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Simulation of Breast Anatomy:
Anthropomorphic Software Phantoms
Predrag R. BakicUniversity of Pennsylvania,
Department of Radiology
Philadelphia, PA, USA
Imaging Symposium – 3D Breast Models AAPM/COMP 2011, Vancouver, Canada, August 1, 2011
Antoinette Flight Simulator(Paris, 1909)
Shepp-Logan Phantom
http://sites.google.com/
site/hispeedpackets/Home/shepplogan
www.mathworks.com/matlabcentral/
fileexchange/9416-3d-shepp-logan-phantom
The Visible Human Project®
http://www.nlm.nih.gov/
research/visible/visible_human.html
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4D-NCAT Phantom
http://dmip.rad.jhmi.edu/
people/faculty/Paul/Segars_research.htm#NCAT
Octree-based NCAT
Imaging Simulation
Badal, Kyprianou, Badano, et al., SPIE 6510, 2007
Validation and Optimization of
Imaging Systems
Challenging due to system complexity;
Large number of parameters influence
performance.
Preferred approach: Imaging clinical trials.
Validation and Optimization of
Imaging Systems Limitations of clinical trials
Cost
Duration
Irradiation of volunteers
Preclinical alternative: Virtual Clinical Trialsbased upon models of anatomy and image acquisition.
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Simulate tissue structures which make up
anatomical noise in clinical images
Simulate consistently images of the same
anatomy while varying acquisition specs
Provide the ground truth info about simulated
tissues for quantitative validation
Cover anatomic variations by providing flexibility
to modify phantom composition
Rationale for Developing
Computer PhantomsMultimodality Breast Imaging
Has been developed since 1996; now used by
15+ research labs worldwide
Provides ground truth, which is not available
clinically
Based on rules for modeling tissue
structures, providing flexibility to cover
anatomical variations
Penn Anthropomorphic
Software Breast PhantomAnatomical Correlation
(A.P. Cooper, On the Anatomy of the Breast, 1840)
(S.R. Wellings,
J Natl Cancer Inst, 1975)
(Bakic, et al.
Med Phys, 2011)
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Adipose Compartments in
Clinical Breast Images
(L. Tabar et al., Breast Cancer,
Thieme, Stuttgart, 2005)
Regions of predominantly adipose and
predominantly fibro-glandular tissue
Adipose compartments simulated
by region growing
Breast CT of mastectomy specimen
(Glick, U. Massachusetts)
n
(Zhang et al. SPIE 2008)
Software Breast Phantom Composition
Simulated Ductal Network
Shown five (out of 12)
ductal lobes
(Bakic et al., Med Phys 2003)
Flexibility of the Phantom Design
Cross-sections and projections of
phantoms with different glandularity.Cross-sections of phantoms corresponding
to different breast size.
(Bakic et al., Med Phys, 2011)
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Phantom Section
w/ Simulated Breast
Compression
DBT Reconstructed ImageX-ray Projection
(Bakic et al., SPIE 2010)
Simulation of Phantom Image Acquisition
Simulated T1-weighted MRI slices
( with 6 and 3mm slice thickness) Simulated ultrasound tomography
images of the phantom
(Bakic et al., SPIE 2011;
Collaboration w/ Duric Lab @ Karmanos)
Simulation of Phantom Image Acquisition
Simulation of Breast Lesions
Spiculated Masses Microcalcifications
Phantom Applications
Mammography:
Estimate dose to simulated tissue (using MC)
Validate registration of temporal images
Simulate scatter contribution (using MC)
Test image compression methods
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Dose Estimation
Phantom Thicknes,
Glandularity
DG
(Voxel Phantom)
DG
(Simple phantom)
Difference
(%)
4cm, 100% 0.185 0.188 1.6%
4cm, 53% 0.205 0.228 10%
5cm, 69% 0.138 0.168 17%
“Simple” Phantom Voxel Phantom
DG = the mean glandular dose for 1 mGy incident air kerma
(Hunt, Dance, Bakic, et al, UKCR 2003)
Phantom Applications
Digital Breast Tomosynthesis:
Assess geometric accuracy
Optimize reconstruction algorithms
Task based: e.g., breast density estimation, calcs
Analyze power spectrum of anatomical noise
Design observer studies using detailed
detector model (via MC) and observer models
Reconstructed ImageDBT Projection
(Bakic et al., IWDM 2010;
Collaboration w/ Real-Time Tomography)
Geometric Accuracy of DBT Methods: Supersampling
We reconstructed a series of 10 images with sub-pixel shifts within
the plane of reconstruction and combined them to form a
supersampled image.
6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.81
1.5
2
2.5
3
3.5
4
x 104
x (mm) from Chest Wall
Su
pe
rsa
mp
led
re
co
ns
tru
cte
d Im
ag
e In
ten
sit
y
6.52 6.53 6.54 6.55 6.56 6.571.5
2
2.5
3x 10
4
x (mm) from Chest Wall
Su
pe
rsa
mp
led
re
co
ns
tru
cte
d Im
ag
e In
ten
sit
y
(Bakic et al., IWDM 2010;
Collaboration w/ Real-Time Tomography)
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Methods: Supersampling
We performed 10× supersampling in the scanning (y) direction.
19.820
20.220.4
34567
1
2
3
4
5
x 104
x (mm) from Chest Wally(mm) along Chest Wall
(from Detector Center)
Su
pe
rsa
mp
led
Re
co
ns
tru
cte
d Im
ag
e In
ten
sit
y
x
y
(Bakic et al., IWDM 2010;
Collaboration w/ Real-Time Tomography)
Results: Marker Position Error
0.00
0.05
0.10
0.15
0.20
0.25
0 10 20 30 40 50
Reconstructed Plane Depth z(mm)
Err
or
(mm
)
(xC-xT)(yC-yT)(zC-zT)Ep
• EP were averaged over all markers at the same depth in the phantom. (Error
bars = one SD.)
• Shown separately are the errors along each coordinate.
(Bakic et al., IWDM 2010;
Collaboration w/ Real-Time Tomography)
Phantom Applications
Other Modalities:
Optimize non-ionizing ultrasound tomography
Task based
Optimize radiation therapy
Future
Validate breast CT
Compare CE-DBT vs. DCE-MRI
Validate Mammo PETCoronal Section of a
Phantom with VBD=20%UST Reconstructed Image
UST Validation
(Bakic et al., SPIE 2011;
Collaboration w/ Duric Lab @ Karmanos)
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Phantom VBD = the volume fraction occupied by dense (non-adipose) tissues.
0
2
4
6
8
10
12
14
16
18
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Phantom VBD(%)
Co
un
t
Phantom VBD roughly follows the distribution calculated from >2800 women (Yaffe, 2009).
(Yaffe et al, “The Myth of the 50-50 Breast”, Med Phys 2009)
UST Validation Simulated Phantom DM Images
Vertical Section of a
Phantom with VBD=40%
Phantom DM Image
(Bakic et al., SPIE 2011;
Collaboration w/ Duric Lab @ Karmanos)
Results: Cumulus PD vs. UST VBD
ρ = 0.78
y = 1.21x - 3.70
R2 = 0.61
0
10
20
30
40
50
0 10 20 30 40 50
UST VBD (%)
Cu
mu
lus P
D (
%)
r = 0.78
ρ = 0.59
ρ = 0.75
Boyd et al., JNCI 2010
(Bakic et al., SPIE 2011;
Collaboration w/ Duric Lab @ Karmanos)
Currently Used
Software Phantoms
• Based upon models of breast anatomy• Taylor et al. /UWA: IWDM 1998, 2000
• Bakic et al. / Penn: CBMS‟98; IWDM‟98-‟10; SPIE‟99, ‟07-11; RadProtDosimetry‟05; MedPhys‟02-03, ‟11
• Bliznakova et al. / Patras, GR: PMB‟03, ‟06, MBEC‟07, IFMBE‟08, MedPhys‟10
• Ma et al./ Dexela: PMB‟09
• Reiser & Nishikawa / UChicago: Med Phys‟10
• Shorey et al. / Duke: AcadRadiol‟11
• Based upon individual clinical images• Hoeschen, Zankl et al. / GSF: RadProtDosimetry‟05
• Glick et al. / UMass: IWDM‟08; SPIE‟08-‟09; MedPhys‟09, TMI‟10
• Hsu (nee Li) et al. / Duke: SPIE‟08-„09, MedPhys‟09
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Univ. of Patras, Greece
3D breast model
Duct system
Background texture
Cooper ligaments
AbnormalityLymphatic system
Breast shape
Courtesy of K. Bliznakova, Patras
Simulation of mammographic background
Random walks
Empty matrix ++
Dilation
+
++
Gaussian FilteringFit to the shape
Low Pass Filtering
Texture matrix
Courtesy of K. Bliznakova, Patras
Examples of projection images
Courtesy of K. Bliznakova, Patras
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examples of ROI for evaluation
real simulated
real
simulated
real simulated
real
simulated
Courtesy of K. Bliznakova, Patras
Univ. Mass., Worcester, MA, USA
Simulated mammogram
Courtesy of S. Glick, UMass, USA
Duke Univ., Durham, NC, USA
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Image Generation
Simulated Mammogram
Christina M.L. HsuBiomedical Engineering
Duke University
Physical Version of the
Penn Software Phantom
Physical Version of the Software
Phantom*
(Carton, Bakic et al, Med Phys 2011, * Patent Pending)
Customized inserts
simulating iodinated lesions
Physical Version of the Software
Phantom*
(Carton, Bakic et al, Med Phys 2011, * Patent Pending)
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Physical Version of the Software
Phantom*
Customized inserts
simulating iodinated lesions
(Carton, Bakic et al, Med Phys 2011, * Patent Pending)
Physical Version of the Software
Phantom*
HE LEDual energy
CE-DM
(Carton, Bakic et al, Med Phys 2011, * Patent Pending)
New Penn Phantom Design**
Upgrade of our region growing concept
Collaboration w/ D. Pokrajac, DSU
Based upon octree recursive partitioning
Advantages
Low (close to minimal) complexity; fast
Scalable
Allows thickness control of skin and
Cooper‟s ligaments
(Pokrajac, Maidment, Bakic, AAPM 2011, ** Patent Pending)
0.1
1
10
100
1000
10000
100000
10 100 1000 10000
Voxel Size (μm)
Sim
ula
tio
n T
ime (
min
ute
s)
Region Growing
New Design
New Design vs. Region Growing**
333 compartments
Region Growing: y ~ x -4.17
New Design: y ~ x -2.04
(Pokrajac, Maidment, Bakic, AAPM 2011, ** Patent Pending)
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400um phantom, 333 compartments,
target thickness 0.6mm
(Pokrajac, Maidment, Bakic, AAPM 2011, ** Patent Pending)
100um phantom, 333 compartments,
target thickness 0.6mm
(Pokrajac, Maidment, Bakic, AAPM 2011, ** Patent Pending)
25um phantom, 333 compartments,
target thickness 0.6mm
(Pokrajac, Maidment, Bakic, AAPM 2011, ** Patent Pending)
“Even Newer” Phantom Design
Partial Volume Simulation
Simulate voxels which contain 2+ tissues
or materials
Improves phantom image quality w/o need
to reduce voxel size
Phantom Section Detail
(no Partial Volume)Phantom Section Detail
(with Partial Volume)
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“Even Newer” Phantom Design
Shape Analysis of Simulated Anatomy
Phantom Section Fitted Ellipsoids
Fit ellipsoids to tissue
compartments
Help validate control
over phantom shape
Analyze clinical data
to refine simulation
Shape Analysis by Ellipsoidal Fitting
Class I
(a) (b)
(c) (d)
Class II
Class III Class IV
Mean Dice Coefficient
0.7
0.75
0.8
0.85
1 2 3 4
Phantom Classes
Class I Class II Class III Class IV
Dice Coefficient Variance
0
0.005
0.01
0.015
0.02
0.025
0 1 2 3 4
Phantom Classes
Class I Class II Class III Class IV
Strengths of the Full Simulation
of Breast Anatomy
Availability of the ground truth Reliable – unlike with clinical data
Needed for quantitative validation
Flexibility to cover wide anatomical variations Not limited to clinically available cases
Scalability Allows control of simulated tissue structures at different scales
Convenient for performing virtual clinical trials Affordable pre-clinical validation of imaging systems
Areas of Potential Improvement
Allow real time generation of many hi-res phantoms. Ongoing research on parallelization.
Future aim: Fast & cheap fabrication of physical phantoms
Improve the realism. Models from clinical data have superior realism; their ground
truth depends on segmentation/classification.
Is that necessary? An optimal phantom should rank the analyzed methods comparable to clinical performance.
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Antoinette Flight Simulator (Paris, 1909)
Space Shuttle Mission Simulator (Cmdr. Mark Kelly, 2011)
… Beyond the Space Shuttle Program?
Orion Multi-Purpose Crew Vehicle Development
@ Space Operations Simulation Center (Colorado)
Penn Radiology Physics Lab Dr. Andrew Maidment, Director
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Acknowledgement
Kristina Bliznakova, Christina (Li) Hsu, and Stephen Glick provided slides on their breast phantom designs.
David Pokrajac created slides on the new phantom design.
Penn breast phantom research has been funded by• Lehigh Univ., NSF Grant (during PhD studies)• Univ. Mass, NIH R01 (subcontract)• Del State Univ., NIH INBRE • RTT, NIH R44 (subcontract)• DoD HBCU DSU/Penn Partnership Training Award • NSF Collaborative ISS • Univ. Chicago (subcontract)• NIH/NIBIB R01 (w/ Barco)
Perelman Center for Advanced Medicine,
UPenn, 2008
Sava & Tesa Bakic, 11 mos
Kosta Bakic, 3.5 years
Thank You!
“Clothespin”, Philadelphia
C. Oldenburg, 1976
See you at the IWDM 2012
in Philadelphia July 8-11!
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