or 4.

7.2.C. Physically Measured CTDI Values

CTDIvol values were obtained for each scanner using the scan protocol described

above using the techniques described Chapter 6 (Section 6.2.C). These CTDIvol values

were all obtained using a 32 cm diameter (body) CTDI phantom. Each CTDIvol value was

recorded on a per mAs/rotation basis (denoted mGy/mAs).


7.2.D.1. Abdominal Exam Simulations

For Scanners 1-4, Monte Carlo simulations of helical exams that utilized the

scanning protocol described in Section 7.2.B were performed using each of the GSF

patient models. As noted above, abdomen exams were performed for which the scan

112

region ranged from approximately 1 cm superior to the top of the diaphragm to

approximately 1 cm inferior to the illiosacral joint for each patient model.

Doses were separately tallied within the in-beam and out-of-beam segments of each

partially-irradiated organ. Since the total mAs value used by different scanners depends

on the total number of rotations (and thus on the nominal beam collimation), dose values

were converted into units of mGy/mAs (where mAs refers to mAs/rotation) by

multiplying each mGy/total mAs value by the total number of rotations used in the

corresponding helical scan simulation (from mGy/total mAs to mGy/mAs).

7.2.E. Data Analysis

7.2.E.1. CTDIvol Normalized Organ Doses

Each simulated abdominal helical scan resulted in a unique dose value for each

patient and scanner combination. Extending the convention introduced in Chapters 5 and

6, these dose values will be denoted as or , where P refers to the patient

model, S to scanner, O to organ, and ―in‖ or ―out‖ for the segment classification (in-beam

or out-of-beam). Each dose value was normalized by the measured CTDIvol value (also in

mGy/mAs) corresponding to the simulated scanner, resulting in a unitless value, denoted

or . For each patient and organ combination, the average normalized

dose was calculated across scanners and denoted or .

7.2.E.2. Out-of-Beam Segments Analysis

113

In order to determine their relative magnitudes, the doses to the out-of-beam

segments of partially-irradiated organs were compared to the dose to the in-beam

segments. Specifically, for each partially-irradiated organ, the ratio of the value

to the value was calculated. The average and standard deviation of the ratios

were calculated across patients and expressed as a percentage.

7.2.E.3. In-Beam Segments Analysis

For the in-beam segment of each partially-irradiated organ, the relationship

between values and patient size was investigated. For each partially-irradiated

organ, was plotted as a function of patient perimeter to determine if a correlation

exists. Based on the plots, a similar analysis as reported in Section 6.2.E.3 was

performed. Exponential regression equations were obtained in the form of:

Eq. 7.7

where unique AO,in and BO,in values exist for each partially-irradiated organ. The

correlation coefficient (R2) of the exponential fit was also obtained for each organ.

7.2.E.4. Percent Coverage Analysis

For each GSF patient model, the fraction of each partially-irradiated organ‘s

volume that was encompassed in the scan region was determined using the software

discussed in Section 7.2.A. These values were previously defined as the αorgan term in

Equation 7.6. In order to determine the variation of percent coverage values across

114

patients, the average, standard deviation, and coefficient of variation (CoV = standard

deviation/mean) of the GSF model‘s αorgan values were obtained.

7.2.E.5. Accuracy of Partially-Irradiated Organ Dose Estimates

Dividing each side of Equation 7.6 by the CTDIvol gives:

Eq. 7.8

Since Dorgan/CTDIvol is equal to and Din/CTDIvol is equal to this can be re-

written as:

Eq. 7.9

where AO,in and BO,in are the exponential fit parameters described in Section 7.2.E.3 and

αorgan are the organ-specific average percent coverage values across patients described in

Section 7.2.E.4. Because organ dose is directly proportional to , the errors of the

estimates calculated with Equation 7.9 are equal to the errors for organ dose

estimates calculated using Equation 7.6. In order to quantify these errors, values

were calculated with Equation 7.9 and directly compared to analogous values reported in

Table 6.4 that were obtained with simulations. The percent error of each estimated value

was calculated for each partially-irradiated organ for each patient model. Finally, the

average and standard deviation of these errors across patients were obtained.

7.3 Results

7.3.A. CTDIvol Normalized Doses to In-Beam and Out-Of-Beam Segments

115

The results of the doses simulated to the in-beam and out-of-beam segments,

normalized by measured CTDIvol values and averaged across scanners, are reported in

Tables 7.1 and 7.2, respectively.

Table 7.1 CTDIvol normalized dose to the in-of-beam portion of each partially-irradiation

organ. (i.e. ). Note that the esophagus was not included in the Child model and the

small intestine was fully-irradiated in the Baby model.



RBM 1.90 1.31 0.79 0.59 0.91 0.72 1.08 0.68

Colon 2.59 1.97 1.34 1.09 1.56 1.18 1.81 1.25

Lungs 2.00 1.62 0.94 0.89 1.11 0.87 1.23 0.86

Esophagus 2.14 - 0.86 0.78 1.03 0.77 1.13 0.78

Bone Surf 5.37 3.70 2.28 1.72 2.61 2.10 3.05 1.95

Skin 2.13 1.76 1.27 1.03 1.36 1.19 1.57 1.17

Heart 1.95 1.65 0.96 0.81 1.17 0.85 1.24 0.93

Muscle 2.24 1.79 1.23 0.98 1.37 1.11 1.59 1.11

S Intestine 2.60 1.95 1.20 0.88 1.44 1.12 1.59 1.18

Table 7.2 CTDIvol normalized dose to the out-of-beam portion of each partially-irradiation

organ. (i.e. ). Note that the esophagus was not included in the Child model and the

small intestine was fully-irradiated in the Baby model.



RBM 0.13 0.06 0.04 0.05 0.04 0.07 0.04 0.06

Colon 0.49 0.37 0.23 0.24 0.19 0.32 0.22 0.23

Lungs 0.68 0.52 0.31 0.25 0.30 0.31 0.24 0.27

Esophagus 0.47 - 0.23 0.17 0.15 0.21 0.19 0.17

Bone Surf 0.40 0.19 0.14 0.16 0.13 0.21 0.13 0.17

Skin 0.17 0.06 0.03 0.06 0.03 0.04 0.03 0.03

Heart 1.07 0.94 0.49 0.38 0.48 0.44 0.38 0.39

Muscle 0.23 0.11 0.06 0.08 0.05 0.07 0.06 0.05

S Intestine FI 0.93 0.32 0.29 0.28 0.35 0.27 0.49

7.3.B. Relative Magnitude of Doses to Out-of-Beam Segments

Table 7.3 reports the ratio (expressed as a percent) of CTDIvol normalized dose to

the out-of-beam portion of each partially-irradiation organ to the in-beam portion (i.e.

116

). The average and standard deviation across patient models are listed

in the last two columns. It can be seen that, on average, out-of-beam dose is less than

~20% of the in-beam dose for six of the nine partially-irradiated organs (for the

esophagus the ratio is just over 20%). The remaining organs had average ratios ranging

from 30% to 47%. It should be noted that the ratios for individual patients were as high as

57% (for the heart in the Child model). The standard deviations across patients were

relatively low for most organs (5% or less for seven of the nine organs), indicating that

the ratio of out-of-beam dose to in-beam dose was fairly consistent across patient models.

Table 7.3 Ratio (expressed as a percent) of CTDIvol normalized dose to the out-of-beam

portion of each partially-irradiation organ to the in-beam portion (i.e. ).

Note that the esophagus was not included in the Child model and the small intestine was

fully-irradiated in the Baby model.



RBM 7% 5% 6% 9% 5% 9% 4% 8% 7% 2%

Colon 19% 19% 17% 22% 12% 27% 12% 19% 18% 5%

Lungs 34% 32% 33% 29% 27% 36% 20% 31% 30% 5%

Esophagus 22% - 26% 21% 14% 28% 17% 22% 21% 5%

Bone Surf 7% 5% 6% 10% 5% 10% 4% 9% 7% 2%

Skin 8% 3% 3% 6% 2% 4% 2% 3% 4% 2%

Heart 55% 57% 52% 46% 41% 52% 31% 42% 47% 9%

Muscle 10% 6% 4% 8% 4% 6% 3% 5% 6% 2%

S Intestine FI 48% 26% 33% 19% 32% 17% 41% 31% 11%

7.3.C. Size Dependency of Doses to In-Beam Segments

The values for fully-irradiated organs presented in Table 7.1 are plotted

as a function of patient perimeter in Figure 7.2. This plot indicates a decreasing

exponential relationship (the exponential regression line and equation for bone surface is

displayed as an example). Exponential regression equations, as described by Equation

117

7.7, were obtained for the in-beam segment for each partially-irradiated organ. The size

coefficients (AO,in and BO,in), along with the correlation coefficient of the exponential

regression analysis (R2), are displayed in Table 7.4. The correlation coefficients are all ≥

0.89 for all partially-irradiated organs. This indicates Equation 7.7 is an excellent method

to obtain values for patients with perimeters ranging from small children to

relatively large adults.

Figure 7.2 CTDIvol normalized dose values for the in-beam segment of each partially-

irradiated organ as a function of patient perimeter in cm. The exponential trendline for

bone surface is shown as an example.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

30 50 70 90 110 130

Mea

n o

rgan

dose

/CT

DI v

olacr

oss

sca

nn

ers

Perimeter (cm)

Red bone Marrow Colon Lungs

Esophagus Bone Surface Skin

Heart Muscle Small Intestine

nDP,Bone Surface= 7.932 exp(-0.0129 x Perimeter)

R2 = 0.970

118

Table 7.4 Results of exponential regression analysis describing as a function of

perimeter (cm) for the in-beam segment of partially-irradiated organs.

Organs Exponential Regression Coefficients

Correlation

Coefficient

AO,in BO,in R2

Red Bone Marrow 2.853 -0.0132 0.97

Colon 3.641 -0.0102 0.98

Lungs 2.741 -0.0104 0.90

Esophagus 2.860 -0.0119 0.89

Bone Surf 7.932 -0.0129 0.97

Skin 2.827 -0.0083 0.99

Heart 2.829 -0.0107 0.94

Muscle 3.123 -0.0096 0.99

Small Intestine 3.867 -0.0118 0.98

7.3.C. Partially-Irradiated Organ Coverage Results

The percent coverage of each partially-irradiated organ is reported in Table 7.5

for each GSF patient model. Additionally, the average and standard deviation across

patients is reported in Table 7.6. These standard deviation results in Table 7.6 show that

the percent coverage for a given organ across patients (i.e. standard deviation of αorgan)

ranges from 4% (for skeletal tissues) to 21% (for small intestine). This variation is due to

the non-uniform position of a given organ for different patient models. This typically

leads to greater percent coverage variation for smaller organs typically located at the scan

boarders, such as the small intestine, heart, and lungs. The validity of the assumption that

a single αorgan value can be applied to any patient model appears to vary based on the

specific organ.

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Table 7.5 The percent coverage of each partially-irradiated organ for a typical abdomen

scan to each GSF patient model.



RBM 26% 21% 18% 19% 18% 28% 16% 24%

Colon 87% 91% 81% 80% 83% 98% 76% 76%

Lungs 45% 50% 30% 30% 32% 43% 16% 30%

Esophagus 40% - 32% 42% 33% 39% 8% 37%

Bone Surf 26% 21% 18% 19% 18% 28% 16% 24%

Skin 38% 23% 18% 31% 20% 38% 16% 27%

Heart 59% 86% 52% 50% 53% 61% 15% 50%

Muscle 31% 28% 20% 30% 19% 26% 19% 22%

S Intestine 100% 98% 59% 65% 81% 87% 39% 90%

Table 7.6 The average percent coverage for a typical abdomens scan of each partially-

irradiated organ across patients (αorgan) and the corresponding standard deviation.

Average

(αorgan) SD

RBM 21% 4%

Colon 84% 8%

Lungs 34% 11%

Esophagus 33% 12%

Bone Surf 21% 4%

Skin 26% 9%

Heart 53% 19%

Muscle 24% 5%

S Intestine 77% 21%

7.3.D. Validation of Partially-Irradiated Dose Estimates

Estimates of calculated for each partially-irradiated organ using Equation

7.9 along with each patient‘s perimeter (Table 6.1), the AO,in and BO,in values in Table

7.4, and the αorgan coefficients in Table 7.6 are presented in Table 7.7.

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Table 7.7 Estimated values obtained using Equation 7.9 for the partially-irradiated

organs of each GSF patient model.



RBM 0.38 0.28 0.17 0.12 0.19 0.15 0.25 0.16

Colon 2.11 1.66 1.16 0.86 1.25 1.04 1.55 1.07

Lungs 0.65 0.51 0.35 0.26 0.38 0.31 0.47 0.32

Esophagus 0.61 0.46 0.31 0.22 0.33 0.27 0.43 0.28

Bone Surf 1.06 0.79 0.50 0.34 0.55 0.43 0.72 0.45

Skin 0.55 0.45 0.34 0.27 0.36 0.31 0.43 0.32

Heart 1.02 0.80 0.54 0.40 0.59 0.48 0.74 0.50

Muscle 0.47 0.37 0.27 0.20 0.29 0.24 0.35 0.25

S Intestine 1.95 1.48 0.97 0.69 1.06 0.85 1.36 0.89

The percent errors of each estimate, compared to the simulated

values presented in Table 6.4, are reported in Table 7.8. The average and standard

deviation across patients of the absolute values of the estimation errors are listed in the

final two columns of Table 7.8

Table 7.8 Percent errors of the estimates obtained with the method derived in this

chapter with respect to the simulated values obtained with simulation (Table 6.4). The

average and standard deviation of the absolute percent errors across patient models are in

the last two columns.



Abs

Avg

Abs

SD

RBM -36% -14% -2% -26% -4% -40% 20% -23% 21% 14%

Colon -9% -9% 3% -6% -5% -11% 9% 7% 7% 3%

Lungs -49% -52% -29% -42% -32% -43% 19% -28% 37% 12%

Esophagus -46% - -26% -50% -23% -38% 62% -30% 39% 14%

Bone Surf -37% -15% -5% -27% -5% -42% 20% -24% 22% 14%

Skin -40% -1% 28% -27% 23% -36% 59% -6% 27% 19%

Heart -36% -49% -26% -33% -30% -30% 43% -24% 34% 9%

Muscle -45% -36% -9% -42% -4% -30% 2% -12% 23% 18%

S Intestine -25% -24% 18% 1% -12% -17% 73% -20% 24% 21%

While the minimum absolute error of the estimate was 7% (±3%, for the

colon), the majority of organ dose estimates had absolute errors ranging from 21%

121

(±14%, for red bone marrow) to 39% (±14%, for the esophagus). As stated above, the

percent errors of estimates are the same as the percent errors associated with organ

dose estimates using the proposed method along with the reported AO,in , BO,in , and αorgan

coefficients

7.4 Discussion

The goal of this study was to develop a scanner- and patient-independent method

to estimate doses to partially-irradiated organs from MDCT exams. This work focused on

typical abdomen exams that ranged from approximately 1 cm superior to the top of the

diaphragm to approximately 1 cm inferior to the illiosacral joint. Three assumptions were

made in order to derive an expression for estimating dose to a partially irradiated organ

(Equation 7.6), including: A) that the portion of the organ outside the scan region

received very small doses compared to the portion in the scan, B) that it is possible to

accurately estimate the dose to the portion of the organ in the scan region, and C) the

percentage of the organ included in the scan does not vary much across patients.

Assumption A was assessed by comparing the dose to the out-of-beam segments

to the dose of the in-beam segments. It was shown that the percent ratio of CTDIvol

normalized dose to the out-of-beam and in-beam segments ranged between 7% (red bone

marrow) to 47% (for the heart). In general, smaller organs located near the scan region

had larger ratios. This makes sense as larger organs have a greater percentage of their

volume located further from the scan and thus receive less scattered radiation. It is not

122

clear how small the ratio of out-of-beam dose to in-beam dose should be in order to

validate Assumption A, but as noted above, the out-of-beam dose is less than ~20% of

the in-beam dose for six of the nine partially-irradiated organs (red bone marrow, colon,

esophagus, bone surface, skin, and muscle). The remaining organs (lungs, heart, small

intestine) have larger ratios indicating that the proposed method may not be as successful

in estimating their dose.

The feasibility of using the method of accounting for patient size and estimating

dose to fully-irradiated organs, presented at the end of Chapter 6, was evaluated for

estimating dose to the in-beam segment of partially-irradiated organs. As shown in Figure

7.2 and Table 7.4 there is an excellent correlation between CTDIvol normalized in-beam

dose and patient perimeter for all organs. This indicates it is possible to calculate

CTDIvol-to-in-beam organ doses for patients with sized ranging from babies to large

adults using size correction coefficients AO,in and BO,in presented in Table 7.4. The fact

that this method exists to accurately estimate dose to the in-beam portion of partially-

irradiated organs suggests that Assumption B is valid for any combination of patient and

organ.

The percent coverage of each organ for the typical abdomen scans investigated in

this study was assessed for each patient model. Table 7.6 reports the average percent

coverage across patients (i.e. αorgan in Equation 7.6). The standard deviation of these

results indicate that the variation of percent coverage across patients is smaller for

relatively large organs that span a large portion of the patient‘s longitudinal range (i.e.

123

skin, muscle, and skeletal tissues). Organs with less longitudinal range located near the

scan boundary, such as the heart and small intestine, had larger standard deviations due to

the variation in their exact locations across patients. This indicates that Assumption C is

better satisfied by organs with large longitudinal range than smaller, local organs.

In order to quantify the errors introduced by the assumptions discussed above

partially-irradiated organ dose estimates made using Equation 7.8 along with the

coefficients derived in throughout this study were directly compared to the simulated

doses presented in Chapter 6. The percent errors of the estimate for each organ in each

GSF patient model presented in Table 7.8 spanned from 1% to 73%. The average of the

absolute percent errors are typically ranged from 21%-39% (the colon had an average of

7%, which was the only average less than 20%). The standard deviations of these

averages were on the order of ~10%-20%, and serve as error bars for the percent error

estimates.

The method of estimating dose presented in this chapter represents the first

technique of estimating doses to partially-irradiated organs that is applicable to any

scanner, patient, and organ. This fact alone suggests that these dose estimates are more

informative than simple CTDIvol values included in dose reports. As a result, the dose

estimation method for partially-irradiated organs summarized by Equation 7.9 and

diagrammed in Figure 7.3 can provide useful information for the overall assessment of

dose from a partial-body MDCT exam.

124

Eq. 7.10

where DS,P,O is dose to the partially-irradiated organ (in mGy), αorgan is the average

percent coverage across patient models for the exam type (Table 7.6 lists values for

typical abdomen scans), and AO,in and BO,in are the size correction coefficients (Table 7.4

lists values for typical abdomen scans).

Figure 7.3 Diagram of the proposed method to estimate patient-, scanner-, and exam-

specific dose to partially-irradiated organs using the size coefficients (AO,in, BO,in), average

percent coverage (αorgan), patient perimeter (in cm), and the CTDIvol.

It should be noted that many of the partially-irradiated organs considered for this

study have relatively large ICRP Publication 103 tissue weighting factors5 (i.e. the factor

for red blood marrow, colon, and lung is 0.12, which is the largest factor). This indicates

even though partially-irradiated organs may receive less dose than fully-irradiated organs,

an accurate estimate of their dose may still be as important due to their increased

Patient-,

Scanner-,

Exam-specific

Organ Dose

(mGy)

Partial-

irradiated Organ

Size

Coefficients

(AO,in,BO,in)

Patient Perimeter

(p)

Exam-specific

CTDIvol

CTDIvol-to-dose

conversion

coefficient for In-

beam segment

(

Partially-

irradiated Organ

Percent

Coverage

(αorgan)

125

radiosensitivity. The list of partially-irradiated organs for a given scan type (i.e. chest,

abdomen, pelvis, etc.) depends on the scan boundaries, however, radiosensitive organs

like red bone marrow, bone surface, and skin will always be included. This study was

limited by the small number of available full-body patient models with segmented

organs. A larger number of patient models should be used to validate this work by

repeating the methods to obtain AO,in, BO,in, and αorgan coefficients and more rigorously

evaluate the accuracy of dose estimates to partially-irradiated organs.

126

Chapter 8 The Feasibility of CTDIvol -to-Organ Dose Coefficients that Account for

Tube Current Modulation

8.1 Introduction

Several technical mechanisms have been developed in order to tailor the amount

of radiation delivered to patients during a CT scan based on their size or anatomy. The

most widely used of these is tube current modulation (TCM) in which the radiation

output is varied on the fly by adjusting the tube current time product (mAs) based on the

local attenuation due to the patient. The goal of TCM is to minimize dose while

maintaining a user selected image quality metric. Most scanners now feature three

dimensional TCM that modulates the tube current in both the axial and longitudinal

planes. An example of a tube current function for a chest/abdomen/pelvis is shown in

Figure 8.1.

Figure 8.1 Tube current function illustrating modulation of the tube current (mA) in the

axial plane (high-frequency oscillations) and longitudinal plane (low-frequency oscillations).

0

100

200

300

400

500

600

0 50 100 150 200 250 300

Table Position (mm)

Tu

be

Cu

rre

nt

(mA

)

127

The high frequency oscillations are due to angular modulation (in the axial, or

x/y, plane) in which the tube current is lowered when projecting through the anterior-

posterior part of the patient, then increased when x-rays must pass through the thicker

lateral portion, then reduced again for the posterior-anterior projections. The lower

frequency envelope represents the longitudinal modulation in which the average mAs is

reduced in body regions with less attenuation (such as the chest), and increased in higher

attenuating areas (such as the abdomen and pelvis regions).

Each scanner manufacturer has implemented a unique TCM algorithm that

requires users to provide certain input metrics to specify the desired image quality of the

final image and, consequently, control the magnitude of the average mAs over the scan

range67

. For example, the TCM feature on Siemens‘ SOMATOM Sensation 64 scanner,

called CareDose4D, requires the user to specify a value referred to as the Quality

Reference mAs. This mAs value is defined as the effective mAs (i.e. mAs/pitch)

appropriate for a standard sized ―reference‖ patient required to achieve the image quality

desired for the actual patient‘s scan. The actual mAs values are then modulated

throughout the scan based on the size and composition of the actual patient to ensure the

resulting image quality of each slice approximately matches that set by the Quality

Reference mAs. The majority of healthcare facilities use TCM as part of their standard

body scanning protocols.

As described in Chapter 1, the most widely used set of metrics to evaluate the

radiation dose delivered to a patient from a CT scan are those specified by the CTDI

128

methodology. It has become standard for CT manufacturers to report CTDIvol values on

the scanner console for each CT exam based on the protocol. The CTDIvol values reported

for TCM exams are typically calculated using the average mAs for the patient‘s scan. An

example of a typical dose report for an exam performed with TCM is shown in Figure

8.2. The dose report includes the kVp, the actual average mAs across the scan, the

reference mAs, and the CTDIvol based on the actual average mAs for each scan in the

exam.

Figure 8.2 An anonymized dose report for an exam performed with TCM on a Siemens

Sensation 64 located at UCLA. For this exam, the first scan was a used to generate a two-

dimensional planning image called a “topogram”. Then, two helical scans were performed

and information including the kVp, average mAs, TCM reference mAs, and CTDIvol for

both is included in the report.

For clarity, in this chapter the CTDIvol reported by the scanner for a TCM exam

(based on the average mAs across the scan) will be denoted CTDIvol,Avg mAs. Additionally,

the CTDIvol corresponding to the reference mAs will be denoted CTDIvol,Ref mAs. It should

be noted that the CTDIvol,Ref mAs is generally not a value included in dose reports. Since

129

CTDIvol values are proportional to mAs, CTDIvol,Ref mAs can be calculated by multiplying

the CTDIvol,Avg mAs (from the dose report) by the ratio of the reference mAs and actual

average mAs. For example, the CTDIvol,Ref mAs corresponding to Scan 2 in the dose report

shown in Figure 8.3 is calculated as .

The studies establishing the feasibility of the organ dose estimation technique in

Chapters 5 and 6 only involved fixed tube current scans. Specifically, the organ dose

simulations and CTDIvol values were obtained without any dose reduction techniques.

Therefore, in order to extend the utility of this method, the purpose of this work is to

determine the feasibility of extending the CTDIvol-to-organ dose estimation method to

accurately predict organ doses from abdomen and chest exams performed using TCM.

Since MDCT scanners from different manufacturers utilize different TCM algorithms this

initial feasibility study will only focus on organ doses from the Siemens Sensation 64

scanner.

As a logical initial step, the first aim of this study will be to assess the accuracy of

organ dose estimates calculated with the CTDIvol included in the dose report (CTDIvol,Avg

mAs). Specifically, these dose estimates will be obtained with Equation 6.2 using the

CTDIvol,Avg mAs. Since AO and BO coefficients are generated based on fixed tube current

exams, these dose estimates will likely have a large error compared to doses obtained

with detailed TCM simulations.

The feasibility of an alternative estimation technique that utilizes the CTDIvol,Ref

mAs will be investigated. This method involves generating patient-specific TCM

130

correction factors for doses estimated with Equation 6.2 and the CTDIvol,Ref mAs.

Correction factors will be defined as the ratio of the actual organ dose to the estimated

organ dose. If a number of patients were scanned with a given Quality Reference mAs

and organ dose estimates were calculated using the corresponding CTDIvol,Ref mAs each of

the dose estimates would be based on a CTDIvol that is not indicative of the actual

radiation output. Since the TCM functions of these patients and hence their actual organ

doses are primarily determined by their size, the magnitude of the estimated doses‘ error

should also be a function of size. So, it is hypothesized that accurate patient-specific

TCM correction factors can be calculated based on patient size. Therefore, the second

aim of this study will be to investigate the correlation between patient-specific TCM

correction factors for dose estimates calculated with CTDIvol,Ref mAs values and patient-

size for each organ. A strong correlation will indicate the feasibility of including a

patient-specific TCM correction factor term in Equation 6.2 for dose estimates obtained

using the CTDIvol,Ref mAs.

8.2 Methods

8.2.A. Study Overview

The Monte Carlo simulation package described in Chapter 3 was modified to

model TCM exams, as described in Section 8.2.D. In order to perform a TCM simulation

it is necessary to use a patient model with a known tube current function, which is not the

case for the GSF Family of Voxelized models. As a result, new patient models were

131

generated based on images acquired from actual exams performed at UCLA. Simulated

organ doses from TCM scans were then obtained using the tube current functions for

these patients which were extracted from the scan‘s raw data file. These patient models

are discussed in detail in Sections 8.2.B and 8.2.C and the TCM simulations are described

in Section 8.2.D.1.

Each of these patient models were generated from a chest exam or an

abdomen/pelvic exam. As discussed above, this study involves estimating doses using

Equation 6.2, which requires AO and BO coefficients. The AO and BO coefficients

reported in Table 6.3 are for abdomen scans and thus are not appropriate for the exams

types simulated in this study. So, AO and BO coefficients had to be generated for chest

and abdomen/pelvis exams, which required fixed tube current simulations for each

patient model. These fixed tube scans and the resulting AO and BO coefficients are

outlined in Section 8.2.E. These scan region-appropriate AO and BO coefficients were

then used to evaluate the two TCM dose estimation methods outlined above.

8.2.B. Patient Selection and Tube Current Modulated Exam Protocols

The patients selected for this study each underwent a clinically indicated CT exam

acquired on a Siemens Sensation 64 scanner with CareDose 4D turned on. Two cohorts

of patients were defined for this study. The first consisted of twenty adult females who

underwent chest exams that typically ranged from the thoracic inlet to the lung basis. As

an indicator of patient size, the perimeter of each of these patients was obtained using a

semi-automated technique to obtain the length of the body contour on an image

132

containing at least one nipple. The second cohort consisted of 17 adult males and 23 adult

females who underwent abdomen/pelvis exams that typically ranged from the diaphragm

to the illiosacral joint. The perimeters of these patients were also obtained using semi-

automated techniques, this time using a single image that contained liver, spleen, and

kidney tissue. For each patient used in this study both clinical images and raw projection

data were obtained under Institutional Review Board (IRB) approval.

Each patient was scanned using the routine clinical scanning protocols for their

given scan type (i.e. chest or abdomen/pelvis) normally performed for the Siemens

Sensation 64 scanner at UCLA. All of the chest and abdomen/pelvis exams were helical

scans performed using a tube voltage of 120 kVp. The chest scans were performed with a

Quality Reference mAs that ranged from of 235 to 250, a nominal collimation of 32 x 0.6

mm, and pitch values of 1.0 (16 patients), 0.8 (three patients), or 1.2 (one patient). The

abdomen/pelvis exams were all acquired with a Quality Reference mAs of 275, a nominal

collimation of 32 x 0.6 mm, and pitch values of 1.0 (25 patients), 1.05 (eleven patients),

0.95 (four patients), or 0.65 (one patient). Every patient in the abdomen/pelvis cohort was

scanned with intravenous iodine contrast, while some also had oral contrast. For each

patient the average effective mAs and CTDIvol,Avg mAs was obtained from the patient‘s

dose report. Then, for each patient, the CTDIvol,Ref mAs was calculated as

Eq. 8.1

8.2.C. Voxelized Patient Models

133

A voxelized model of each patient was created from image data using the

methods described by Angel, et al.61,62

and diagramed in Figure 8.3. First, organs of

interest in each cohort were explicitly segmented on each slice using manual and semi-

automatic contouring techniques. Organs that were fully encompassed in the scan region

were chosen for this study. Specifically, the lungs and glandular breast tissue were

segmented in the chest cohort, and the liver, spleen, and kidneys were segmented in the

abdomen/pelvis cohort. The density and material composition of each voxel within each

organ contour was defined based on the corresponding description in the ICRU Report 44

composition of body tissue tables55

. The composition of the remaining voxels in each

image were automatically assigned to one of six tissue types (lung, fat, water, muscle,

bone or air) and one of 17 density levels based on their Hounsfield Unit (HU) value using

the methods described in DeMarco et al.68

Figure 8.3 Generation of a voxelized model: (a) original patient image, (b) radiologist’s

contour of the breast region, (c) threshold image to identify glandular breast tissue and (d)

the resulting voxelized model. Reprinted from Angel, et al.61,62

.

134

8.2.D. Overview of Monte Carlo Simulation Techniques

Organ doses for this study were obtained using the Monte Carlo MDCT dosimetry

package described in Chapter 3. For this study, fixed tube current simulations were

performed for third generation, 64-slice MDCT scanner from each of the four major CT

scanner manufacturers, including: SOMATOM Sensation 64 (Siemens Medical

Solutions, Forcheim, Germany), the LightSpeed VCT (GE Healthcare, Waukesha, WI),

Brilliance CT 64 (Philips Medical Systems, Cleveland, Ohio), and Aquilion 64 (Toshiba

Medical Systems, Inc., Otawara-shi, Japan). Simulations of TCM exams were performed

for the Siemens Sensation 64 scanner only.

8.2.D.1. TCM Simulations

The methods described by Angel, et al.61,62

to model varying mAs values using

the Monte Carlo package described in Chapter 3 were employed to simulate organ doses

from TCM exams. The tube current values for each source location (specified by the

gantry angle and longitudinal position) were obtained from the raw projection data

acquired from the scanner for each patient. Each of these values was normalized by the

maximum mAs value for the scan. The MCNPX source weight of each simulated photon

was multiplied by the normalized mAs value corresponding to the randomly selected

starting location.

135

The normalization process described in Chapter 3 to convert the MCNPX tally

results to dose (in mGy/mAs) was repeated and then the result was multiplied by the

maximum mAs/rotation value to calculate absolute organ doses (in mGy).

8.2.E. Organ-Specific Size Coefficients (AO, BO)

The methods described in Section 6.2.E were utilized to generate AO and BO

coefficients for the chest and abdomen/pelvis patient cohorts. This method involves first

performing fixed tube current simulations to obtain CTDIvol normalized organ doses.

Then, an exponential regression analysis was performed to determine the dependence of

CTDIvol normalized organ dose with patient perimeter. These two steps are elaborated on

in the next two sections.

8.2.E.1. Fixed Tube Current Simulations

Monte Carlo simulations of fixed tube current exams were performed using each

of the patient models described in Section 8.2.C with all four of the scanners listed in

Section 8.2.D. For both the chest and abdomen/pelvis cohorts, 120 kVp helical scans

were simulated with a pitch of 1 using the largest available nominal collimation and the

bowtie filter appropriate for an adult. In order to model overscan, each fixed mAs

simulated scan started 1 cm superior to the top of the patient model and ended 1 cm

inferior to the bottom. Doses (in mGy/total mAs) were tallied for the lungs and glandular

breast tissue in the chest patient cohort and liver, kidney, and spleen in the

abdomen/pelvis cohort. In order to account for the differences in total mAs across

136

scanners due to the variation in the number of rotations necessary to traverse the scan

length, organ dose values were converted into units of mGy/mAs (where mAs refers to

mAs/rotation) by multiplying each mGy/total mAs value by the total number of rotations

used in the corresponding helical scan simulation (from mGy/total mAs to mGy/mAs) for

each patient.

8.2.E.2. Exponential Regression Analysis

Each of the simulated fixed tube current organ doses (in mGy/mAs) were

normalized by the CTDIvol (also in mGy/mAs) for the scanner on which they were

obtained. For each patient/organ combination, the average CTDIvol normalized dose

across scanners was calculated and denoted . Then, for each organ, an exponential

regression analysis was performed to obtain the relationship between and patient

perimeter. As defined in Chapter 6, this relationship is summarized by Equation 8.2:

Eq. 8.2

Unique AO and BO coefficients exist for each organ and scan region. The correlation

coefficient (R2) of the exponential fit was also obtained for each organ.

8.2.F. Accuracy of Organ Dose Estimates using CTDIvol,Avg mAs

The AO and BO coefficients generated using the voxelized patient models and all

MDCT four scanner models were used to estimate dose from the Siemens Sensation 64

scanner for the organs of interest in both the chest and abdomen/pelvis patient cohorts

137

with the CTDIvol reported by the scanner for each patient‘s TCM exam (i.e. CTDIvol,Avg

mAs) using Equation 8.3:

Eq. 8.3

where DP,O is the patient- and organ-specific dose.

Then, the percent error of each estimated dose was calculated with respect to the

dose values obtained with TCM simulations. For each patient cohort the root mean

square, minimum, and maximum of these percent errors across patients were determined

as summary statistics.

8.2.G. TCM Correction Factors for Organ Dose Estimates Using CTDIvol,Ref mAs

Organ dose estimates were calculated in a similar fashion as described above for

each patient but this time using the CTDIvol corresponding to the Quality Reference mAs

(i.e. CTDIvol,Ref mAs), as described by Equation 8.4:

Eq. 8.4

For each organ dose estimate, a TCM correction factor, denoted kP,O, was calculated as

the ratio of the simulated organ dose to the estimated dose obtained using Equation 8.4:

Eq. 8.5

TCM corrected dose estimates are thus calculated using Equation 8.6:

138

Eq. 8.6

8.2.G.1. Size Dependency of Correction Factors

To determine if a correlation exists between patient perimeter and kP,O factors

both linear and exponential regression analyses were performed for the chest and

abdomen/pelvis patient cohorts. The correlation coefficients (R2) for each type of

regression model (linear and exponential) were determined. Finally, the regression fit

parameters for the model with the best correlation were determined.

8.2.G.2. Validation of Correction Factors

A leave-one-out cross-validation analysis was performed to assess the accuracy of

organ doses estimated using Equation 8.6 with the patient-specific kP,O factors. The

following steps were carried out for each organ:

Step 1: Regression fit parameters were obtained as described in Section 8.2.G.1

using all but one patient model in the appropriate patient cohort (i.e. chest

cohort for lung and glandular breast, abdomen\pelvis cohort for liver,

spleen, and kidney)

Step 2: These parameters were used to calculate a kP,O value for the left-out

patient model using its perimeter.

Step 3: An organ dose estimate was determined for the left-out patient using

Equation 8.6

139

Step 4: The percent error of the estimated dose was calculated with respect to the

dose obtained with the corresponding TCM simulation.

Step 5: Steps 1-4 were repeated, leaving out a different patient each time until a

set of organ dose estimates and their associated errors s were obtained for

each patient.

Step 6: The root mean square, maximum, and minimum of the percent errors for

each organ were calculated as summary statistics across all patients in the

appropriate cohort.

8.3 Results

8.3.A. Fixed Scan Simulations and AO and BO Coefficients

The values of the average CTDIvol-normalized-organ doses from fixed scan

simulations across scanners ( ) are presented as a function of patient perimeter in

Figure 8.4 for the lung and glandular breast tissue (chest cohort) and in Figure 8.5 for the

liver, spleen, and kidney (abdomen/pelvis cohort). The exponential fit parameters

determined by exponential regression analyses performed to obtain size-coefficients (AO

and BO) are summarized in Table 8.1. This table also shows that correlation coefficients

(R2) for each organ.

140

Figure 8.4 (mean organ dose/CTDIvol across scanners) from fixed tube current scans

as a function of patient perimeter (in cm) for lung and glandular breast tissue. The

exponential regression curves for each organ are also shown.

Figure 8.5 (mean organ dose/CTDIvol across scanners) from fixed tube current scans

as a function of patient perimeter (in cm) for liver, spleen, and kidney. The exponential

regression curves for each organ are also shown.

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

85 95 105 115 125 135 145

CT

DI v

oln

orm

ali

zed

Org

an

Do

se f

rom

Fix

ed T

ub

e C

urr

ent

Sca

ns

Perimeter (cm)

Lung

Breast

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

2.3

75 85 95 105 115 125

CT

DI v

oln

orm

ali

zed

Org

an

Dose

fro

m

Fix

ed T

ub

e C

urr

ent

Sca

ns

Perimeter (cm)

Liver

Spleen

Kidney

141

Table 8.1 Results of the exponential regression analysis between from fixed tube

current scans and patient perimeter. For each organ the patient cohort, AO and BO

coefficients, and correlation coefficient (R2) is reported.

Organ Patient Cohort AO BO R2

Lung Female Chest 5.69 -0.0101 0.91

Glandular Breast Female Chest 4.28 -0.0102 0.92

Liver Abdomen/Pelvis 5.39 -0.0136 0.75

Spleen Abdomen/Pelvis 3.29 -0.0084 0.46

Kidney Abdomen/Pelvis 5.29 -0.0127 0.76

8.3.B. Results of Tube Current Modulation Exam Simulations

The organ doses resulting from detailed Monte Carlo simulations of exams

performed with TCM for each of the organs are reported in Figure 8.6 for the lung and

glandular breast tissue (chest cohort) and in Figure 8.7 for the liver, spleen, and kidney

(abdomen/pelvis cohort). For all organs there was an increase in organ dose with patient

perimeter, which was expected since the Quality Reference mAs was the same for

patients in each cohort.

142


chest exams performed with TCM as a function of perimeter in cm for lung and glandular

breast tissue.


abdomen/pelvis exams performed with TCM as a function of perimeter in cm for liver,

spleen, and kidney.

8.3.C. Organ Dose Estimates using CTDIvol,Avg mAs

10

15

20

25

30

35

85 95 105 115 125 135 145

Sim

ula

ted

Org

an

Do

se (

mG

y)

for

TC

M e

xa

ms

Perimeter (cm)

Glandular

Breast

Lung

12

16

20

24

28

75 85 95 105 115 125

Sim

ula

ted

Org

an

Dose

(m

Gy)

for

TC

M E

xam

s

Perimeter (cm)

Liver

Spleen

Kidney

143

The organ dose estimates calculated using Equation 8.3 with the CTDIvol,Avg mAs,

as described in Section 8.3.F, were obtained for each organ of interest in both patient

cohorts. As an example of these results, Figure 8.8A shows the estimates calculated for

lung tissue along with the doses obtained with TCM simulations as a function of patient

perimeter. Figure 8.8B is a plot of the percent difference between the estimated and

simulated lung dose for each patient. It can be seen that lung doses calculated with

CTDIvol,Avg mAs consistently overestimated the simulated lung doses for all patient models.

15

20

25

30

35

40

45

85 95 105 115 125 135 145

Lu

ng D

ose

(m

Gy)

Perimeter (cm)

Estimated

Lung Dose

Simulated

Lung Dose

A

144

Figure 8.8 A) Lung dose estimates calculated with CTDIvol,Avg mAs and lung doses from TCM

simulations. B) Percent error of lung dose estimates calculated with CTDIvol,Avg mAs with

respect to lung doses from TCM simulations.

Similar results were found for the glandular breast tissue. Organ dose estimates for the

liver, spleen, and kidney were almost always overestimates of the simulated doses;

however, the degree of overestimation was less than that of the chest organs and there

were a few cases in which the dose estimates were slightly less than the simulated doses.

Table 8.2 shows the summary statistics for the percent errors of the estimated doses

across patient models, including the root mean square, the maximum, and the minimum.

These results indicate that for some patients the estimated organ doses exactly matched

the simulated doses (minimum percent error of ~0%) but the maximum errors ranged

from 47% to 85% across organs. The root mean square percent errors across all patients

for different organs ranged from 23% to 41%.

0%

20%

40%

60%

80%

85 95 105 115 125 135 145

% e

rro

r o

f es

tim

ate

d l

un

g d

ose

Perimeter (cm)

B

145

Table 8.2 Summary statistics for the percent errors of organ dose estimates calculated with

CTDIvol,Avg mAs with respect to doses obtained from TCM simulations, including: root mean

square, minimum error, and maximum error across patients in appropriate cohort.

Organ Patient Cohort Percent Error

Root Mean Square Minimum Maximum

Lung Female Chest 36.5% 11.3% 69.1%

Glandular Breast Female Chest 41.6% 3.5% 84.9%

Liver Abdomen/Pelvis 25.9% 0.9% 59.8%

Spleen Abdomen/Pelvis 23.2% 1.5% 46.7%

Kidney Abdomen/Pelvis 25.2% 0.1% 62.4%

8.3.D. Correction factors for Organ Dose Estimates using CTDIvol,Ref mAs

Plots of the correction factors for organ doses estimated with CTDIvol,Ref mAs

values (kP,O) described in Section 8.2.G, calculated as the ratio of the simulated dose to

the estimated dose, as a function of patient size are shown for each organ in Figures 8.9

and 8.10.

146

Figure 8.9 kP,O as a function of patient perimeter (in cm) for lung and glandular breast

tissue. The linear regression curves for each organ are also shown.

Figure 8.10 kP,O as a function of patient perimeter (in cm) for liver, spleen, and kidney. The

linear regression curves for each organ are also shown.

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

85 95 105 115 125 135 145

Sim

lua

ted

Org

an

Do

se/E

stim

ate

d O

rga

n D

ose

Perimeter (cm)

Lung

Breast

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

75 85 95 105 115 125

Sim

ula

ted

Org

an

Do

se/E

stim

ate

d O

rga

n D

ose

Perimeter (cm)

Liver

Spleen

Kidney

147

The regression analysis demonstrated that the best correlation between kP,O

factors and perimeter was for a linear function. This indicates the feasibility of

calculating TCM correction factors for any patient based on their perimeter with Equation

8.7. The linear fit parameters for each organ (denoted CO and DO) are reported in Table

8.3 along with the linear correlation coefficient (R2).

Eq. 8.7

Table 8.3 Results of the linear regression analysis between kP,O and patient perimeter. For

each organ the patient cohort, CO and DO coefficients, and correlation coefficient (R2) is

reported

Organ Patient Cohort CO DO R2

Lung Female Chest 0.0120 -0.6119 0.84

Glandular Breast Female Chest 0.0150 -0.9263 0.88

Liver Abdomen/Pelvis 0.0150 -0.7763 0.88

Spleen Abdomen/Pelvis 0.0150 -0.7613 0.86

Kidney Abdomen/Pelvis 0.0165 -0.9113 0.91

The results of the leave-one-out cross-validation analysis discussed in Section

8.2.G.2 performed to assess the accuracy of dose estimates calculated with the CTDIvol,Ref

mAs and patient-specific kP,O factors obtained with Equation 8.7 are presented in Table 8.4.

Specifically, this table shows the summary statistics of the percent errors of the dose

estimation for the left-out patient, including the root mean square, maximum, and

minimum across all patient models.

148

Table 8.4 Summary statistics for the leave-one-out cross-validation analysis to quantify the

percent errors for estimated doses calculated using CTDIvol,Ref mAs and kP,O from Equation

8.7.

Organ Patient Cohort

Percent Error

Root Mean

Square Minimum Maximum

Lung Female Chest 14.0% 0.0% 27.7%

Glandular Breast Female Chest 16.2% 1.2% 40.4%

Liver Abdomen/Pelvis 8.7% 0.5% 25.0%

Spleen Abdomen/Pelvis 9.5% 0.2% 22.1%

Kidney Abdomen/Pelvis 8.0% 0.3% 20.8%

8.4 Discussion

This study was performed in order to investigate the feasibility of accurately

estimating organ doses from CT exams performed with TCM using the CTDIvol-to-organ

dose estimation method proposed in Chapter 6. As an initial step, this work was limited

to dose from Siemens Sensation 64 scanners. The fundamental dose estimation equation

presented in Chapter 6 (Equation 6.2) includes a CTDIvol term that is used along with

patient-specific CTDIvol-to-organ dose coefficients to estimate scanner-specific organ

doses. Previous work has demonstrated that, for fixed tube current scans, using the

CTDIvol with Equation 6.2 results in reasonably accurate organ dose estimates. The

purpose of this work was to investigate the utility of using this dose estimation technique

for TCM exams by: a) assessing the accuracy of organ doses calculated using the CTDIvol

based on the average mAs of the TCM exam (CTDIvol,Avg mAs), and, b) extending the dose

estimation method to include a patient-specific correction term for doses obtained using

the CTDIvol corresponding to the Quality Reference mAs (CTDIvol,Ref mAs).

149

The organ dose estimates resulting from calculations with the CTDIvol,Avg mAs were

shown, on average, to have considerable variation from the organ doses obtained using

TCM simulations. The results in Table 8.2 demonstrate that for each organ of interest

there was a wide range of estimation errors across the patient models. For all organs,

except the lung, the minimum percent error was less than 5% (the minimum percent error

for lung was ~11%) which indicates that the dose estimates calculated with CTDIvol,Avg

mAs were very accurate for some patient models. However, the maximum errors ranged

from ~47% (for spleen) to ~85% (for glandular breast). As an overall metric of accuracy,

the root mean square of the percent errors was calculated across all the patient models for

each organ. These results suggest that estimating organ doses with the CTDIvol,Avg mAs

using Equation 8.3 can produce patient-specific dose estimates with average accuracies

across patients ranging from ~23% (for spleen) to ~42% (for glandular breast), but, as

seen by the large maximum and small minimum errors, the error bars for these estimates

are relatively broad.

Next, an alternative approach was proposed in which the organ dose estimation

technique was extended to include patient-specific factors to correct dose estimates

calculated with CTDIvol,Ref mAs values. The CTDIvol,Ref mAs metric was chosen because for

a fixed Quality Reference mAs, the TCM function is primarily governed by the size of

the patient. As a result, the offset of a dose estimated using the Quality Reference mAs

value relative to the actual dose should be a function of patient size; therefore, a

relationship between dose estimate correction factors and patient size should exist. The

150

data in Figures 8.8 and 8.9 illustrate that the correction factors (kP,O), defined as the ratio

of simulated doses to doses estimated with only the CTDIvol,Ref mAs, did have a strong

linear correlation with patient perimeter. Each organ had a specific set of linear fit

parameters as shown in Table 8.3 that can be used to calculate a patient-specific

correction factor for any patient using Equation 8.6. The correlation coefficients for the

linear regression analyses ranged between 0.84 and 0.91 across organs, indicating that

this model has strong predictive capabilities.

The accuracy of patient-specific organ dose estimates from TCM exams using

each patient‘s CTDIvol,Ref mAs and kP,O value was evaluated using a leave-one-out cross-

validation analysis. The summary statistics reported in Table 8.4 illustrate that the root

mean square across patient models of the estimation errors ranged between 8.0% and

16.2%. The maximum errors of these dose estimates were on the order of ~25% for

almost all organs (glandular breast had a maximum estimation error of 40% while the rest

were less than 28%). Thus, this study demonstrates the feasibility of obtaining reasonably

accurate organ dose estimates from TCM exams with only a knowledge of the patient‘s

perimeter, CTDIvol,Ref mAs, and organ dose estimation coefficients (AO, BO, CO,,and DO)

using Equation 8.8:

) Eq. 8.8

The proposed method to estimate dose from a TCM scan performed with the Sensation

64 for any patient is illustrated in Figure 8.11.

151

Figure 8.11 The proposed method to estimate patient-, scanner-, and exam-specific organ

dose using the size coefficients (AO, BO), TCM correction factor coefficients (CO, DO)

patient perimeter (in cm), and the CTDIvol corresponding to the Quality Reference mAs.

This feasibility study was performed using two cohorts of patient models created

from actual image data of patients scanned on the Siemens Sensation 64 scanners at

UCLA. The chest cohorts included twenty female patients with lung and glandular breast

tissue and the abdomen/pelvis cohort for this study included forty patient models (23

females and 17 males). These models were all created by first segmenting organs of

interest and assigning them to the ICRU 44 composition of body definitions and then

directly mapping all remaining voxels to a predefined anatomical tissue type with a

specific composition and density based on their HU values. While this approach made it

possible to include more patient models than previous Monte Carlo studies, only a limited

set of organs could be contoured. The organs used for this study were selected because

Patient-,

Scanner-,

Exam-specific

Organ Dose

(mGy)

Size Coefficients

(AO,BO)

Patient Perimeter

(p)

Exam-specific

CTDIvol

(for Quality

Reference mAs)

Patient-specific

CTDIvol-to-organ

dose conversion

coefficient

TCM Correction

Coefficients

(CO,DO)

Patient Perimeter

(p)

Patient-specific

TCM correction

factor

152

they were the simplest to contour using manual and semi-automated segmentation

methods. In order to obtain organ dose estimation coefficients for other radiosensitive

organs it will be necessary to develop additional tools for quickly and accurately

contouring organs on large patient datasets.

The coefficients derived for the abdomen/pelvis models in this study had a

much weaker correlation with patient perimeter than the data reported in Chapter 6. This

is probably partially due to the fact that the original study used only eight patient models

(the GSF Family of Voxelized Models) that were built using organ segmentation

techniques that included a number of approximations, such as thresholding and

smoothing techniques. The abdomen/pelvis patients used for this study represented a

wider spectrum of patient characteristics (size, habitus) and therefore resulted in a patient

cohort with increased anatomical variation compared to the GSF models. However,

another major confounding factor was the fact that the abdomen/pelvis CT exams were

all performed with intravenous iodine contrast while some also had oral contrast. The

stomach and large intestine in patients scanned with oral contrast typically had much

higher HU values than those without and therefore these organs in the corresponding

patient models had much higher attenuation properties. Future studies will be performed

to determine if the variation of contrast distribution in patients with similar perimeters

leads to significant differences in their organ dose values. It should be noted that the

results of this study demonstrated that, even with the contrast effect, the dose estimation

method still produced values with reasonable accuracy.

153

Since this work was only meant to show feasibility of estimating organ doses

from TCM exams and TCM algorithms vary across scanner manufacturers this study only

focused on the Siemens Sensation 64 scanner. It was demonstrated that the CTDIvol

corresponding to the Quality Reference mAs was a sufficient input into the dose

estimation equation to produce reasonably accurate organ dose estimates. The Quality

Reference mAs is a Siemens-specific concept and does not apply to TCM exams

performed on scanners from other manufacturers. However, each scanner requires some

metric to determine the overall level of TCM. Using methods similar to those presented

in this study, it should be possible to determine some type of CTDIvol variant and

scanner-specific correction factor parameters (such as the CO and DO coefficients) to

estimate TCM doses for scanners of each manufacturer.

154

Chapter 9 Advanced MDCT Monte Carlo Dosimetry Validation Methods

9.1 Introduction

The work presented in the previous chapters illustrates the power of Monte Carlo

radiation transport codes to investigate the radiation dose delivered by CT scanners to

patient models. In fact, this research tool has become the preferred method to investigate

dose distributions in patient models and phantoms from different scanners and scan

protocols69

. A number of different groups have developed Monte Carlo simulation

packages and the disparities between the different packages range from fundamental

radiation transport techniques to advanced aspects of modeling CT scanners (i.e. x-ray

output, filtration, source motion, etc.)29-37

.

As a result of the discrepancies between different simulation packages, the only

way to assess their accuracy is through the results of the specific benchmark experiments

used to validate each individual code. The level of detail and robustness of the validation

methods reported in the literature vary greatly. A fairly common approach is to compare

simulated results with simple physical measurements (such as CTDI values, which only

require a homogenous cylindrical phantom and a single rotation). While this is a

reasonable first step, these types of benchmarks do not demonstrate the precision of the

techniques used to model longitudinal source motion (i.e. helical scans with various pitch

values) or the ability to accurately simulate doses to more complex geometries made up

of different types of material.

155

More sophisticated benchmark measurements have been described by various

groups in order to further prove the accuracy of their simulations. For example, a method

to validate radiation transport codes in the diagnostic energy range was suggested by

Nikolopoulos, et al.70

in which the results of simple simulations are compared to those

obtained using MCNP, which he considered as the gold standard. Boone, et al.37

validated their Monte Carlo code (SIERRA) by comparing to a larger set of data obtained

from other Monte Carlo codes and from physical measurements. Their measured

parameters included depth dose curves, lateral energy scattering profiles, scatter to

primary ratios, normalized glandular doses, angular scattering distributions, and CTDI

values. A set of CT-specific benchmarks were described by Deak, et al.71

that utilized

thermoluminescent dosimeters (TLD) to measure continuous longitudinal dose profiles

and doses measured in-anthropomorphic phantom.

While it is clear that these more complex validation metrics are more informative

than the simple phantom measurements described above, there are several issues that

must be taken into account. The use of a relative benchmark, such as comparing the

results of one Monte Carlo code with another, does not provide sufficient validation of

the accuracy of a simulation since neither code is being compared with an absolute

physical value.

In order to overcome this problem for validating Monte Carlo CT dosimetry

packages it is necessary to obtain absolute dose measurements. Air ionization chambers

are the only available dosimeters without large energy dependences in the diagnostic

156

energy range. However, the use of various solid state type detectors, TLD‘s, metal–

oxide–semiconductor field-effect transistors (MOSFET‘s) and optically stimulated

luminescence (OSL) detectors are common in CT dosimetry studies. Methods to calibrate

these devices with ionization chambers are typically described in the literature; however,

this is usually done in air since ionization chambers usually do not fit in the measurement

set up (such as in small holes in an anthropomorphic phantom). Since the beam is

hardened by the phantom and its average energy increases before interacting with the

detector. Due to the large energy dependence of these detectors, even a slight energy shift

due to beam hardening can negate the calibration factors derived in air. This problem is

illustrated in Appendix B which describes a study to examine the energy dependence of a

small volume, solid state detector.

In order to address the limitations of typically used validation metrics described

above, a set of more advanced benchmarking techniques will be described in this chapter.

Section 9.2 will describe the work of AAPM Task Group 195. The UCLA CT dose

research team joined this group when it formed in 2010 in order to develop a set of

standardized simulation cases representative of typical diagnostic imaging research

problems. The results of these cases will be obtained with several, common Monte Carlo

radiation transport packages and published in order to serve as a reference set for

comparison by researchers developing their own transport algorithms. Next, a set of

advanced benchmarks will be proposed to validate the accuracy of CT-specific x-ray

source information (i.e. energy spectrum and filtration) that are based on ionization

157

chamber measurements. Finally, the use of a dose measurement made using a small

volume ionization chamber on the surface of a heterogeneous thorax phantom from a

helical exam will be assessed as a more advanced benchmark measurement.

9.2 AAPM Task Group 195

9.2.A Purpose of AAPM Task Group 195

AAPM Task Group 195 was formed in order to develop several Monte Carlo

simulation specifications that model common projection x-ray imaging tasks

(radiography, mammography, and CT) and to publish a set of corresponding results to be

used as a reference data set. These results can be used by researchers who are learning

how to implement Monte Carlo simulations or needing to validate their own Monte Carlo

simulations. The specifications for each of these simulations will be implemented in five

different Monte Carlo software packages: EGSnrc72

, Geant473

, MCNPX33,44

, Penelope74

,

and Sierra37

. The simulation conditions and the results for all five Monte Carlo codes will

be published, in detail, in the Task Group report and will be made available online in the

AAPM website. Once published, the submission of results from scientists outside the

Task Group will be encouraged to increase the power of the published values.

Additionally, this work may be extended to include other non-projection x-ray diagnostic

imaging modalities, such as nuclear medicine imaging.

9.2.B. AAPM TG 195 Reference Cases

158

The first aim of this Task Group was to define a set of diagnostic imaging

research problems that are commonly investigated using Monte Carlo simulation

techniques. The resulting references ―cases‖ include:

Case 1. X-ray Production. A pencil beam of electrons is emitted towards a target

consisting of tungsten (as used for general radiography and CT), molybdenum,

or rhodium (as used for mammography) with a specific anode angle. The x-ray

fluence and x-ray energy distribution emitted from the target, normal to the

incident electrons, will be tallied.

Case 2. Half Value Layer (HVL) and Quarter Value Layer (QVL) simulations. Both

monoenergetic and polyenergetic (spectrum) x-ray beams are emitted towards a

slab of aluminum with thicknesses equal to the corresponding theoretical HVL

and QVL thicknesses. The exposure (or air kerma) before and after the slab is

tallied and their ratio is calculated.

Case 3. Radiography Dosimetry. Monoenergetic and polyenergetic (spectrum) x-ray

cone beams are emitted towards a simple geometry representing the body (e.g. an

elliptical tube). The energy deposited in the body is tallied.

Case 4. Radiography Scatter Analysis. Monoenergetic and polyenergetic (spectrum) x-

ray cone beams are emitted towards a simple geometry representing the body

(e.g. an elliptical tube), as in Case 3. The x-ray energy scatter-to-primary ratio at

various locations at a detector behind the body is tallied.

159

Case 5. Mammography Dosimetry. Monoenergetic and polyenergetic (spectrum) x-ray

cone beams are emitted towards a simple geometry representing the breast with

varying size and glandular fractions. The energy deposited in the whole breast

and the normalized glandular dose is tallied.

Case 6. Mammography Scatter Analysis. Monoenergetic and polyenergetic (spectrum) x-

ray cone beams are emitted towards a simple geometry representing the breast

with varying size and glandular fractions, as in Case 5. The x-ray energy scatter-

to-primary ratio at various locations at a detector behind the body is tallied.

Case 7. CT Dosimetry in Simple Volumes. Monoenergetic and polyenergetic (spectrum)

x-ray fan beams are emitted towards a cylinder representing an infinitely long

CTDI phantom. Both a stationary source and moving source (single rotation) will

be simulated and the energy deposited in thin axial slabs and in CTDI-like rods

will be tallied.

Case 8. CT Dosimetry in Voxelized Patient Models. Monoenergetic and polyenergetic

(spectrum) x-ray fan beams are emitted towards a voxelized patient model. Dose

from a rotating source is tallied in various voxels (or combinations of voxels) in

the patient model.

A set of initial implementation specifications for each of these cases is currently

being developed and tested by members of AAPM Task Group 195. The specifications

include: full geometry details, material compositions, particle energies, spectral

160

definitions, and tallying details. The spectra chosen for this work consists of the various

beam quality reference spectra published by International Electrotechnical Commission

(IEC)75

and the Institute of Physics and Engineering in Medicine (IPEM)76

. These spectra

were designed in order to achieve a specific combination of peak voltage (kVp) and

HVL.

This dissertation will focus only on the HVL simulations (Case 2) and CT

Dosimetry in Simple Volumes (Case 7). The patient model description for the CT

Dosimetry in Voxelized Patients (Case 8) is still under development and is not yet ready

for initial testing. The detailed specifications for Cases 2 and 7 as well as the results

obtained with MCNPX simulations will be described in detail in the next two sections.

9.2.C. Half Value Later and Quarter Value Layer Simulations

9.2.C.1. Introduction

The purpose of this test case is to compare the HVL and QVL of monoenergetic

and polyenergetic radiation beams obtained using Monte Carlo simulations with

theoretical HVL and QVL values. This will be done using both monoenergetic and

polyenergetic beams.

HVL and QVL are defined as the thicknesses of aluminum necessary to reduce

the intensity of a radiation beam by ½ and ¼, respectively. In order to derive theoretical

HVL and QVL values for monoenergetic beams the standard assumption of exponential

attenuation is applied:

161

Eq. 9.1

where I is the intensity of the beam after passing through an attenuating material with

some thickness (tm), Io is the initial intensity of the beam and μm,E is the material- and

energy-dependent linear attenuation coefficient. The HVL is the value of aluminum

necessary to reduce the intensity of a beam by ½ (so tAl = HVL when I = ½ Io).

Substituting these equalities and solving for HVL gives:

Eq. 9.2

In the same manner the QVL for a monoenergetic beam with energy E can be derived as:

Eq. 9.3

In order to derive an expression to obtain the HVL and QVL values for a

polyenergetic beam we start by use air kerma as a metric for intensity. The air kerma

from a polyenergetic beam is defined as:

Eq. 9.4

where ΦE is the number of photons (fluence) with energy E located at the point of interest

and (μen/ρ)Air,E is the energy-dependent mass energy absorption coefficient for air. The

photon flux obeys the same exponential attenuation described in Equation 9.1 (i.e.

), so the ratio of air kerma without and with an aluminum filter is given by:

162

Eq. 9.5

The HVL and QVL are defined as the values of tAl necessary for Equation 9.5 to be equal

to ½ or ¼, respectively. It is not possible to explicitly solve for tAl in Equation 9.5,

however, iterative numerical methods can be used to find the appropriate values of tAl for

calculating HVL and QVL.

9.2.C.2. Methods

This work used both monoenergetic beams with energies of 30 keV and 100 keV

and polyenergetic beams meant to represent those used for mammography and

radiography. Specifically, the 30 kVp and 100 kVp polyenergetic beams were modeled

using the x-ray spectra defined by the IEC Publication 6126775

and IPEM Report 7876

(the RQR-M3 spectrum was used for the 30 kVp beam and the RQR-8 spectrum for the

100 kVp beam). Equations 9.2 and 9.3 were used to calculate the theoretical HVL and

QVL values for the monoenergetic beams. The HVL and QVL values for the

polyenergetic beams were obtained by iteratively finding the appropriate values of tAl in

Equation 9.5 using the goal seek function of Microsoft Excel 2007.

MCNPX simulations were performed using standard input files and source

definitions to model a cone beam radiation source. Each simulation consisted of a cone

beam emanating in the –z direction from a source located at the center of the simulation

geometry (x=0, y=0, z=0). The F2 tally type was used to tally the photon fluence in 0.5

163

keV energy bins ranging from 0 keV to 120 keV with a 0.5 keV across a circle with a

diameter of 10 mm located 1000 mm from the source (centered at x=0, y=0, z=-1000

mm). For monoenergetic simulations only photons with the source energy were tallied.

The cone beam angle was chosen that the cone diameter at the detector was also 10 mm.

Simulations were first performed with no aluminum present. Then, for each beam

described above, simulations were performed with an aluminum slab located between the

source and detector. The aluminum filter was modeled using a right circular cylinder

(RCC) MCNPX macrobody. The top face of the slab was located 100 mm from the

source and the thickness was equal to the theoretical HVL or QVL corresponding to the

specific beam being simulated. Figure 9.1 is a diagram of the simulation geometry. The

relatively large distance between source and detector was chosen in order to approximate

narrow beam geometry in which the contribution of scattered photons to the total kerma

is minimized. The number of simulated photons (NPS) for was 1.0 x 109 for all

monoenergetic simulations and 2.0 x 109 for all polyenergetic simulations in order to

ensure that the statistical uncertainty in any given tally was less than 1%.

164

Figure 9.1 Diagram of the simulation geometry used to simulate HVL and QVL

measurements as defined by Task Group 195.

Since MCNPX reports fluence values as the number of particles per number of

simulated photons, the fluence/NPS values in each energy bin were multiplied by the

NPS used for the given simulation. The air kerma in the detector circle was calculated

offline by first multiplying the fluence in each energy bin by the energy of the bin and the

energy-dependent mass energy absorption coefficient. Next, the total air kerma was

calculated as the sum of the air kerma from each energy bin (Equation 9.4). Then, the

ratio of simulated air kerma without aluminum to air kerma with kerma was obtained. In

theory, this ratio should be equal to 0.5 for HVL simulations and 0.25 for QVL

simulations, so the percent error of each simulation was calculated. It should be noted

165

that since a ratio was being obtained and the NPS for simulations of a given beam type

were the same, it was not necessary to multiply the tally results by NPS. However, since

it is feasible to perform simulations with and without aluminum using different NPS

values, care must be taken to ensure the values are properly normalized before obtaining

their ratio.

9.2.C.3. Results

Tables 9.1 and 9.2 report the theoretical HVL and QVL of the monoenergetic and

polyenergetic beams calculated using Equations 9.1-9.3. These values were used as the

thicknesses of aluminum for the HVL and QVL simulations, as described in Section

9.2.C.2.

Table 9.1 Theoretical HVL and QVL values for monoenergetic photon beams.

Photon Energy (keV) Theoretical HVL (mm Al) Theoretical QVL (mm Al)

30 2.277 4.554

100 15.07 30.14

166

Table 9.2. Theoretical HVL and QVL values for polyenergetic photon beams. The kVp,

tube target material, and tube filtration material of the IEC beam quality reference

spectrum is also listed.

kVp Tube Target

Material

Tube Filtration

Material

Theoretical

HVL (mm Al)

Theoretical

QVL (mm Al)

30 Molybdenum Molybdenum 0.323 0.735

100 Tungsten Aluminum 3.958 9.832

The total air kerma for each beam type, with and without the aluminum filter, is

listed in Table 9.3 for monoenergetic beams and Table 9.4 for polyenergetic beams.

These tables also include the ratio of air kerma with no aluminum to air kerma with

aluminum. The percent error relative to the theoretical ratio (0.5 for HVL and 0.25 for

QVL) is reported in the last column of these tables.

Table 9.3 Results of HVL and QVL simulations for monoenergetic beams including the

energy, air kerma with and without the Al filter, their ratio, and percent error from the

theoretical ratio.

Energy Air kerma without

Al (keV/g)

Simulation

Type

Air kerma with Al

(keV/g) Ratio

Percent

Error

30 1.12 x 105

HVL 5.58 x 104 0.4997 -0.06%

QVL 2.79 x 104

0.2497 -0.10%

100 5.71 x 104 HVL 2.85 x 10

4 0.5000 0.00%

QVL 1.43 x 104 0.2501 0.02%

Table 9.4 Results of HVL and QVL simulations for polyenergetic beams including the kVp,

air kerma with and without the Al filter, their ratio, and percent error from the theoretical

ratio.

kVp Air kerma without

Al (keV/g)

Simulation

Type

Air kerma with Al

(keV/g) Ratio

Percent

Error

30 1.79 x 105

HVL 9.31 x 104

0.5191 3.82%

QVL 4.71 x 104

0.2627 5.08%

100 3.37 x 104

HVL 1.69 x 104 0.5012 0.23%

QVL 8.49 x 103 0.2515 0.60%

9.2.C.4. Discussion

167

The purpose of this exercise was to perform HVL and QVL simulations using the

MCNPX radiation transport code in order to derive a set of results that will be included in

the AAPM Task Group 195 report. The theoretical HVL and QVL values for the specific

monoenergetic and IEC polyenergetic beams described in Section 9.2.C.2 are well

established. These values are reported in Tables 9.1 and 9.2. For each type of beam,

simulations were first performed without aluminum present in order to determine the air

kerma from the unfiltered beam. Then, simulations were performed with an aluminum

filter with a thickness corresponding to the HVL or QVL corresponding to each beam.

These simulations utilized narrow beam geometry in order to minimize the detection of

scattered photons from the filter. Under these conditions, the simulated ratio of air kerma

without aluminum to air kerma with the aluminum should be 0.5 when the aluminum

thickness was equal to the theoretical HVL and 0.25 when the aluminum thickness was

equal to the QVL.

The absolute values of the percent errors of the simulated air kerma ratios, relative

to the theoretical ratios, were all ≤ ~5% for simulations performed with MCNPX. In fact,

except for the 30 kVp HVL simulations, the percent errors were all less than 0.6%. These

results illustrate that a validated Monte Carlo transport package should be able to produce

HVL and QVL values to within at least 5% of the theoretical values.

9.2.D. CT Dosimetry in Simple Volumes

168

In order to provide Monte Carlo CT researchers with more modality-specific

validation cases a pair of increasingly complex reference CT simulations were developed.

The first involves tallying dose in a simple CTDI-like homogenous phantom.

9.2.D.1. Introduction

The purpose of this CT-specific validation case is to verify the implementation of

a source rotating about an isocenter and to create a reference set of simulation results for

the energy deposited within a long cylindrical phantom. Two simulations types were

included in this case description. The first involves tallying the dose from a fixed-source

position to thin axial sections of the CTDI-like phantom. The second involves tallying the

dose to CTDI-like rods in the cylindrical phantom from a rotating source.

9.2.D.2. Methods

The CT simulations described in this section were performed using the MCNPX

radiation transport package and the source file modifications used to model CT scanners

described in Chapter 3. The generic virtual CT scanner is not meant to represent any

actual commercial CT scanner. Simulations were performed using both monoenergetic

and polyenergetic radiation beams. The spectrum of the polyenergetic beam was defined

as the RQR-9 IEC beam quality reference beam. This spectrum is characterized by a tube

voltage of 120 kVp, aluminum target material with an 11 degree anode angle, a mean

energy of 56.4 keV, and an HVL of 5.00 mm Al. Monoenergetic simulations were

169

performed using a 56.4 keV beam, the mean energy of the 120 kVp spectrum. No extra

filtration (including bowtie filter) was specified for the virtual CT scanner.

The geometry description of the virtual scanner included the source to isocenter

distance (i.e. rotation radius), fan-angle, and longitudinal beam width. The fan-beam was

chosen so that it exactly irradiated the diameter of phantom (see Figure 9.2). Table 9.5

lists the specifications of the virtual scanner as defined by AAPM Task Group 195:

Table 9.5 Design specifications of the virtual scanner as defined by AAPM Task Group 195.

Parameter Units Value

Source to isocenter distance mm 600

Fan-angle deg 14.94

Narrow slice thickness mm 10

Wide slice thickness mm 80

Monoenergetic beam energy keV 56.4

Polyenergetic beam energy kVp 120

170

Figure 9.2 Diagram of CTDI-like phantom simulation as defined by AAPM Task Group

195.

All simulations involved tallying dose within a CTDI-like phantom that consisted

of a 32 cm diameter cylinder with a length of 300 cm (this length was chosen to

approximate an infinitely long cylinder). The phantom is completely homogenous and

made of PMMA. The CTDI-like phantom was centered at the isocenter of the virtual CT

scanner geometry, so that the axial center coincided with the rotational axis and the

longitudinal center was equal to the longitudinal position of the source (these simulations

do not include longitudinal source motion as in a helical scan). As shown in Figure 9.3,

the phantom includes two 10 cm long CTDI rod-like regions with a diameter of 1 cm

171

located at either the axial center or periphery (1 cm from outer edge of the phantom). For

simplicity, the rods also consist of PMMA so the entire phantom volume is homogenous

in composition. The first projection (angle 0) was defined as the line through the center of

the two CTDI rod-like regions, with the periphery cylinder closest to the source.

Figure 9.3 Diagram of CTDI-like phantom. Note the two CTDI rod-like inserts and the first

projection angle.

Test 1: Longitudinal Beam Width Model.

Test 1 was created as a benchmark to validate the source geometry model,

including the fan angle and beam width. Simulations were performed to tally the energy

deposited in four contiguous cylindrical 1 cm long segments (i.e. axial slices) near the

longitudinal center of the CTDI-like phantom (the CTDI rod-like segments were ignored

for these simulations). A diagram of Test 1‘s tally geometry is shown in Figure 9.4. The

longitudinal (z-axis) boundaries of the axial segments were -5 mm to 5 mm, 5 mm to 15

mm, 15 mm to 25 mm, and 25 mm to 35 mm, respectively. Because of the symmetry of

172

the problem it was only necessary to perform these simulations with a non-rotating

source, fixed at the first projection angle.

Simulations were performed using the narrow (10 mm) and the thick longitudinal

beam width (80 mm). Simulations for both beam widths were performed using both the

monoenergetic and polyenergetic radiation beams. In all, four simulations were

performed (i.e., 10 mm/monoenergetic, 80 mm/monoenergetic, 10 mm/polyenergetic, and

80 mm/polyenergetic). For each simulation, the energy flux in each segment was tallied

using the MCNPX *F4 tally type. The total kerma was obtained by multiplying the flux

for each photon by the mass-energy absorption coefficient for PMMA using the MCNPX

DE and DF dose multiplier cards. 1.0 x 108 photons were simulated in each case in order

to ensure less than 1% statistical uncertainty in each tally region. The MCNPX results

were in units of MeV/kg/NPS.

Figure 9.4 Diagram of the contiguous axial tally regions for the Test 1. For these simulations

the source is fixed and located at the longitudinal center of the phantom (z=0).

Test 2: Rotation About Isocenter Model.

173

Test 2 was performed to verify the technique used to model the source‘s rotation

about isocenter. This was done by performing a series of fixed tube simulations at various

gantry angles to approximate a single rotation of the source in the axial plane

corresponding to the longitudinal center (z=0). Since the purpose of these simulations

was to verify the rotational accuracy of the CT model it was only necessary to simulate

one combination of beam energy and beam width. The 120 kVp polyenergetic beam with

a beam collimation of 10 mm was selected for these simulations. In theory, the results at

the center rod should be constant for all angles while the tallies at the peripheral rod

should have a sinusoidal dependence on the gantry angle.

In all, eighteen different simulations were performed, varying the gantry angle in

increments of 20 degrees (i.e. 0°, 20°, ..., 320°, 340°). For each simulation, kerma

deposited in the center and peripheral PMMA CTDI rod-like structures was obtained

using the *F4 MCNPX tally type with the DE and DF dose multiplier cards, as described

for the Test 1. For each simulation, 1.0 x 107 photons were used in order to ensure less

than 1% statistical uncertainty in each tally region and the results MCNPX results were in

units of MeV/kg/NPS.

Test 2 was specifically designed to validate the rotation about isocenter for

simulation packages that model source motion using a series of fixed source positions.

However, it is also common to model the source rotation by randomly selecting source

positions from a continuous function describing the source trajectory (i.e. a circle in the

174

case of a single rotation). In order for the results of the Test 2 to be useful for validating

packages that use random sampling of continuous source trajectory functions, the

specified number of discrete source simulations at different angles must be large enough

to approximate a continuous rotation. As an initial investigation of this issue, the sum of

the kerma resulting from eighteen fixed source simulations with twenty degree intervals

will be compared to a continuous rotation simulation performed with the UCLA Monte

Carlo CT package. In theory, the total kerma (Ktot) is equal to the integral of kerma as a

function of gantry angle over one rotation.

Eq. 9.6

The total kerma can be approximated by summing the individual kerma from a fixed

source position (K from a finite number of gantry angles (specifically, with twenty degree

intervals):

Eq. 9.7

Thus, the kerma from the full rotation simulation will be compared to the average of the

kerma values from the fixed gantry angles ranging from 0 to 340 degrees.

9.2.D.3. Results

The results of the Test 1 simulations to obtain kerma in units of MeV/kg/NPS to

the four contiguous axial segments of the CTDI-like phantom from a fixed source are

175

presented in Table 9.6 for the monoenergetic beam s and Table 9.7 for the polyenergetic

beam. These results are also displayed in column plots in Figures 9.5 and 9.6. These

values are all normalized by the number of simulated photons (NPS) and thus do not

account for the variation in total photon fluence for different beam widths.


monoenergetic beam in keV/kg/NPS for the narrow and wide beam widths.

Beam Width -5mm to 5mm 5mm to 1.5mm 1.5mm to 2.5mm 2.5mm to 3.5mm

10 mm 1.20E-05 2.66E-06 1.82E-06 1.37E-06

80 mm 3.49E-06 3.44E-06 3.28E-06 2.65E-06


monoenergetic beam in keV/kg/NPS for the narrow and wide beam widths.

0.00E+00

2.00E-06

4.00E-06

6.00E-06

8.00E-06

1.00E-05

1.20E-05

1.40E-05

-5mm to

5mm

5mm to

1.5mm

1.5mm to

2.5mm

2.5mm to

3.5mm

MeV

/Kg

/NP

S

Monoenergetic Beam

10mm

80mm

176


polyenergetic beam in keV/kg/NPS for the narrow and wide beam widths.

Beam Width -5mm to 5mm 5mm to 1.5mm 1.5mm to 2.5mm 2.5mm to 3.5mm

10 mm 1.36E-05 2.68E-06 1.77E-06 1.31E-06

80 mm 3.71E-06 3.66E-06 3.50E-06 2.79E-06


polyenergetic beam in keV/kg/NPS for the narrow and wide beam widths.

The results of the Test 2 simulations to obtain kerma in the center and peripheral

rods from the series of fixed source positions at angular increments of twenty degree

ranging from 0 to 360 degrees are presented in Figure 9.7 on a logarithmic scale. The

kerma values to the central rod had a Coefficient of Variation (standard deviation/mean)

of 0.52%, indicating that this value has almost no dependence on the gantry angle. The

plot in Figure 9.7 indicates a strong sinusoidal relationship between the kerma to the

peripheral rod and the fixed source gantry angle. The actual kerma values for each gantry

angle are reported in units of MeV/kG/NPS in Table 9.8.

0.00E+00

2.00E-06

4.00E-06

6.00E-06

8.00E-06

1.00E-05

1.20E-05

1.40E-05

1.60E-05

-5mm to

5mm

5mm to

1.5mm

1.5mm to

2.5mm

2.5mm to

3.5mm

MeV

/Kg

/NP

S

Polyenergetic Beam

10mm

80mm

177

Figure 9.7 MCNPX simulated kerma tallied in the center and peripheral rods from fixed

source positions at gantry angles ranging from 0 to 360 degrees on a logarithmic scale

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

0 40 80 120 160 200 240 280 320 360

ker

ma

(M

eV/k

g/N

PS

)

Fixed gantry angle (degrees)

Center

Periphery

178

Table 9.8 MCNPX simulated kerma values for the center and peripheral CTDI rod-like

volume from each gantry angle in units of MeV/kG/NPS. The average kerma from angles 0

to 360 is reported for the peripheral rod.

Fixed Gantry Angle Center Rod Peripheral Rod

0 1.217 x 10-6

1.222 x 10-5

20 1.226 x 10-6

1.162 x 10-5

40 1.221 x 10-6

9.845 x 10-6

60 1.213 x 10-6

6.730 x 10-6

80 1.212 x 10-6

2.499 x 10-6

100 1.219 x 10-6

6.931 x 10-7

120 1.209 x 10-6

2.373 x 10-7

140 1.220 x 10-6

1.110 x 10-7

160 1.224 x 10-6

7.167 x 10-8

180 1.231 x 10-6

5.938 x 10-8

200 1.226 x 10-6

6.922 x 10-8

220 1.224 x 10-6

1.108 x 10-7

240 1.222 x 10-6

2.334 x 10-7

260 1.215 x 10-6

6.992 x 10-7

280 1.217 x 10-6

2.506 x 10-6

300 1.224 x 10-6

6.722 x 10-6

320 1.213 x 10-6

9.855 x 10-6

340 1.233 x 10-6

1.158 x 10-5

360 1.217 x 10-6

1.222 x 10-5

1.220 x 10-6

4.214 x 10-6

The average kerma from the gantry angles ranging from 0 to 340 degrees is also

reported in Table 9.8. The kerma from a single continuous rotation simulation was 1.220

x 10-6

MeV/kg/NPS for the central rod and 4.211 x 10-6

MeV/kg/NPS peripheral rod. The

percent difference between the average kerma from the discrete source position

simulations and the continuous rotation simulations was 0.054 % for the center rod

0.082% and for the peripheral rod. Again, these errors are both within the reported

statistical error of the MCNPX simulations.

179

9.2.D.4. Discussion

The goal of this set of simulations was to provide researchers a tool to validate

their methods of modeling a rotating fan beam with a given beam width, similar to those

used by CT scanners. Two types of simulations were presented that both utilized a simple

homogenous cylindrical phantom. The results included in Section 9.2.D.3 were all

obtained with the MCNPX transport package and will be submitted for inclusion in the

benchmark data sets that will be in the Task Group 195 report.

The purpose of the Test 1 was to validate the methods used to model the

longitudinal beam profile and did not include source motion. Simulations were performed

to obtain kerma to contiguous axial segments at the center of the CTDI-like phantom.

Results obtained with MCNPX appear to behave as expected. The narrow beam

simulations were performed with a 10 mm wide beam that had a longitudinal range of -5

mm to 5 mm. The kerma from both the monoenergetic and polyenergetic beams to the

axial segment ranging from -5 mm to 5 mm was approximately five times greater than

the kerma in the adjacent segment. Since no primary x-rays reached the other segments,

the kerma to non-directly radiated segments can all attributed to scattered photons from

within the phantom. Conversely, the wide beam was 80 mm and thus covered the entire

range of axial segments. As a result the kerma had less variation across the segments.

Since all segments were directly-irradiated, the decrease in kerma to the more distal

segments can be attributed to the fact that the intensity of a cone beam is inversely

proportional to the square of the distance from the source. As mentioned in Section

http://en.wikipedia.org/wiki/Distance

180

9.2.D.3, the reported results are all on a per simulated photon basis and so the magnitude

of the doses presented in Figures 9.5 and 9.6 do not reflect the absolute doses to these

segments. In reality, wider collimations would deposit higher doses as the relative fluence

is greater than for narrow collimations.

The purpose of the Test 2 was to validate the rotation of the source about the

isocenter. Simulations were performed to obtain kerma values to the CTDI rod-like

regions at the center and periphery of the cylindrical phantom from a series of

incremental fixed gantry angles. Again, the results obtained with MCNPX simulations

matched what was expected. Due to the symmetry of the rotation and the cylindrical

phantom the kerma to the center rod should be the same regardless of the gantry angle of

the source. As shown in Figure 9.7, the kerma to the center rod was essentially the same

for each simulated gantry angle. As reported above, the variation across all gantry angles

was ~0.5%, which was within the MCNPX reported statistical variation (i.e. simulation

error). The kerma to the peripheral rods should have a sinusoidal dependency on the fixed

gantry angle. The MCNPX results for the peripheral rod exhibited expected periodic

dependence, as shown in Figure 9.7.

Test 2 utilized simulations of several fixed source positions at incremental gantry

angles meant to model a single source rotation. CT simulation codes commonly use an

alternative approach in which the source positions are obtained by randomly sampling a

continuous function. To determine if the results from the set of fixed source simulations

can be used as a benchmark for a package that models a continuous rotation, kerma in the

181

center and peripheral rods was obtained from the UCLA Monte Carlo CT package and

compared to the total kerma from the fixed position source MCNPX simulations. The

percent errors of the total kerma from discrete angle simulations were within the

statistical error of the simulations for both the center and peripheral rod. This indicates

that the average kerma value from set of fixed gantry angle can be used as a validation

benchmark for simulation packages that model rotations using continuous functions

instead of discrete source positions.

9.3 Half Value Layer and Bowtie Profile Measurements as Benchmarks

9.3.A. Introduction

In order to properly model a particular MDCT scanner using Monte Carlo

simulations it is necessary to use a detailed description of the scanner‘s x-ray energy

spectrum, the bowtie and inherent filtration design, and the geometry of the scanner (e.g.

focal spot to isocenter distance, fan angle, z-axis collimation, cone angle settings, etc.).

While it is usually possible to ascertain the geometry of a scanner from documentation,

descriptions of filtration and spectra are generally proprietary. To circumvent this

limitation some groups have worked with scanner manufacturers to obtain spectra and

filtration designs (usually through a non-disclosure agreement)31,33

. Others have used

generic tungsten anode x-ray energy spectra and bowtie filter specifications that are based

on experimentally measurements or theoretical derivations.37

Alternatively, studies have

been performed to examine the utility of obtaining spectrum and filtration schemes based

on measurement. Examples of this method include the equivalent source method

182

described in Chapter 4 and the method developed by McKenney, et al.77

to characterize

bowtie filters using a real-time dose probe.

The most straightforward method to assess a source model‘s accuracy is to

directly compare the results of a simulation with an analogous physical measurement. In

order to isolate the contribution of the source model to the overall simulation accuracy

these type of validation measurements should be made using simple phantoms and source

trajectories (with this approach the errors due to other components of the simulation, such

as inaccuracies in modeling the longitudinal source motion or phantom, are avoided).

Typically, CTDI metrics are used as a benchmark. While these simple simulations

provide some information about a source model‘s precision, it is not clear that they are

sufficient to fully validate a source model. For example, the majority of dose measured

by an ionization chamber in the center of the CTDI phantom is from primary radiation

that passes only through the central ray of the bowtie filter. Thus, on its own, the

CTDI100,center benchmark does not provide adequate confirmation that the filtration across

the fan beam is accurate. Furthermore, if there is significant disagreement between a

CTDI measurement and simulation it is difficult to pinpoint the source of the error (i.e. an

incorrect spectrum, filtration description, or some combination).

The purpose of this work was to compare the sensitivity of conventional CTDI

values to measurements designed to isolate specific components of a scanner-specific

source model. Specifically, the ability of HVL and QVL to assess the spectra and bowtie

183

profile measurements (first introduced in Chapter 4) to evaluate the filtration model will

be investigated as alternative benchmarks to the CTDI.

9.3.B. Methods

For this study, third-generation 64-slice MDCT scanners from two scanner

manufacturers were used (referred to as Scanner 1 and Scanner 2). All the measurements

and simulations described below for each scanner were performed using a protocol of

120 kVp, the widest available collimation setting, and the largest available bowtie. The

mAs setting for each physical measurement was high enough to ensure reproducible

measured values.

All simulations were performed using the UCLA MDCT Monte Carlo simulation

package described in Chapter 3. All MCNPX simulation results were converted to

absolute dose using scanner- and beam width-specific normalization factors, also

described in Chapter 3. For each simulation, enough photon histories were executed to

ensure the reported doses have statistical errors of less than 1% for each tally.

Two different types of x-ray source models, consisting of a photon energy

spectrum and a description of the scanner‘s filtration, were constructed for each scanner.

The first type (denoted Source Model A) consisted of the equivalent source models

generated from scanner-specific measurements, as outlined in Chapter 4. The

HVL&QVL method was used, in which a tungsten anode spectrum was hardened using

various hardening materials until its calculated QVL matched the measured value,

184

resulting in several candidate spectra. Then the spectrum with the calculated HVL that

best matched the measured HVL was deemed the equivalent spectrum. equivalent source

models for Scanners 1 and 2 were referred to as ―Source Model A1‖ and ―Source Model

A2‖, respectively. The second type of source model (denoted Source Model B) was based

on manufacturer provided spectrum and filtration descriptions. These manufacturer

provided source models were referred to as ―Source Model B1‖ and ―Source Model B2‖

for Scanner 1 and 2, respectively.

First, conventional exposure measurements were made on Scanners 1 and 2 in

order to calculate CTDI100,center and CTDI100,periphery for both the 16 cm diameter (head)

and 32 cm diameter (body) CTDI phantom. Analogous simulations were performed,

using geometric descriptions of the CTDI phantoms and ionization chamber, to obtain

simulated CTDI100,center and CTDI100,periphery values using Source Models A1, A2, B1, and

B2. The percent error of each simulated CTDI100 value was calculated relative to the

analogous measured CTDI100 value.

Next, HVL and QVL values were measured on Scanners 1 and 2. HVL was

defined as the thickness of aluminum necessary to reduce the exposure, measured in air at

isocenter, to ½ of the exposure measured with no aluminum present. The QVL was the

thickness of aluminum necessary to reduce the exposure to ¼ of that with no aluminum.

Measurements were obtained for each scanner using a stationary, non-rotating tube

parked at the 6 o‘clock position, as shown in Figure 9.8 (the bed was moved out of the

beam‘s path). An initial exposure measurement was obtained with a 100 mm ionization

185

chamber at isocenter with no aluminum present. Additional exposure measurements were

made, adding thin slabs (0.5–2.0 mm) of type 1100 alloy aluminum in the beam‘s path,

until the HVL and QVL were obtained. Analogous simulations were performed, using

geometric descriptions of the ionization chamber and aluminum slabs, to obtain simulated

HVL and QVL values using Source Models A1, A2, B1, and B2. The percent error of

each simulated HVL and QVL was calculated based on the value of the analogous HVL

and QVL measurements.

Figure 9.8 Diagram of the set up used to measure the HVL and QVL for both Scanners 1

and 2. The x-ray source remained stationary at the 6o'clock position.

Finally, bowtie profile measurements were obtained for Scanners 1 and 2. A

bowtie profile was defined as a set of exposure measurements across the top half of the

fan beam, normalized by the exposure measured at isocenter. All bowtie profile

measurements were made using a stationary, non-rotating tube parked at the 3 o‘clock

position, as shown in Figure 9.9. An initial exposure measurement was obtained with a

100 mm ionization chamber positioned in air at isocenter. Additional exposure

186

measurements were made in air, incrementally moving the scanner bed up in the y-

direction and then each measurement was normalized to the isocenter exposure.

Analogous simulations were performed, using geometric descriptions of the ionization

chamber, to obtain simulated bowtie profiles using Source Models A1, A2, B1, and B2.

The percent error of each simulated bowtie profile value was calculated relative to the

analogous physically measured value.

Figure 9.9 Diagram of the set up used to measure the bowtie profile for both Scanners 1 and

2. The x-ray source remained stationary at the 3 o'clock position.

9.3.C. Results

The percent error of each CTDI100 simulation relative to the actual measured

value is reported in Table 9.9 for all four combinations of scanners and source models

(i.e. Source Model A1, A2, B1, and B2). The root mean square of all the percent errors

for a given scanner/source model combination is listed in the last column. While the

purpose of this study is not to directly compare the source model types, it can be seen

187

that, on average, the percent errors for Source Model A (equivalent source models) are

less than those for Source Model B (manufacturer provided source model).

Table 9.9 The percent error of each CTDI100,center and CTDI100,periphery simulation.

Source

Model

16 cm diameter phantom 32 cm diameter phantom Root Mean

Square Center Periphery Center Periphery

A1 -0.7% 0.2% -3.3% -1.0% 1.8%

A2 6.9% 5.1% -0.5% 1.6% 4.4%

B1 -2.5% -0.2% -8.5% -5.0% 5.1%

B2 -62.1% 31.6% 25.6% 20.4% 38.5%

Table 9.10 shows the percent errors of each HVL and QVL simulation.

Specifically, for each scanner/source model combination, the percent error for the HVL,

QVL, and their root mean square is included. The percent errors for Source Model A are

lower for the QVL values than for the HVL values. This was expected since the

equivalent spectrum was optimized primarily to match the measured QVL. The percent

errors for the source model provided by the manufacturer for Scanner 1 were the smallest

while those for the manufacturer provided model for Scanner 2 were several times greater

than the other source models. It should be noted that the percent errors associated with

the HVL and QVL benchmarks are typically higher than those for the CTDI benchmarks.

Table 9.10 The percent error of each HVL and QVL simulation.

Source Model HVL QVL Root Mean Square

A1 11.2% 4.9% 8.6%

A2 14.5% 6.4% 11.2%

B1 2.7% -1.6% 2.2%

B2 45.1% 34.4% 40.1%

188

The percent errors of the bowtie profile simulation results with respect to the

measured values are shown as a function of the distance from isocenter for each source

model type are shown in Figure 9.10 for Scanner 1 and Figure 9.11 for Scanner 2. The

root mean square of these errors for each scanner and source model combination are

reported in Table 9.11. It can be seen that the root mean square values for Source Model

A are less than those for Source Model B for both scanners. Comparison with Table 9.9

shows that the root mean squares of the simulation errors for the bowtie profile

experiments were not consistently greater or less than those of the CTDI benchmark

experiments.

Figure 9.10 Percent error of bowtie profile simulations as a function the distance from

isocenter (in cm) for Scanner 1.

-20%

-15%

-10%

-5%

0%

5%

0 50 100 150

Distance from isocenter (in cm)

Source Model A1 Source Model B1

189

Figure 9.11 Percent error of bowtie profile simulations as a function the distance from

isocenter (in cm) for Scanner 2.

Table 9.11 The Root Mean Square percent error of each bowtie profile simulation.

Source Model Root Mean Square

A1 1.1

A2 3.9

B1 8.0

B2 23.7

9.3.D. Discussion

This study was conducted to evaluate the sensitivity of CTDI metrics to

benchmark the accuracy of scanner-specific source models. First, it was illustrated that

the CTDI is less sensitive than HVL and QVL benchmarks. Comparisons of Tables 9.9

and 9.10 shows that the root mean squares of the simulation errors were smaller for the

CTDI benchmark experiments compared to the HVL and QVL benchmark experiments

-10%

0%

10%

20%

30%

40%

50%

0 50 100 150 200 250

Distance from isocenter (in cm)

Source Model A2 Source Model B2

190

for almost all source models. This suggests that CTDI metric are less sensitive to

inaccuracies in the source models than HVL and QVL measurements.

Since CTDI100,center, HVL and QVL benchmarks depend primarily on the accuracy

of the spectrum (the majority of the dose for these measurements is due to the portion of

the beam passing through the center of the bowtie filter), bowtie profile measurements

were proposed to assess the accuracy of attenuation across the fan beam. This analysis

revealed that the root mean squares of the simulation errors for the bowtie profile

experiments were not consistently greater or less than those of the CTDI benchmark

experiments. This indicates that summary statistics of the errors from bowtie profile

measurements (such as root mean square) may not be an improvement over CTDI

metrics. However, the percent error profiles presented in Figures 9.10 and 9.11 illustrate

how individual bowtie profile measurements are useful for determining the accuracy of

the bowtie filter model as a function of fan angle.

This analysis illustrated that CTDI metrics are informative and should not be

omitted from a Monte Carlo validation study (i.e. CTDI benchmarks are necessary),

however, it did show that other tests reveal more specific information about the accuracy

of the source model (i.e. CTDI benchmarks are not sufficient). Finally, this work only

included measurements made in air or in a simple, homogenous phantom. Another

sensitivity study should be conducted to investigate the use of more complex,

heterogeneous phantoms for validation purposes.

191

9.4 Surface Dose Measurements on a Thorax Anthropomorphic Phantom

9.4.A. Introduction

The advanced validation methods presented in Sections 9.2 and 9.3 were

developed to assess the accuracy of specific components of a MDCT simulation package,

namely the radiation transport code and x-ray source model. While these are important

initial steps in developing a successful MDCT simulation code, they do not assess

techniques of modeling typical x-ray source trajectories (i.e. helical scans) or methods of

developing detailed patient models.

In order to assess the full simulation package it is necessary to compare

simulations to a dose measurement made using a complex phantom using a complex scan

type. As described above, typical detectors used to measure dose inside a phantoms, such

as TLD‘s, are problematic due to their high energy dependence and the unknown energy

of the hardened beam at the measurement point. As a result, the preferred detector for CT

energies is an ionization chamber, however, they are usually too large to fit inside

anthropomorphic phantoms or the attached wire makes it impossible to properly close the

phantom. The purpose of this study is to address these limitations by investigating the

utility of benchmarking a MDCT Monte Carlo package using a dose measurement from a

helical exam made with an ionization chamber placed on the surface of a phantom.

9.4.B. Methods

192

This study utilized the Alderson Lung/Chest Phantom developed by Radiology

Support Devices, Inc78

. This anthropomorphic thorax phantom extends from the neck to

below the diaphragm and is shown in Figure 9.12. The phantom constructed using the

skeleton of a male patient who is 175 cm tall and weighing 73.5 kg. RSD materials are

equivalent to natural bone and soft tissues.78

Animal lungs selected to match the size of

the adult male are fixed in their inflated state and molded to fit inside the pleural cavities

of the phantom and the blood vessels are filled with blood equivalent plastic.

Figure 9.12 The Alderson Chest/Lung Phantom from Radiological Support Devices, INC.

78

The RadCal modern wide beam multi-slice CT chamber (0.6-cc active volume)79

was used to measure dose for this study. This chamber was used with the RadCal Accu-

Pro Multi Purpose meter and was calibrated by the manufacturer using 150 kVp x-rays.79

This small ionization chamber was taped to the anterior of the chest phantom, just below

the shoulder region at the center of the coronal plane.

193

Dose was measured on a Siemens Sensation 64 scanner from 120 kVp helical

scans of pitch 1.5, nominal beam width of 28.8 mm, and effective mAs of 150 (tube

current modulation was not used). The scan region was defined using a topogram image.

The extent of the image data was 17.4 cm however there was 3.5 cm of z-overscan on

each side so the total irradiation length was 24.4 cm. The table height was adjusted so

that the center of the phantom was approximately in the center of the CT gantry and 25

dose measurements were obtained. Since the start angle for scans on the Siemens

Sensation 64 is random the relative position of the source when passing over the

ionization chamber varied for each separate measurement. The start angle for each scan

was retrieved from the raw data.

A voxelized model of the thorax phantom was created from the image data from

one of the scans using the methods developed by Angel, et al.61,62

and described in detail

in Chapter 8. Each voxel in the image was mapped to one of five anatomical materials

including soft tissue, lung, water, fat, or bone. This process is illustrated in Figure 9.13.

194

Figure 9.13 Generation of a voxelized model: (a) original patient image, (b) radiologist’s

contour of the breast region, (c) threshold image to identify glandular breast tissue and (d)

the resulting voxelized model. Reprinted from Angel, et al.61,62

.

Since the Alderson Chest/Lung phantom was constructed using tissue equivalent

materials, each of tissue type was assigned a single elemental composition and density

corresponding to the definitions specified in ICRU Report 4455

. Additionally, the wall

and air inside the ionization chamber were separately contoured by hand. In order to

obtain sufficient resolution to tally within the air portion of the ionization chamber it was

not possible to sub-sample the images when creating voxelized models. As a result each

axial slice consisted of a 512 x 512 array of material numbers. Each voxel was 0.8 mm

0.8 mm and the slice thickness was 1.5 mm. A series of illustrations of the high-

resolution voxelized phantom are shown in Figures 9.14-9.16. The green portion of the

ionization chamber represents the air tally region. As can be seen from these figures, the

scanner bed was mapped to various tissue types based on the CT number.

195

Figure 9.14 Axial view of the voxelized model created from images of the Alderson

Lung/Chest Phantom.

Figure 9.15 Sagital view of the voxelized model created from images of the Alderson

Lung/Chest Phantom.

Tally Region

Tally Region

196

Figure 9.16 Coronal view of the voxelized model created from images of the Alderson

Lung/Chest Phantom.

The UCLA MDCT Monte Carlo dosimetry package was used to simulate the dose

to the air inside the tally region from the helical scans. The longitudinal start and stop

locations for the simulations coincide with the boundaries of the voxelized phantom. As

mentioned above, the actual start and stop locations of the scans do not coincide with the

image data due to the 3.5 cm of z-overscan on each side. In order to ensure that the

helical path of the simulations was in phase with the actual measurement it was necessary

to determine the correct start angle for the simulations. The tube angle recorded from the

raw data corresponds to the angle at which the tube actually turned on, not the angle of

the tube at the start of the image data. So, the number of degrees that the source rotated

during the initial overscan was calculated based on the overscan distance, collimation

width, and pitch. Then, the proper simulated start angle (SAsim) was obtained by adding

Tally Region

197

the overscan rotation to the actual tube start angle (SAactual). The simulated tube start

angle was obtained from Equation 9.8:

Eq. 9.8

For the helical scans used to measure dose on the thorax phantom, the last two terms was

equal to 291.67 degrees.

Dose was obtained in mGy/NPS by tallying the energy fluence in the air portion

of the ionization chamber and converting to kerma using the energy-dependent mass-

energy absorption coefficients for air45

. Next, the appropriate scanner- and collimation-

dependent normalization factor was used to convert these results to dose in mGy/total

mAs. Then, the absolute dose in mGy was obtained by multiplying by the total mAs,

which is the product of the effective mAs (150), the total number of simulated rotations

(4.0278), and the pitch (1.5). Finally, the percent error of the simulated dose was

calculated for each tube start angle relative to the dose measured with the ionization

chamber.

9.4.C. Results

The measured and simulated dose values obtained with the ionization chamber on

the surface of the thorax phantom is reported in Table 9.12 for each actual tube start

angle. The last column shows the percent error of the simulated dose with respect to the

measured dose. The root mean square of the percent errors is 6.6%.

198

Table 9.12 The measured and simulated doses to the ionization chamber located on the

surface of the thorax phantom and the simulation percent error for each actual start angle.

Tube Start Angle Measured Dose

(mGy)

Simulated Dose

(mGy) Simulation % Error

291.72 10.07 9.26 -8.0%

355.03 11.36 11.70 3.0%

99.31 17.70 18.31 3.5%

6.2069 12.01 12.74 6.1%

281.79 10.42 9.40 -9.8%

219.72 14.24 13.08 -8.1%

255.72 11.72 10.44 -10.9%

84.414 17.07 17.67 3.5%

214.76 14.50 13.18 -9.1%

194.9 16.38 14.92 -8.9%

333.93 10.30 10.20 -1.0%

153.93 17.96 17.55 -2.3%

357.52 11.51 11.96 3.9%

114.21 18.22 18.88 3.6%

99.31 17.78 18.31 3.0%

198.62 15.77 14.37 -8.8%

202.34 15.28 14.44 -5.5%

356.28 11.41 11.88 4.1%

222.21 13.96 12.73 -8.9%

230.9 13.32 12.27 -7.9%

106.76 18.05 18.63 3.2%

269.38 11.00 9.70 -11.8%

24.828 13.26 13.62 2.7%

27.31 13.44 13.92 3.6%

A plot of the measured and simulated doses as a function of start angle is shown Figure

9.17. From this plot it can be seen that there appears to be a alight phase shift between the

simulated and measured doses.

199

Figure 9.17 The measured and simulated doses to the ionization chamber located on the

surface of the thorax phantom as a function of tube start angle.

9.4.D. Discussion

This work was performed to investigate the feasibility of using a surface dose

measurement made with a small ionization chamber placed on an anthropomorphic

phantom as a Monte Carlo simulation benchmark. These measurements were made using

a helical scan (with a pitch of 1.5) on a Siemens Sensation 64 scanner. This

benchmarking method overcomes many limitations of other, commonly utilized

validation techniques. First, the dose measurement was made with an ionization chamber

which has been shown to be energy independent, even in the diagnostic ranges. This

0

2

4

6

8

10

12

14

16

18

20

0 40 80 120 160 200 240 280 320 360

Do

se (

mG

y)

Tube Start Angle (degree)

Measured Simulated

200

immediately represents an improvement over studies that attempt to quantify absolute

dose with energy-dependent solid state detectors, TLD‘s, MOSFET‘s, OSL‘s, etc.

Second, this validation metric is comprehensive in that all the components of the

simulation package must be accurate in order to obtain a simulation result that matches

the measurement. This includes the radiation transport code, the scanner-specific x-ray

source, the longitudinal beam profile model, the methods used to model a rotating and

translating source, and the techniques used to build a voxelized patient model from image

data.

The results of this exercise illustrated that it is possible to obtain simulation

accuracies with a root mean square error of less than 10% across a number of different

starting conditions (i.e. tube start angle). The error of the simulations with respect to the

measurements for all 24 of the tube start angles are reported in Table 9.12. It has been

suggested that for complex CT dose simulations that incorporate a high number of

parameters that influence the results, simulation with errors of up to 20% can be

considered accurate.71

The maximum absolute error reported in Table 9.12, was 11.8%,

which is well within the 20% criterion.

While it is probably sufficient to attribute the simulation error to inaccuracies in

various levels of the simulation chain (i.e. imprecise spectra, filtration descriptions,

patient modeling techniques, etc.), the plot shown in Figure 9.17 indicates another

potential source of error. From this plot it appears there is a phase shift between the

201

simulated and measured values as a function of tube start angle. A phase shift suggests

that, for a given tube start angle, the lateral location of the ionization chamber in the

simulations may have been slightly different than that of the actual measurements. A

diagram to illustrate this principle is presented in Figure 9.18. This cartoon shows both a

centered phantom and one slightly shifted laterally to the left. For a specific tube start

angle, assume the source was at the 12 o‘clock position when passing directly over the

chamber (red source). This source position will result in the maximum dose to the green

chamber. It can be seen that the distance between the red source and the yellow chamber

(black arrow) is longer than the distance between the red source and the green chamber.

Thus the green chamber will receive a higher dose for that particular start angle because

of the inverse square law. Now, when the x-ray tube is located at the blue source position

the same argument can be used to show that the yellow chamber gets a higher dose. In

fact, it can be seen that the blue arrow (maximum dose to yellow chamber) is shorter than

the red arrow (maximum dose to green chamber). This indicates that the maximum dose

to the yellow chamber is greater than the maximum dose to the green chamber. A similar

argument can be made to show that the minimum dose to the yellow chamber is less than

the minimum dose to the green. In conclusion, the dose as a function of start angle for the

laterally shifted phantom will have a greater amplitude and be phase shifted in

comparison that of a centered phantom. The plot in Figure 9.17 shows this exact

behavior, indicating a lateral shift between the simulated and measured data. This

illustrates the importance of exactly recreating the measurement set up with the

202

simulation geometry for a validation study as intricate and detailed as the voxelized

thorax phantom benchmark described above.

Figure 9.18 Diagram to illustrate how a lateral shift results in a phase shift and amplitude

change for dose as a function of tube start angle plot.

This study utilized a helical scan performed with a constant tube current. It is

feasible that this method could be extended in order to validate simulations of TCM

exams. The same measurement procedure could be used to measure the dose from a

helical exam performed with TCM. The resulting dose value would serve as benchmark

for simulations that account for TCM, such as the one described in detail in Chapter 8.

203

This would represent one of the first attempts to assess the accuracy of TCM simulations

by direct comparison to physical dose measurements and should be addressed in future

work.

9.5 Conclusions

The focus of this chapter was on developing advanced benchmarks for validating

MDCT Monte Carlo dosimetry codes. It is difficult to establish standard validation

methodologies due to the large variation in Monte Carlo radiation transport codes and

the often misunderstood issues involved with properly calibrating physical dose detectors

at diagnostic energies (including TLD‘s, MOSFET‘s, and OSL‘s).

First, the reference test cases currently being generated by the AAPM Task Group

195 were summarized and the results obtained with the MCNPX simulation package for

the HVL/QVL and CTDI with Simple Phantoms test cases were presented. It was

demonstrated that these reference simulations can be performed using standard CT

modeling methods and that the results produced with the MCNPX transport code

matched expected results. Thus, the data presented in Section 9.2 can be used for

comparisons to assess the relative performance of other types of Monte Carlo radiation

transport codes. This work represents the first standardized set of Monte Carlo

simulations specifically designed for validation of common diagnostic imaging tasks.

When completed, the data included in the Task Group 195 Report will serve as a valuable

204

tool for researchers developing their own Monte Carlo codes or learning how to do

Monte Carlo simulations.

Next, a set of more sophisticated measurements for assessing the accuracy of

scanner-specific x-ray source models was proposed. Specifically, the goal of this exercise

was to determine better methods of assessing the x-ray energy spectrum and filtration

description based on measurements made on the scanner of interest. These benchmarks

consisted of HVL, QVL, and bowtie profile measurements. In order to avoid issues with

improper calibration, absolute dose measurements were all acquired with a 100 mm

ionization chamber. Since all three of these metrics are obtained with a parked x-ray

source, the accuracy of the analogous simulation s primarily depends on the accuracy of

the scanner-specific source model for the scanner of interest. It was shown that the HVL

and QVL measurements are more sensitive than common CTDI validation techniques

and thus can be considered a higher order check of the x-ray source model accuracy. The

bowtie profile measurements provide extra spatial information to evaluate the accuracy of

the filtration model that provides attenuation across the fan beam. This work was not

meant to suggest that CTDI validation techniques should be ignored; however, additional,

higher-order benchmarking should also be included in order to gain more specific

information about the specific source of potential simulation errors.

Finally, a more comprehensive benchmark measurement was proposed using a

small volume ionization chamber and a complex, anthropomorphic phantom. Dose from

a helical exam was measured using the ionization chamber which was fixed to the surface

205

of the Alderson Chest/Lung phantom. A large number of measurements were obtained,

all at different tube start angles. In order to accurately simulate these dose values it was

necessary to obtain an accurate voxelized patient model and scanner-specific x-ray source

information and to utilize a precise source trajectory model. Simulation results obtained

with the UCLA MDCT Monte Carlo package discussed in Chapters 3 and 4 were, on

average, within 10% of measured dose values. The power of this validation methodology

is the fact that the overall simulation error is due to the propagation of errors introduced

by the inaccuracies of the various simulation components. While a test like this is not

extremely useful for diagnosing where errors are coming from, it is the best indicator of

how well a simulation package can accurately obtain the dose to specific points on an

actual patient model. This approach could also be used to validate the method of

modeling tube current modulation by repeating the same steps but for a helical exam

performed with TCM.

It is clear that in order to produce credible results the accuracy of Monte Carlo CT

simulation packages must be properly validated. The advanced validation methods

presented in this chapter were each developed to assess specific components of a CT

simulation model. These benchmarks should be used to evaluate the accuracy of a code

from the ground up, starting with the radiation transport, then the x-ray source model,

then a simple rotating source with no translation, and finally a full helical and/or

translating axial scan.

206

A suggested path for robustly validating a Monte Carlo code along with some

specific benchmarks is presented in Figure 9.19. Starting at the top, each level should be

addressed individually until the accuracy of each major component of the simulation

package is successfully demonstrated. It is certainly possible to develop other types of

tests to assess each level‘s main component and to add more levels to this validation map;

however, this type of comprehensive approach to validation should be a prerequisite

before assuming the validity of simulation results obtained with CT Monte Carlo

packages.

207

Figure 9.19 Proposed approach for robustly validating the accuracy of a Monte Carlo CT

simulation package. Starting at the top, each level introduces a new level of complexity in

order to assess a different component of the simulation package.

Scanner-Specific Tube Current Modulation model

Dose measured with ion chamber on surface of heterogeneous anthropomorphic phantom for a TCM helical exam (extension of Section 9.4)

Scanner-specific helical source motion model and patient modeling techniques

Dose measured with ion chamber on surface of heterogeneous anthropomorphic phantom for a fixed tube current helical exam (Section 9.4)

Scanner-specific single rotation model with a simple phantom

Measured center and peripheral CTDI100 with head and body CTDI phantoms (Chapter 4)

Scanner-specific source model (energy spectrum and filtration)

Measured HVL, QVL, and bowtie profile benchmarks (Section 9.3)

Dose distribution in a simple voxelized patient with a generic scanner model

AAPM Task Goup 195 CT Dosimetry in Voxelized Patient Models Test Case (currently being developed)

Beam width model and rotation about isocenter model with a generic scanner

AAPM Task Group 195 CT Dosimetry in Simple Volumes (Section 9.2.D)

Radiation Transport Code

AAPM Task Group 195 HVL and QVL Test Case (Section 9.2.C)

208

Chapter 10 Dissertation Summary and Conclusions

This purpose of the work presented in this dissertation was to extend the field of

CT dosimetry by introducing novel methods of evaluating dose to patients. It was

established that the currently accepted clinical dose measurement paradigm, namely the

CTDI, is not a direct measurement of the preferred dose evaluation metric, the dose to

individual organs. In this dissertation, Monte Carlo simulations were heavily utilized to

obtain the dose to organs in detailed patient modes; however, the necessity of

individualized segmented organs makes it infeasible to perform such simulations on all

patients undergoing CT exams. Instead, the goal was to develop a generalizable organ

dose estimation method that could actually be used in a clinical setting based on readily

available information about the radiation output from the scanner and the size of the

patient.

Chapters 3 and 4 described the intricate details of the UCLA MDCT Monte Carlo

dosimetry package. Since the modeling of a specific scanner requires an accurate

description of the x-ray energy spectrum and filtration design (including bowtie filter)

and this information is difficult to obtain for specific scanners, an algorithm to generate

―equivalent x-ray source models‖ was developed. This methodology, presented in

Chapter 4, can be used to create source models for any scanner as it is based solely on

measured data. The high accuracy of simulations performed using the equivalent source

models was demonstrated using both common validation techniques (i.e. CTDI) as well

as the advanced benchmark metrics described in Chapter 9.

209

The advent of the equivalent source models made it possible to simulate any

MDCT scanner. As a result the work presented in Chapter 5 represented the first study

that compared the organ doses from different MDCT scanners under comparable scan

protocols. It was shown that, while absolute organ doses varied considerably, organ doses

normalized by measured CTDIvol values had very small variations across scanners. Thus

the average CTDIvol normalized organ dose value across scanners served as an accurate

approximation for any given scanner. This finding demonstrated the feasibility of

generating scanner-independent CTDIvol-to-organ dose conversion coefficients for

individual patients.

The study presented in Chapter 6 was conducted in order to investigate the

dependence of CTDIvol-to-organ dose conversion coefficients on patient size. It was

established that a strong decreasing exponential correlation exists with patient perimeter

for all organs fully encompassed in the scan region. Exponential fit parameters that are

specific to the type of scan (i.e. abdominal, chest, pelvis, etc) were determined for each

organ (denoted AO and BO) which can be used to calculate a CTDIvol-to-organ dose

conversion coefficient for any patient based on the perimeter of the central slice in the

scan region. Then, with a knowledge of the CTDIvol reported by the scanner for that

exam, a set of patient-, scanner-, and exam-specific organ doses can be obtained.

Chapters 7 and 8 describe a pair of studies that extend the organ dose estimation

technique to include organs only partially encompassed in the scan region and to account

210

for dose reductions due to tube current modulation. An expression to estimate dose to

partially-irradiated organs was derived in Chapter 7 that made several assumptions

regarding the individual doses to the portion of the organ inside and outside the scan

region. It was demonstrated that even though these assumptions are not always

completely satisfied, the partially-irradiated dose estimates are reasonably accurate,

especially when compared to the alternative dose evaluation, the CTDI. The work in

Chapter 8 illustrated a method to calculate patient-specific TCM correction factors for

dose estimates to fully-irradiated organs.

The major limitation that affected all of the organ dose studies was the small

number of available patient models with full sets of contoured organs. Only eight fully

segmented models (the GSF Family of Voxelized Phantoms) were available. Since these

models spanned a large range of ages and included both males and females, it was not

possible to obtain good enough statistics to generate dose estimation coefficients for

particular anatomical regions, namely the chest (inclusion of breast tissue) and pelvis (i.e.

variation in gonads and other gender-specific organs). The small number of patient

models also made it impossible to properly assess the accuracy of the dose estimation

method using training and testing patient model subsets. Currently, the number of UCLA

voxelized patient models, similar to those described in Chapter 8, are being constructed

and will be utilized to obtain dose estimation coefficients for anatomical regions other

than the abdomen and to rigorously validate the accuracy of the dose estimation method.

211

In conclusion, the culmination of the work presented in this dissertation

demonstrated the feasibility of a method to estimate dose to fully- and partially-irradiated

organs for fixed or modulated tube current exams from any scanner to any patient. An

overview of this method along with the necessary coefficients derived in this manuscript

is presented in Appendix C.

Since the required inputs to the proposed organ dose estimation method only are

either readily available (estimation coefficients summarized in Appendix C and the

CTDIvol included in the CT dose report) or relatively easily attainable (patient perimeter),

it is reasonable to suggest that doses to organs should be calculated on a routine clinical

basis. Work is already being done to include organ dose estimates in a DICOM dose

structure report which can be stored to commercial PACS systems and potentially used to

track the dose and even the risk to patients associated with CT exams.

212

Appendix A. Supplementary Tables from Chapter 4

Table 4.3 – Scanner/Bowtie Combination A – CTDI100 results across all kVp’s, phantom

sizes and positions for both measured and simulated results. Simulated results are from all

three source models: (a) source based on manufacturer-provided data, (b) equivalent source

model based on HVL method and (c) equivalent source model based on HVL&QVL

method. Percent difference values are the percent difference between simulated and

measured for each source model method.

% difference between simulated CTDI100 and

measured CTDI100

kVp Chamber

position

Measured

CTDI100

(mGy/mAs)

Manufacturer-

based source

model

HVL source

model

HVL&QVL

source model

16

cm

CT

DI

Ph

an

tom

80 center 0.036 -4.59 -3.05 -4.34

12:00 0.039 2.64 4.10 -1.81

100 center 0.075 -3.73 -2.70 -1.86

12:00 0.081 -0.78 0.78 -1.21

120 center 0.131 -2.51 -1.45 -0.67

12:00 0.140 -0.20 -0.47 -0.43

140 center 0.199 -2.83 -3.01 -1.28

12:00 0.212 0.03 -2.39 0.78

32

cm

CT

DI

Ph

an

tom

80 center 0.010 -10.27 -7.29 -10.76

12:00 0.021 -5.15 11.06 -4.92

100 center 0.024 -10.24 -8.09 -7.38

12:00 0.045 -5.47 1.29 -4.46

120 center 0.044 -8.50 -7.04 -3.67

12:00 0.081 -5.05 -3.20 -3.04

140 center 0.070 -6.36 -6.95 -2.71

12:00 0.125 -4.65 -7.87 0.34

Root Mean Square % Difference: 5.50 5.38 4.14

213

Table 4.4 – Scanner/Bowtie combination B - CTDI100 results across all kVp’s, phantom sizes

and positions for both measured and simulated results. Simulated results are from all three

source models: (a) source based on manufacturer-provided data, (b) equivalent source





measured CTDI100

kVp Chamber

position

Measured

CTDI100

(mGy/mAs)

Manufacturer-

based source

model

HVL source

model

HVL&QVL

source model

16

cm

CT

DI

Ph

an

tom

80 center 0.061 13.09 3.92 8.30

12:00 0.073 13.44 10.68 12.34

100 center 0.120 11.12 1.32 3.26

12:00 0.139 10.00 3.78 4.24

120 center 0.186 8.75 1.70 0.99

12:00 0.206 11.51 4.50 4.46

135 center 0.243 6.44 1.68 0.20

12:00 0.276 6.28 -1.04 -1.55

32

cm

CT

DI

Ph

an

tom

80 center 0.015 16.09 0.66 7.62

12:00 0.035 10.01 17.30 18.48

100 center 0.034 12.82 -4.96 -1.25

12:00 0.068 8.94 5.64 6.05

120 center 0.058 10.31 -5.27 -5.53

12:00 0.108 6.98 -1.94 -1.60

135 center 0.078 10.66 -7.31 -7.96

12:00 0.139 8.25 -3.53 -4.28


214

Table 4.5 – Scanner/Bowtie combination C - CTDI100 results across all kVp’s, phantom sizes







measured CTDI100

kVp Chamber

position

Measured

CTDI100

(mGy/mAs)

Manufacturer-

based source

model

HVL source

model

HVL&QVL

source model

16

cm

CT

DI

Ph

an

tom

80 center 0.058 18.51 -4.49 -2.37

12:00 0.073 9.06 -0.77 -0.25

100 center 0.112 10.69 -4.57 -2.74

12:00 0.134 5.48 -0.74 0.26

120 center 0.177 7.46 -4.41 -4.28

12:00 0.209 2.54 -2.63 -2.59

140 center 0.252 2.77 -4.51 -0.72

12:00 0.292 0.35 -3.06 -1.53

32

cm

CT

DI

Ph

an

tom

80 center 0.016 31.31 -10.10 -7.03

12:00 0.043 0.07 -6.51 -5.77

100 center 0.034 22.74 -9.57 -6.40

12:00 0.077 2.13 -3.88 -3.41

120 center 0.056 16.64 -7.09 -6.86

12:00 0.116 2.84 -3.38 -3.38

140 center 0.083 10.60 -4.20 -0.54

12:00 0.165 -0.44 -6.01 -4.78


215

Table 4.6 – Scanner/Bowtie combination D - CTDI100 results across all kVp’s, phantom sizes







measured CTDI100

kVp Chamber

position

Measured

CTDI100

(mGy/mAs)

Manufacturer-

based source

model

HVL source

model

HVL&QVL

source model

16

cm

CT

DI

Ph

an

tom

80 center 0.032 -- -2.85 -2.08

12:00 0.038 -- 1.80 2.02

100 center 0.111 -1.61 0.06 -0.03

12:00 0.124 2.81 0.93 1.20

140 center 0.162 -- -1.29 -1.06

12:00 0.180 -- 0.38 0.39

32

cm

CT

DI

Ph

an

tom

80 center 0.009 -- -4.22 -3.00

12:00 0.023 -- 1.48 1.45

100 center 0.037 -3.48 -2.65 -0.81

12:00 0.075 1.92 -3.58 -3.46

140 center 0.057 -- -2.04 -0.97

12:00 0.110 -- -5.78 -6.36


216

Table 4.7 – Scanner/Bowtie combination E - CTDI100 results across all kVp’s, phantom sizes







measured CTDI100

kVp Chamber

position

Measured

CTDI100

(mGy/mAs)

Manufacturer-

based source

model

HVL source

model

HVL&QVL

source model

16

cm

CT

DI

Ph

an

tom

80 center 0.066 -13.98 -4.08 -0.34

12:00 0.075 -11.20 0.75 2.27

100 center 0.122 -11.80 -4.54 -2.73

12:00 0.130 -9.74 -0.79 -0.19

120 center 0.188 -9.70 -4.83 -1.46

12:00 0.197 -8.59 -2.94 -1.75

140 center 0.263 -10.53 -5.49 0.70

12:00 0.273 -8.96 -5.63 -3.38

32

cm

CT

DI

Ph

an

tom

80 center 0.015 -13.19 1.39 6.76

12:00 0.036 -14.10 7.77 8.86

100 center 0.031 -11.79 0.08 4.81

12:00 0.066 -15.84 -1.57 -0.55

120 center 0.051 -10.70 -2.90 2.32

12:00 0.097 -12.16 -2.38 -1.98

140 center 0.076 -11.58 -5.59 3.05

12:00 0.137 -12.90 -7.38 -5.88


217

Table 4.8 – Scanner/Bowtie combination F - CTDI100 results across all kVp’s, phantom sizes







measured CTDI100

kVp Chamber

position

Measured

CTDI100

(mGy/mAs)

Manufacturer-

based source

model

HVL source

model

HVL&QVL

source model

16

cm

CT

DI

Ph

an

tom

80 center 0.084 23.94 4.15 6.85

12:00 0.110 18.23 12.00 13.01

100 center 0.152 21.31 3.01 4.58

12:00 0.183 17.89 7.04 7.43

120 center 0.227 19.64 3.66 6.86

12:00 0.265 18.07 3.64 5.05

135 center 0.292 13.71 1.47 2.49

12:00 0.349 9.50 -3.48 -2.59

32

cm

CT

DI

Ph

an

tom

80 center 0.020 29.94 -1.82 2.40

12:00 0.057 19.99 21.79 22.05

100 center 0.042 26.66 -4.76 -2.23

12:00 0.103 17.01 6.29 6.54

120 center 0.069 25.57 -4.16 -0.46

12:00 0.150 20.43 0.71 1.59

135 center 0.092 21.79 -7.49 -5.72

12:00 0.176 27.95 4.99 5.01


218

Table 4.9 – Scanner/Bowtie combination G - CTDI100 results across all kVp’s, phantom sizes







measured CTDI100

kVp Chamber

position

Measured

CTDI100

(mGy/mAs)

Manufacturer-

based source

model

HVL source

model

HVL&QVL

source model

16

cm

CT

DI

Ph

an

tom

80 center 0.070 -13.07 -4.81 -0.91

12:00 0.088 -7.75 -0.40 1.28

100 center 0.130 -10.81 -2.31 -2.31

12:00 0.153 -7.83 -1.34 -1.34

120 center 0.200 -8.81 -0.76 -0.76

12:00 0.230 -6.49 -1.60 -1.60

135 center 0.279 -9.75 -5.06 -1.43

12:00 0.311 -5.34 -3.19 -1.71

32

cm

CT

DI

Ph

an

tom

80 center 0.016 -12.04 -0.73 4.76

12:00 0.043 -9.84 6.16 7.40

100 center 0.034 -10.24 4.76 4.76

12:00 0.077 -9.08 2.31 2.31

120 center 0.056 -8.93 3.35 3.35

12:00 0.116 -8.21 -1.40 -1.40

135 center 0.083 -10.19 -5.45 -0.50

12:00 0.165 -10.75 -7.94 -6.75


219

Appendix B. Energy Dependence of Small Volume Ionization Chambers and Solid

State Detectors at Diagnostic Energy Ranges for CT Dosimetry – Assessment In Air

and In Phantom

Introduction

AAPM Task Group 111 has described a new methodology for measuring the dose

profile from CT exams to address the limitations of conventional 100 mm ionization

chambers. Their report suggests the use of ―a conventional thimble ionization chamber

with… a flat energy response (~1.5% variation) over the HVL range 2–15 mm Al and

which is calibrated by an accredited dosimetry calibration laboratory (ADCL) for ranges

of beam quality and kVp (80–140 kVp) associated with those of CT‖13

. Ionization

chambers are typically calibrated at higher energy levels. Since then, solid state detectors

with even smaller active lengths have also become a viable option. However, a

comprehensive investigation of the energy response of commercial small ionization

chambers and solid state detectors has not been published.

Reference chamber: PTW Farmer Ionization chamber with a sensitive volume of 0.6-cc

that has been calibrated by the University of Wisconsin ADCL using beams with HVL‘s

ranging from 2.96 to 10.2 mm Al. A polynomial function was obtained to describe the

calibration factor (NK) as a function of HVL:

HVL ADCL NK (x109 cGy/C)

2.96 4.722

4.98 4.663

6.96 4.641

10.2 4.685

220

Polynomial Regression:

Test Ionization Chamber: RadCal modern wide beam multi-slice CT chamber (0.6-cc

active volume). This chamber was used with the RadCal Accu-Pro Multi Purpose meter

and was calibrated by the manufacturer using 150 kVp x-rays.

Test Solid State Dosimeter: RTI CT Dose Profiler uses a solid-state chip with an active

length of 0.3 mm that has negligible angular dependence. Used with the RTI Barracuda

multimeter.

Half-value Layer: HVL‘s (mm Al) were obtained for the Siemens SOMATOM

Sensation 64 and the Toshiba Aquilion 64. The HVL of the beam was measured in-air

using a simple, fixed-tube approach. The HVL of the beam at the center of both head (16

cm diameter) and body (32 cm diameter) CTDI phantoms were determined using Monte

4.6

4.65

4.7

4.75

0 3 6 9 12

HVL in mm Al

ADCL Nk (x10^9 cGy/C)

221

Carlo simulations (these simulations are the subject of a separate AAPM conference

submission). For each HVL, a corresponding NK was calculated using the regression

equation above in order to precisely determine absolute dose values with the reference

chamber.

Toshiba Aquilion 64

kVp In-Air Head CTDI Phantom Body CTDI Phantom

HVL NK HVL NK HVL NK

80 3.49 4.703 3.14 4.715 2.80 4.729

100 4.47 4.675 4.03 4.687 3.58 4.700

120 5.45 4.655 4.91 4.665 4.36 4.678

135 6.10 4.648 5.49 4.655 4.88 4.666

Siemens SOMATOM Sensation 64

kVp In-Air Head CTDI Phantom Body CTDI Phantom

HVL NK HVL NK HVL NK

80 6.20 4.647 5.60 4.653 5.30 4.658

100 7.80 4.646 6.80 4.644 6.20 4.647

120 8.70 4.656 7.90 4.647 7.20 4.643

140 9.70 4.676 8.80 4.657 7.90 4.647

Methods: Doses were measured for single axial scans with the dosimeter at the isocenter.

The widest collimation setting was used to ensure the active portion of each dosimeter

was fully-irradiated (Siemens: 24 x 1.2 mm and Toshiba: 8 x 5.0 mm). Measurements

were also made in-air and in the center of the head and body CTDI phantoms. For each

kVp on both scanners, the mAs value necessary to produce the same measurement using

the reference chamber as the 80kVp/500mAs condition was established for in-air and in-

phantom set-ups. The same conditions were used for the two test chambers.

222

Results:

Head (16 cm diameter) CTDI Phantom Measurements

Scan Parameters Reference

Test 0.6-cc Ionization

Chamber

Test Solid State

Dosimeter

kVp HVL mAs mGy mGy

% error

relative to

Reference

mGy

% error

relative to

Reference

To

shib

a

80 3.14 500 19.84 20.50 3.2% 22.46 13.2%

100 4.03 280 19.70 20.32 3.1% 21.34 8.3%

120 4.91 180 19.02 19.97 4.8% 20.11 5.8%

135 5.49 140 19.33 20.15 4.0% 19.73 2.1%

Sie

men

s

80 5.60 500 8.82 9.26 4.9% 10.66 20.8%

100 6.80 240 8.82 9.25 4.8% 9.91 12.3%

120 7.90 145 8.92 9.24 3.6% 9.35 4.8%

140 8.80 92 8.84 9.21 4.2% 8.60 -2.7%

Body (32 cm diameter) CTDI Phantom Measurements

Scan Parameters Reference

Test 0.6-cc Ionization

Chamber

Test Solid State

Dosimeter

kVp HVL mAs mGy mGy

% error

relative to

Reference

mGy

% error

relative to

Reference

Tosh

iba

80 2.80 500 3.90 4.20 7.3% 5.33 36.7%

100 3.58 250 3.81 4.25 10.4% 5.13 34.8%

120 4.36 150 3.72 4.21 11.6% 4.93 32.3%

135 4.88 110 3.67 4.16 11.8% 4.80 30.8%

Sie

men

s

80 5.30 500 1.95 2.14 9.4% 2.39 22.5%

100 6.20 216 2.00 2.14 7.0% 2.25 12.6%

120 7.20 121 2.04 2.12 3.7% 2.08 1.8%

140 7.90 76 2.05 2.15 4.7% 2.00 -2.3%

Conclusions: The results show that ionization chamber measurements agree to within

3.5% in-air and 4.9% in the head CTDI phantom across all HVL‘s. Variations were larger

in the body CTDI phantoms (as high as 11.8%). On average, measurements made with

the solid state dosimeter had considerably larger differences with the reference chamber.

The maximum differences with the reference chamber were as high as 16.1%, 20.8%, and

36.7% for in-air, head, and body CTDI phantom, respectively. However, for a given

223

measurement condition, the magnitude of the differences between the solid state

dosimeter and the reference chamber varied considerably across HVL values. For

example, solid state measurements made in-air at 80 kVp (3.5 mm Al) with the Toshiba

scanner had a 12.0% difference from the reference chamber while at 135 kVp (6.1 mm

Al) the difference was only 2.1%. Similar variations with kVp were seen for in-phantom

measurements. This demonstrates the need to carefully calibrate solid state detectors for

the specific HVL of the beam being measured. Thus, for measurements made in phantom,

it is necessary to know the HVL of the beam at the location of the measurement (i.e. the

HVL of the spectrum resulting from primary beam hardening and scatter in phantom).

224

Appendix C. Summary of Organ Dose Estimation Method

This appendix contains a summary of the organ dose estimation method proposed

in this dissertation. The work presented in Chapters 5-8 demonstrated the possibility of

calculating patient-specific, scanner-independent CTDIvol-to-organ dose conversion

coefficients. Separate methods were derived for fully-irradiated organs and partially-

irradiated organs. Additionally, the effects of tube current modulation (TCM) can be

accounted for using patient-specific correction factors for fully-irradiated organs.

The initial step in the estimation process is to classify each organ as fully-

irradiated, partially-irradiated, or non-irradiated depending on the type of scan (e.g. chest,

abdomen, pelvis, abdomen/pelvis, etc). Next, based the parameters of the scan (including

whether or not TCM was used), the dose can be estimated using equations and diagrams

listed below.

Each equation and diagram refers to a set of coefficients (i.e. AO, BO, CO, DO,

etc.). These coefficients are specific to the scan region, kVp, and are reported for a pitch

of 1 (since dose is inversely related to pitch it is only necessary to divide organ dose

estimates by the actual pitch values). For each scan region, a set of tables listing the

coefficients that have been presented so far in the feasibility studies presented in this

dissertation is included at the end of this. Dashes (-) in the table indicate that the

particular coefficient has not been generated yet Future work will need to be performed to

obtain coefficients for other organs, additional scan regions, and all kVp‘s.

225

Fully-irradiated organs with fixed tube current

Diagram of the proposed method to estimate patient-, scanner-, and exam-specific dose to

fully-irradiated organs using the size coefficients (AO, BO), patient perimeter (in cm), and

the CTDIvol.

Patient-,

Scanner-,

Exam-specific

Organ Dose

(mGy)

Size Coefficients

(AO, BO)

Patient Perimeter

(p)

Exam-specific

CTDIvol

(body phantom)

Patient-specific

CTDIvol-to-organ

dose conversion

coefficient

226

Fully-Irradiated with tube current modulation (TCM)


fully-irradiated organs using the size coefficients (AO, BO), TCM correction factor

coefficients (CO, DO) patient perimeter (in cm), and the CTDIvol corresponding to the

Quality Reference mAs.

Note: The study performed to demonstrate the feasibility of accounting for TCM only

focused on the Siemens Sensation 64 scanner. The Quality Reference mAs is a Siemens-

specific concept and does not apply to TCM exams performed on scanners from other

manufacturers. However, each scanner requires some metric to determine the overall

level of TCM. Using methods similar to those presented in this study, it should be

possible to determine some type of CTDIvol variant and scanner-specific correction factor

parameters (such as the CO and DO coefficients) to estimate TCM doses for scanners of

each manufacturer and should be addressed in future work.

Patient-specific

TCM correction

factor

Exam-specific

CTDIvol

(for Quality

Reference mAs)

TCM Correction

Coefficients

(CO, DO)

Patient Perimeter

(p)

Patient-,

Scanner-,

Exam-specific

Organ Dose

(mGy)

Patient-specific

CTDIvol-to-organ

dose conversion

coefficient

Patient Perimeter

(p)

Size Coefficients

(AO,BO)

227

Partially-Irradiated Organs


partially-irradiated organs using the size coefficients (AO,in, BO,in), average percent coverage

(αorgan), patient perimeter (in cm), and the CTDIvol.

Patient-,

Scanner-,

Exam-specific

Organ Dose

(mGy)

Partial-

irradiated Organ

Size

Coefficients

(AO,in,BO,in)

Patient Perimeter

(p)

Exam-specific

CTDIvol

CTDIvol-to-dose

conversion

coefficient for In-

beam segment

(

Partially-

irradiated Organ

Percent

Coverage

(αorgan)

228

Dose Estimation Coefficients for Various Scan Regions

Abdomen

Fully-Irradiated Organs

Organs AO BO CO DO

Liver 3.824 -0.0120 - -

Stomach 3.780 -0.0113 - -

Adrenals 4.029 -0.0128 - -

Kidney 3.969 -0.0124 - -

Pancreas 3.715 -0.0122 - -

Spleen 3.514 -0.0111 - -

Gall Bladder 3.994 -0.0115 - -

Partially-Irradiated Organs

Organs AO,in BO,in αorgan

Red Bone Marrow 2.853 -0.0132 0.21

Colon 3.641 -0.0102 0.84

Lungs 2.741 -0.0104 0.34

Esophagus 2.860 -0.0119 0.33

Bone Surf 7.932 -0.0129 0.21

Skin 2.827 -0.0083 0.26

Heart 2.829 -0.0107 0.53

Muscle 3.123 -0.0096 0.24

Small Intestine 3.867 -0.0118 0.77

Abdomen Pelvis


Organs AO BO CO DO

Liver 5.39 -0.0136 0.0150 -0.7763

Spleen 3.29 -0.0084 0.0150 -0.7613

Kidney 5.29 -0.0127 0.0165 -0.9113

Chest


Organs AO BO CO DO

Lung 5.69 -0.0101 0.0120 -0.6119

Glandular Breast 4.28 -0.0102 0.0150 -0.9263

229

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