Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging

in patients with emphysematous lung regions

This article has been downloaded from IOPscience. Please scroll down to see the full text article.

2011 Phys. Med. Biol. 56 2279

(http://iopscience.iop.org/0031-9155/56/7/023)

Download details:IP Address: 129.78.32.23The article was downloaded on 10/05/2012 at 11:03

Please note that terms and conditions apply.

View the table of contents for this issue, or go to the journal homepage for more

Home Search Collections Journals About Contact us My IOPscience

IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY

Phys. Med. Biol. 56 (2011) 2279–2298 doi:10.1088/0031-9155/56/7/023

Investigation of four-dimensional computedtomography-based pulmonary ventilation imaging inpatients with emphysematous lung regions

Tokihiro Yamamoto1,4, Sven Kabus2, Tobias Klinder3, Cristian Lorenz2,Jens von Berg2, Thomas Blaffert2, Billy W Loo Jr1 and Paul J Keall1

1 Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake WilburDr, Stanford, CA 94305-5847, USA2 Department of Digital Imaging, Philips Research Europe, Roentgenstrasse 24-26, D-22335Hamburg, Germany3 Clinical Informatics, Interventional, and Translational Solutions, Philips Research NorthAmerica, Briarcliff Manor, NY 10510, USA

E-mail: Tokihiro@stanford.edu

Received 23 July 2010, in final form 12 January 2011Published 16 March 2011Online at stacks.iop.org/PMB/56/2279

AbstractA pulmonary ventilation imaging technique based on four-dimensional (4D)computed tomography (CT) has advantages over existing techniques. However,physiologically accurate 4D-CT ventilation imaging has not been achieved inpatients. The purpose of this study was to evaluate 4D-CT ventilation imagingby correlating ventilation with emphysema. Emphysematous lung regionsare less ventilated and can be used as surrogates for low ventilation. Wetested the hypothesis: 4D-CT ventilation in emphysematous lung regions issignificantly lower than in non-emphysematous regions. Four-dimensionalCT ventilation images were created for 12 patients with emphysematous lungregions as observed on CT, using a total of four combinations of two deformableimage registration (DIR) algorithms: surface-based (DIRsur) and volumetric(DIRvol), and two metrics: Hounsfield unit (HU) change (VHU) and Jacobiandeterminant of deformation (VJac), yielding four ventilation image sets perpatient. Emphysematous lung regions were detected by density masking.We tested our hypothesis using the one-tailed t-test. Visually, different DIRalgorithms and metrics yielded spatially variant 4D-CT ventilation images.The mean ventilation values in emphysematous lung regions were consistentlylower than in non-emphysematous regions for all the combinations of DIRalgorithms and metrics. VHU resulted in statistically significant differencesfor both DIRsur (0.14 ± 0.14 versus 0.29 ± 0.16, p = 0.01) and DIRvol

(0.13 ± 0.13 versus 0.27 ± 0.15, p < 0.01). However, VJac resulted innon-significant differences for both DIRsur (0.15 ± 0.07 versus 0.17 ± 0.08,

4 Author to whom any correspondence should be addressed.

0031-9155/11/072279+20$33.00 © 2011 Institute of Physics and Engineering in Medicine Printed in the UK 2279

2280 T Yamamoto et al

p = 0.20) and DIRvol (0.17 ± 0.08 versus 0.19 ± 0.09, p = 0.30). This studydemonstrated the strong correlation between the HU-based 4D-CT ventilationand emphysema, which indicates the potential for HU-based 4D-CT ventilationimaging to achieve high physiologic accuracy. A further study is needed toconfirm these results.

(Some figures in this article are in colour only in the electronic version)

1. Introduction

Pulmonary function tests (PFTs) provide a global measure of pulmonary physiologicinformation which is relatively insensitive to regional changes. Pulmonary diseases presentregional involvement especially during the initial phases (de Jong et al 2006) before globalmeasures change. Imaging techniques of regional function would further our understanding ofpathophysiological characteristics of pulmonary diseases and could also be used for functionalavoidance in lung cancer radiotherapy (Marks et al 1995, Seppenwoolde et al 2002, Christianet al 2005, McGuire et al 2006, Shioyama et al 2007, Yaremko et al 2007, Yamamoto et al2011). There have been several techniques of pulmonary ventilation imaging, includingnuclear medicine, magnetic resonance (MR) and computed tomography (CT). Nuclearmedicine imaging has been the only technique of ventilation imaging for the past few decades(Alderson and Line 1980, Suga 2002, Harris and Schuster 2007). Hyperpolarized gas MRI(Albert et al 1994, Kauczor et al 1998) has been found to be useful for the assessment ofasthma (de Lange et al 2006), cystic fibrosis (McMahon et al 2006) and emphysema (Swiftet al 2005). Xe-CT has been pioneered by Gur et al (1979, 1981) and has been studied byseveral investigators (Marcucci et al 2001, Tajik et al 2002). However, these techniques havedrawbacks such as low resolution, high cost, long scan time and/or low accessibility.

A ventilation image can be created by a four-dimensional (4D) CT-based techniquewhich has been applied to animal subjects (Guerrero et al 2007, Reinhardt et al 2008,Ding et al 2010) and human subjects (Guerrero et al 2005, 2006, Christensen et al 2007,Kabus et al 2008, Vik et al 2008, Castillo et al 2010, Yamamoto et al 2010). The 4D-CT-derived ventilation can be considered ‘free’ information for lung cancer radiotherapy patients,because 4D-CT scans are routinely acquired during treatment planning at many radiotherapycenters and ventilation computation involves only image processing, i.e. deformable imageregistration (DIR). Moreover, 4D-CT ventilation imaging is higher resolution, costs less, hasa shorter scan time and is more accessible from radiotherapy centers than existing techniques.However, variations in DIR results between algorithms have been reported (Kashani et al2008, Brock 2009, Kabus et al 2009). Recently, two multi-institution studies were conductedto evaluate the accuracy of various DIR methods. These studies demonstrated large variabilityin the maximum error ranging from 5.1 to 15.4 mm (vector) in a phantom study (Kashaniet al 2008) and from 2.0 to 7.8 mm (SI) in a patient study (Brock 2009). Such variabilityin the DIR results may influence 4D-CT ventilation imaging. In addition, two classes ofventilation metric have been used for 4D-CT ventilation imaging: Hounsfield unit (HU)change (Guerrero et al 2005, 2006, Fuld et al 2008, Kabus et al 2008, Castillo et al 2010,Ding et al 2010, Yamamoto et al 2010) and Jacobian determinant of deformation (Kabuset al 2008, Reinhardt et al 2008, Castillo et al 2010, Ding et al 2010, Yamamoto et al 2010).The variability in the resulting ventilation images has been reported by several investigators(Castillo et al 2010, Ding et al 2010, Yamamoto et al 2010). The physiologic accuracy of4D-CT ventilation imaging has been investigated using both animal subjects (Fuld et al 2008,

4D-CT lung ventilation imaging in emphysema patients 2281

Reinhardt et al 2008, Ding et al 2010) and human subjects (Castillo et al 2010, Yamamotoet al 2010). The CT (4D-CT or gated-CT) ventilation was compared with the Xe-CT ventilationusing anesthetized sheep and was found to have reasonable correlations. However, thesestudies suffered from limited axial coverage of Xe-CT scans (Fuld et al 2008, Reinhardtet al 2008, Ding et al 2010). In addition, Guerrero et al (2007) demonstrated a reduction inmass-specific pulmonary compliance following whole lung irradiation in mice using breath-hold CT scans. More recently, Castillo et al (2010) and Yamamoto et al (2010) comparedthe 4D-CT and single photon emission CT (SPECT) ventilation images in thoracic cancerpatients. Castillo et al (2010) reported relatively high Dice similarity coefficients between the4D-CT and SPECT ventilation in low-functional regions, however low similarity overall. Theyclaimed that the low similarity was likely due to central airway depositions of the technetium-99m-labeled diethylenetriamine pentaacetate (99mTc-DTPA) aerosols. Yamamoto et al (2010)also reported low correlations between the 4D-CT and SPECT ventilation (r = 0.18). Theyfound much higher correlations between the 4D-CT ventilation and SPECT perfusion (r =0.48), which is expected to highly correlate with ventilation. Given the lack of data showinghigh correlations for the entire lungs, physiologically accurate 4D-CT ventilation imaging hasnot been achieved and further studies are necessary.

The purpose of this study was to evaluate 4D-CT ventilation imaging in patients withemphysematous lung regions by correlating ventilation with emphysema. In emphysema,there is destruction of alveolar septa leading to decreased elastic recoil of the alveoli andradial traction, which help hold small airways open (Levitzky 2007). Thus, emphysematouslung regions are less ventilated (Zaporozhan et al 2004, Spector et al 2005), and can beused as surrogates for low ventilation (Ley-Zaporozhan et al 2007). We tested the followinghypothesis: 4D-CT ventilation in emphysematous lung regions is significantly lower thanin non-emphysematous lung regions, which is a necessary condition for a physiologicallyaccurate 4D-CT ventilation imaging. Given that there are various DIR algorithms and twoclasses of ventilation metrics that can be used for 4D-CT ventilation imaging, we investigateda total of four combinations of two DIR algorithms and two metrics.

2. Methods and materials

2.1. Patients

This study was a retrospective analysis approved by Stanford University’s Institutional ReviewBoard. We studied 12 patients with emphysematous lung regions as observed on CT, whounderwent 4D-CT scanning as well as radiotherapy for the thoracic cancers. We confirmedthat all the patients had emphysematous lung regions based on the CT quantification describedbelow in section 2.4. The characteristics of the patients are described in table 1. Half ofpatients had stage I non-small-cell lung cancer (NSCLC). Two of the 11 NSCLC patients hadtwo tumors.

2.2. 4D-CT pulmonary ventilation imaging

Figure 1 shows a schematic diagram for creating a ventilation image from 4D-CT andcomparing the ventilation in emphysematous and non-emphysematous lung regions. Four-dimensional CT ventilation imaging consists of three steps as follows. The first step is toacquire 4D-CT images for radiotherapy planning purposes. At Stanford, we routinely acquire4D-CT scans for thoracic and abdominal cancer. Four-dimensional CT images are createdby acquiring oversampled CT data simultaneously with a respiratory trace and reconstructing

2282 T Yamamoto et al

Table 1. Characteristics of the 12-patient cohort.

Parameter Value

Age (y/o), median (range) 76 (62—90)Smoking history (pack-year), median (range) 50 (10–165)Gender, n (%)

Male 8/12 (66.7)Female 4/12 (33.3)

Histology and stage, n (%)NSCLCa, stage I 6/12 (50.0)NSCLCa, stage II 2/12 (16.7)NSCLCa, stage III 3/12 (25.0)Follicular lymphoma, stage IV 1/12 (8.3)

Lung tumor location, n (%)RULb 3/14 (21.4)RLLc 3/14 (21.4)LULd 5/14 (35.7)LLLe 3/14 (21.4)

a Non-small-cell lung cancer.b Right upper lobe.c Right lower lobe.d Left upper lobe.e Left lower lobe.

a number of 3D-CT data sets correlated with a given respiratory phase range (Rietzel et al2005). We acquired 4D-CT scans on the GE Discovery ST multislice positron emission CT(PET)/CT scanner (GE Medical Systems, Waukesha, WI) in cine mode with the Varian Real-time Position Management (RPM) system (Varian Medical Systems, Palo Alto, CA) to recordpatient respiratory traces. The CT data were acquired at multiple couch positions coveringthe entire lung for a cine duration that is a little longer than the estimated respiratory periodat each position, resulting in approximately 15 time-resolved images at each slice position.Scan parameters were set as follows: 120 kVp, approximately 100 mAs per slice, 0.5 s gantryrotation, 0.45 s cine interval and 2.5 mm slice thickness as used clinically in our radiationoncology department. The GE Advantage 4D software was used to create a 4D-CT image setby sorting raw 4D-CT slices correlated with the RPM data into 10 respiratory phase-based bins(i.e. 0–90% at 10% intervals). We used paired 4D-CT images at the peak-exhale (typically50%) and peak-inhale (typically 0%) phases for ventilation computation which were identifiedbased on the dome of diaphragm showing the most superior position and the most inferiorposition, respectively. More details on the 4D-CT acquisition using the GE scanner withAdvantage 4D have been described by Rietzel et al (2005).

The second step of 4D-CT ventilation imaging is DIR for spatial mapping of the peak-exhale CT image to the peak-inhale image, deriving a displacement vector field (DVF).We investigated surface-based registration (DIRsur) (von Berg et al 2007) and volumetricregistration (DIRvol) (Kabus and Lorenz 2010). Kabus et al validated the geometric accuracyof these algorithms by evaluating the distances between landmark positions at two differentphases with and without registration, which were reduced from 6.0 mm to 2.3–2.5 mm onaverage for the four patients (Kabus et al 2008). We also performed validation using a publiclyavailable data set of five cases with 300 landmarks for each (Castillo et al 2009). On average,

4D-CT lung ventilation imaging in emphysema patients 2283

Detectemphysema bydensity masking

Acquire 4D-CT scan

Perform

Create Create Create Create

Perform

Correlate

surJacVsur

HUV

surDIR

volJacVvol

HUV

Compare inemphysematous andnon-emphysematous

lung regions

V

volDIR

Create CreatesurDVF volDVF

Figure 1. A schematic diagram for creating a ventilation image from 4D-CT and comparingthe ventilation in emphysematous and non-emphysematous lung regions. The first step was theacquisition of a 4D-CT image set. The second step was deformable image registration (DIR) forspatial mapping of the peak-exhale CT image to the peak-inhale image, deriving a displacementvector field (DVF). The third step was the creation of a 4D-CT ventilation image through thecomputation of a ventilation metric. Given that there are various DIR algorithms and two classesof ventilation metrics that can be used for 4D-CT ventilation imaging, we studied a total of fourcombinations of two DIR algorithms: surface-based (DIRsur) and volumetric (DIRvol), and twometrics: Hounsfield unit (HU) change (VHU) and Jacobian determinant of deformation (VJac),yielding four ventilation image sets per patient (V sur

HU, V surJac , V vol

HU and V volJac ). The final step was to

correlate resultant 4D-CT ventilation images with emphysema.

the distances were reduced from 3.9–9.8 mm to 1.0–1.7 mm. Given the slice thickness of2.5 mm and an average observer error of 0.7–1.1 mm, both DIRsur and DIRvol are expected toachieve high physiologic accuracy. However, different DIR methods yielded varying DVFs inregions apart from the landmarks despite having similar and small mean landmark registrationerrors overall, which motivated further investigation of these two algorithms in this study.DIRsur matches surface models at the peak-exhale phase (i.e. lung surface along with innerstructures such as vessel trees) to those at the peak-inhale phase, followed by thin plate splineinterpolation to create a dense vector field. Further details on DIRsur have been describedby von Berg et al (2007). DIRvol is volumetric by itself and tries to find a vector field thatminimizes both a similarity function (i.e. the sum of squared difference between the peak-inhale and deformed peak-exhale images) and a regularizing term (i.e. elastic regularizer)based on the Navier–Lame equation. The Navier–Lame equation has two parameters: ! andµ, which can be converted into the parameters better known as Young’s modulus and Poisson’sratio, respectively. Further details on DIRvol has been described by Kabus and Lorenz (2010).We used algorithm parameters identical to those previously investigated (Kabus et al 2008)for both DIRsur and DIRvol. Given that emphysematous lungs have different elastic properties

2284 T Yamamoto et al

than healthy lungs, we studied the effect of elasticity parameter of DIRvol by using threedifferent µ values. The µ value of 0.0025 was used for the baseline elasticity setting (Kabuset al 2008), which was decreased and increased by a factor of four to simulate a more elasticand stiff material, respectively. The ! value was set to 0 to reflect a compressible material inall settings. The elasticity settings were global and the entire lung was set to these parameters.The accuracy of DIR may be improved with regionally different elasticity parameters. Thiswas outside the scope of this study. DIRvol denotes the baseline setting unless otherwisenoted.

The final step of 4D-CT ventilation imaging is the creation of a ventilation image atthe peak-exhale phase through quantitative analysis of the DVF. We investigated HU change(Guerrero et al 2005, 2006, Fuld et al 2008, Kabus et al 2008, Castillo et al 2010, Dinget al 2010, Yamamoto et al 2010) and Jacobian determinant of deformation (Kabus et al2008, Reinhardt et al 2008, Castillo et al 2010, Ding et al 2010, Yamamoto et al 2010),which were the only two classes of ventilation metrics proposed previously. Both metricsare based on the assumption that regional ventilation is proportional to regional volumechange, which is supported by the literature, i.e. the HU metric (Fuld et al 2008) and Jacobianmetric (Reinhardt et al 2008) were found to have reasonable correlations with Xe-CT-measuredregional ventilation in sheep. For the HU metric, Simon (2000) originally derived a relationshipbetween the local change in fractional air content and local volume change, which was adaptedto the relationship between the local HU density change and local volume change by Guerreroet al (2005). The exhale-to-inhale volume change ("Vol) normalized by the exhale air volume(Volair

ex ) in the voxel at location (x, y, z) is given by

"VolVolair

ex (x, y, z)

= 1000HUin{x + ux(x, y, z), y + uy(x, y, z), z + uz(x, y, z)} ! HUex(x, y, z)

HUex(x, y, z)[HUin{x + ux(x, y, z), y + uy(x, y, z), z + uz(x, y, z)} + 1000], (1)

where HU is the HU value and u is the displacement vector mapping the voxel at location(x, y, z) of a peak-exhale image to the corresponding location of a peak-inhale image. Notethat the air and tissue densities were assumed to be !1000 and 0 HU, respectively. To dateequation (1) has been used as a ventilation metric by many investigators (Guerrero et al2006, Fuld et al 2008, Kabus et al 2008, Castillo et al 2010, Ding et al 2010, Yamamoto etal 2010). However, the value independent of the initial air volume was defined as the HUventilation metric (VHU) in this study, considering the findings from a hyperpolarized 3Hestudy by Spector et al (2005). They demonstrated remarkably lower ventilation, which wasdefined as the amount of 3He gas added to a region of interest normalized by the total lungvolume of that region (i.e. proportional to the absolute volume change), in emphysematousrats than in healthy rats. The exhale air volume (Volair

ex ) in the voxel at location (x, y, z) canbe estimated by

Volairex (x, y, z) = !HUex(x, y, z)

1000Volvoxel

ex (x, y, z), (2)

where Volvoxelex is the exhale voxel volume (Hoffman and Ritman 1985). Substitution of

equation (2) into equation (1) yields

"Vol = HUex(x, y, z) ! HUin{x + ux(x, y, z), y + uy(x, y, z), z + uz(x, y, z)}HUin{x + ux(x, y, z), y + uy(x, y, z), z + uz(x, y, z)} + 1000

" Volvoxelex (x, y, z). (3)

4D-CT lung ventilation imaging in emphysema patients 2285

Given that Volvoxelex is the same for all voxels, we defined the HU ventilation metric (VHU) as

VHU(x, y, z) = HUex(x, y, z) ! HUin{x + ux(x, y, z), y + uy(x, y, z), z + uz(x, y, z)}HUin{x + ux(x, y, z), y + uy(x, y, z), z + uz(x, y, z)} + 1000

. (4)

A mass correction was applied to HUin to account for the difference in CT-derived lung masswhich would be due to the changes in blood distribution between exhale and inhale in thesame manner as Guerrero et al (2006). HUex and HUin at the same location of the deformedpeal-exhale and peak-inhale images were used to compute VHU which was mapped back to theoriginal peak-exhale image domain to create a 4D-CT ventilation image. Given that VHU wasbased on HU values and influenced by the statistical noise, the 4D-CT images were smoothedusing an isotropic Gaussian filter kernel before computing VHU. We studied the effect ofsmoothing by using small (variance, # 2 = 1.5 mm2) and large (# 2 = 5 mm2) smoothing levels.VHU denotes the HU metric based on the small smoothing level unless otherwise noted. Forthe Jacobian metric, the Jacobian determinant (J ) of the displacement vector u is given by

J (x, y, z) =

!!!!!!!!!!!!

1 +$ux(x, y, z)

$x

$ux(x, y, z)

$y

$ux(x, y, z)

$z$uy(x, y, z)

$x1 +

$uy(x, y, z)

$y

$uy(x, y, z)

$z$uz(x, y, z)

$x

$uz(x, y, z)

$y1 +

$uz(x, y, z)

$z

!!!!!!!!!!!!

. (5)

The volume of voxel deformed into the inhale phase"Volvoxel

in

#can be estimated by

Volvoxelin = Volvoxel

ex J (x, y, z). (6)

The exhale-to-inhale volume change ("Vol) is expressed as

"Vol = Volvoxelin ! Volvoxel

ex = Volvoxelex {J (x, y, z) ! 1} . (7)

Given that Volvoxelex is the same for all voxels, we defined the Jacobian ventilation metric (VJac)

as

VJac(x, y, z) = J (x, y, z) ! 1. (8)

For both VHU and VJac, a value of zero corresponds to local volume preservation (i.e. zeroventilation). A value smaller than zero indicates local contraction and a value larger than zeroindicates local expansion. The VHU or VJac values outside the segmented lung parenchymavolumes have been zeroed. The lung volume was segmented by delineating lung voxels withHU values less than a threshold of !600 (Shikata et al 2004) or !250 (Guerrero et al 2006,Castillo et al 2010) within the lung outlines generated by the model-based segmentation of thePinnacle3 treatment planning system (Philips Radiation Oncology Systems, Fitchburg, WI).Manual trimming of the central airways and great vessels was also performed where necessary.Castillo et al (2010) used morphological growing to remove the central airways.

2.3. Comparison of the calculated and measured tidal volumes

The absolute tidal volumes calculated by 4D-CT ventilation were compared to those measuredby segmented lung parenchyma volumes (ground truth) to investigate the global accuracy of4D-CT ventilation computation. For the calculated tidal volume, the exhale-to-inhale volumechanges were calculated for all lung parenchyma voxels at the peak-exhale phase based onequations (3) for VHU and (7) for VJac, which were then integrated to determine a tidal volume.For the measured tidal volume, the air volumes in the peak-exhale and peak-inhale lungs wereestimated based on equation (2) and were then subtracted to determine a tidal volume. The

2286 T Yamamoto et al

measured and calculated tidal volumes were determined for 12 emphysema patients. ThePearson’s linear correlation coefficients between the calculated and measured tidal volumeswere determined for four ventilation image sets.

2.4. Emphysema quantification

Emphysematous lung regions were detected by density masking using MATLAB (TheMathWorks, Natick, MA) where CT voxels with HU values less than !910 within the lungswere identified as emphysema. The density masking technique has been validated againstpathology (Hayhurst et al 1984, Muller et al 1988, Gevenois et al 1996, Bankier et al 1999)and PFT (Kinsella et al 1990, Gould et al 1991, Gevenois et al 1996, Haraguchi et al 1998,Park et al 1999, Arakawa et al 2001, Baldi et al 2001). The mean percentage low-attenuationarea below !910 HU (%LAA) for the study subjects was 21.9 ± 15.2%. Also, a metricof global emphysema severity, i.e. the mean 15th percentile HU value below which 15% ofthe lung voxels are distributed (Parr et al 2006, Newell 2008, Parr et al 2008), was !971 ±21 HU. Peak-exhale CT images were used for emphysema quantification, given that exhalehigh-resolution CT (HRCT) scans have been reported to show better correlations with PFTsthan inhale scans (Kauczor et al 2000, 2002, Arakawa et al 2001, Spiropoulos et al 2003,Zaporozhan et al 2005).

2.5. Statistical analysis

Statistical analyses were performed to test whether the 4D-CT ventilation in emphysematouslung regions is significantly lower than in non-emphysematous lung regions (p < 0.05) usingthe one-tailed t-test for the four 4D-CT ventilation image sets

"V sur

HU, V surJac , V vol

HU and V volJac

#.

3. Results

3.1. Differences between two DVFs for the 12-patient cohort

Table 2 shows the mean length of 3D displacement vectors for DVFsur, DVFvol, and thedifference between these two DVFs for 12 patients. For the !600 HU threshold, the vectordifference was 2.0 ± 1.0 mm, i.e. both the mean and SD were smaller than the voxeldimension of the image set. The differences were larger in non-emphysematous regions thanin emphysematous regions, however they were smaller than the voxel dimension. The largerdifferences in non-emphysematous regions were likely due to larger displacement magnitudesthan in emphysematous regions. The !250 HU threshold demonstrated similar results.

3.2. Correlations between the calculated and measured tidal volumes

Figure 2 shows scatter plots for the absolute tidal volumes for the four 4D-CTventilation image sets of 12 patients. There were strong correlations between the VHU-calculated and measured tidal volumes for both !600 HU and !250 HU thresholds(figure 2(a)). The slopes of the least-squares regression lines of the !600 HU thresholdwere closer to unity compared to the !250 HU threshold for both V sur

HU and V volHU. The

differences between the measured and calculated tidal volumes could be due to non-optimalregistration of high-contrast structures (e.g. small pulmonary vessels) and/or uncertainties inlung segmentation. Even spatially small misalignments of high-contrast structures cause oneshadow region with a positive sign and the other region with a negative sign, resulting inextreme erroneous VHU values. This effect can be reduced with accurate lung parenchyma

4D-CT lung ventilation imaging in emphysema patients 2287

0

500

1000

1500

2000

0 500 1000 1500 2000

Cal

cula

ted

abso

lute

tidal

volu

me

(ml)

Measured absolute tidal volume (ml)

surJac-600

volJac-600

surJac-250

volJac-250

(b)

surJacV (-600 HU threshold, r = 0.95)vol

JacV (-600 HU threshold, r = 0.98)sur

JacV (-250 HU threshold, r = 0.99)vol

JacV (-250 HU threshold, r = 0.98)

0

500

1000

1500

2000

0 500 1000 1500 2000

Cal

cula

ted

abso

lute

tidal

volu

me

(ml)

Measured absolute tidal volume (ml)

surHU-600

volHU-600

surHU-250

volHU-250

(a)

surHUV (-600 HU threshold, r = 0.98)vol

HUV (-600 HU threshold, r = 0.98)sur

HUV (-250 HU threshold, r = 0.98)vol

HUV (-250 HU threshold, r = 0.96)

Figure 2. Correlations between the absolute tidal volumes measured by segmented lungparenchyma volumes in the 4D-CT images and those calculated by 4D-CT ventilation derivedby (a) the HU metric (VHU) and (b) the Jacobian metric (VJac) for 12 emphysema patients. Theresults are shown for the two thresholds of lung parenchyma segmentation (!600 and !250 HU).The lines of best fit are also shown: solid (!600 HU) and dashed (!250 HU).

Table 2. Mean length of 3D displacement vectors (mean ± SD, mm) for DVFsur, DVFvol and thedifference between these two DVFs for 12 emphysema patients. The results are shown for the twothresholds of lung parenchyma segmentation (!600 and !250 HU).

Threshold DIR algorithm Total lung Emphysema Non-emphysema

!600 HU DVFsur 7.9 ± 2.3 6.2 ± 2.0 8.4 ± 2.3DVFvol 8.5 ± 2.7 6.4 ± 2.0 9.0 ± 2.8Difference 2.0 ± 1.0 1.4 ± 0.7 2.1 ± 1.1

!250 HU DVFsur 8.2 ± 2.4 6.2 ± 2.0 8.6 ± 2.4DVFvol 8.8 ± 2.8 6.4 ± 2.0 9.4 ± 2.9Difference 2.0 ± 1.1 1.4 ± 0.7 2.2 ± 1.1

segmentation. Visually, the !600 HU threshold resulted in less high-contrast structures inthe segmented volumes than the !250 HU threshold, and hence the effects of non-optimalregistration were considered to be small and the slopes were closer to unity. There werealso strong correlations between the VJac-calculated and measured tidal volumes for both!600 HU and !250 HU thresholds (figure 2(b)). The slopes of the regression lines of the!600 HU threshold were much smaller than unity, while the slopes of the !250 HU thresholdwere close to unity for both V sur

Jac and V volJac . This result was probably due to opposing effects

of threshold values on the VJac-calculated and measured tidal volumes. For the calculatedtidal volumes, the !600 HU threshold masked out more voxels that normally have positiveVJac values and resulted in smaller tidal volumes overall compared to the !250 HU threshold.For the measured tidal volumes, in contrast, the !600 HU threshold gave relatively smallerlung volumes than the !250 HU threshold at peak exhale (average relative volume difference,14 ± 5%) while relatively similar lung volumes at peak inhale (11 ± 3%), resulting in largertidal volumes. The optimal threshold for lung parenchyma segmentation remains an open

2288 T Yamamoto et al

surHUVCT sur

JacVAxial

volHUV vol

JacV

surHUVCT sur

JacVCoronal

(a)

Emphysemaoverlaid

Emphysemanot overlaid

V

volHUV vol

JacV

Emphysemaoverlaid

Emphysemanot overlaid

-1 -0.5 0 0.5 10

0.5

1

1.5

Ventilation

Pro

babi

lity

(b)

Emphysema

Non-emphysema

surHUV

-1 -0.5 0 0.5 10

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Ventilation

Pro

babi

lity

-1 -0.5 0 0.5 10

1

2

3

4

5

VentilationP

roba

bilit

y

volHUV vol

JacV

-1 -0.5 0 0.5 10

0.5

1

1.5

2

2.5

3

3.5

4

Ventilation

Pro

babi

lity

surJacV

Figure 3. (a) Example coronal and axial images at the same level of peak-exhale CT and four4D-CT ventilation (V sur

HU, V surJac , V vol

HU and V volJac ) for patient 3 showing the largest difference between

VHU values in emphysematous and non-emphysematous lung regions. (b) Probability densityfunctions of 4D-CT ventilation in emphysematous and non-emphysematous lung regions are alsoshown. The lung parenchyma volumes were segmented with the !600 HU threshold.

question and is further discussed in section 4. The results for both !600 HU and !250 HUthresholds are presented in this report.

3.3. 4D-CT ventilation in emphysematous versus in non-emphysematous lung regions forexample patients

Figure 3 shows example images of peak-exhale CT and four 4D-CT ventilation for patient 3(90 year old male, %LAA = 11.6%) demonstrating the largest difference between VHU valuesin emphysematous and non-emphysematous lung regions. Visually, different DIR algorithmsand metrics yielded spatially variant 4D-CT ventilation images. There were clearly lesshighly ventilated lung voxels in emphysematous regions (or more highly ventilated lungvoxels in non-emphysematous regions) for V sur

HU and V volHU. In contrast, there were mixtures

of highly and poorly ventilated lung voxels in emphysematous regions for V surJac and V vol

Jacas observed in the axial images. The peaks of the probability density functions of 4D-CTventilation in emphysematous and non-emphysematous lung regions were clearly separatedfrom each other for V sur

HU (mean ventilation, 0.17 ± 1.32 in emphysema versus 0.47 ± 0.60 innon-emphysema) and V vol

HU (0.15 ± 1.12 versus 0.46 ± 0.50), while they almost overlapped

4D-CT lung ventilation imaging in emphysema patients 2289

surHUVCT sur

JacVAxial

volHUV vol

JacV

surHUVCT sur

JacVCoronal

(a)

Emphysemaoverlaid

Emphysemanot overlaid

V

volHUV vol

JacV

Emphysemaoverlaid

Emphysemanot overlaid

-1 -0.5 0 0.5 10

0.5

1

1.5

2

2.5

3

Ventilation

Pro

babi

lity

(b)

Emphysema

Non-emphysema

surHUV

-1 -0.5 0 0.5 10

2

4

6

8

10

Ventilation

Pro

babi

lity

-1 -0.5 0 0.5 10

0.5

1

1.5

2

2.5

3

Ventilation

Pro

babi

lity

surJacV vol

HUV

-1 -0.5 0 0.5 10

2

4

6

8

10

VentilationP

roba

bilit

y

volJacV

Figure 4. (a) Example coronal and axial images at the same level of peak-exhale CT and four 4D-CT ventilation (V sur

HU, V surJac , V vol

HU and V volJac ) for patient 7 showing the smallest difference between

VHU values in emphysematous and non-emphysematous lung regions. (b) Probability densityfunctions of 4D-CT ventilation in emphysematous and non-emphysematous lung regions are alsoshown. The lung parenchyma volumes were segmented with the !600 HU threshold.

each other with no separations for V surJac (0.24 ± 0.23 versus 0.29 ± 0.30) and V vol

Jac (0.26 ±0.17 versus 0.30 ± 0.16) (figure 3(b)). Other example images for patient 7 (62 year old male,%LAA = 7.0%) demonstrating the smallest difference between VHU values in emphysematousand non-emphysematous lung regions are shown in figure 4. Compared to patient 3, therewere relatively more mixtures of highly and poorly ventilated lung voxels in emphysematousregions, though the peaks of the two probability density functions were still separated for V sur

HU(0.23 ± 1.67 versus 0.24 ± 0.23) and V vol

HU (0.19 ± 1.58 versus 0.23 ± 0.19). No separationswere observed for V sur

Jac (0.16 ± 0.06 versus 0.16 ± 0.09) and V volJac (0.19 ± 0.07 versus

0.17 ± 0.09) as were observed in patient 3. For both VHU and VJac (especially VHU), there wereconsiderable probabilities of negative ventilation values that would not, in principle, appearduring inhalation. Although the geometric accuracy of the DIR algorithms used in this studyhas been validated as described above, we cannot rule out that the negative ventilation wascaused by residual errors in the DIR process. In a separate study, significant lung regions withnegative ventilation values were also observed by Christensen et al (2007). However, this stillleaves open the question of DIR algorithm errors.

2290 T Yamamoto et al

Table 3. Mean 4D-CT ventilation values in emphysematous and in non-emphysematous lungregions of 12 patients for the four combinations of two deformable image registration (DIR)algorithms and two ventilation metrics. The results are shown for the two thresholds of lungparenchyma segmentation (!600 and !250 HU).

Ventilation, mean ± SD

Threshold DIR algorithm Metric Emphysema Non-emphysema p-value

!600 HU DIRsur VHU 0.14 ± 0.14 0.29 ± 0.16 0.01VJac 0.15 ± 0.07 0.17 ± 0.08 0.20

DIRvol VHU 0.13 ± 0.13 0.27 ± 0.15 <0.01VJac 0.17 ± 0.08 0.19 ± 0.09 0.30

!250 HU DIRsur VHU 0.13 ± 0.08 0.22 ± 0.12 0.03VJac 0.15 ± 0.07 0.18 ± 0.08 0.19

DIRvol VHU 0.13 ± 0.07 0.18 ± 0.08 0.06VJac 0.17 ± 0.08 0.18 ± 0.09 0.36

3.4. 4D-CT ventilation in emphysematous versus in non-emphysematous lung regions for the12-patient cohort

Table 3 shows a summary of the mean 4D-CT ventilation values in emphysematous andnon-emphysematous lung regions for the 12 patients. Different DIR algorithms and metricsyielded variant ventilation values. The mean ventilation values in emphysematous regionswere consistently lower than in non-emphysematous regions for any combination of DIRalgorithms and ventilation metrics for both !600 HU and !250 HU thresholds. However,the differences were statistically significant only for VHU and not for VJac. VHU resulted insignificantly lower ventilation in emphysematous regions than in non-emphysematous regionsfor both DIRsur (p = 0.01) and DIRvol (p < 0.01) for the !600 HU threshold. However,VJac resulted in non-significant differences for both DIRsur (p = 0.20) and DIRvol (p = 0.30).The !250 HU threshold demonstrated similar results, except for VHU which resulted innon-significant differences for DIRvol (p = 0.06) due to smaller mean VHU values in non-emphysematous regions than the !600 HU threshold. Aligned high-contrast structures givesmall VHU values, and hence are considered to lower average values for the !250 HU thresholdcompared to the !600 HU threshold. The elasticity parameter of DIRvol did not show a clearimpact on the statistical significance as shown in table 4, except for VHU for the !250 HUthreshold. The p-values decreased with decreasing elasticity, which was likely driven by non-optimal registration of high-contrast structures. Misalignments of high-contrast structuresresult in extremely high VHU values and contribute to increasing the average values. Theimage registration with the elastic setting is expected to have more freedom to deform animage than the stiff setting such that the sum of squared difference becomes smaller. Thestiff setting might result in more misalignments of high-contrast structures, i.e. high averageVHU values, than the elastic setting. The volumes segmented with the !600 HU thresholdcontained less high-contrast structures than the !250 HU threshold, and hence the effect ofelasticity parameter was relatively small. For the !600 HU threshold, the mean VHU values innon-emphysematous regions increased from 0.25 for the elastic setting to 0.27 for the baselinesetting and to 0.29 for the stiff setting. For the !250 HU threshold, the mean values were0.15, 0.18 and 0.21, respectively. The image smoothing level of VHU had a clear impact onthe statistical significance as shown in table 5. The large smoothing level (# 2 = 5 mm2) ledto statistically non-significant differences. The large smoothing level resulted in larger mean

4D-CT lung ventilation imaging in emphysema patients 2291

Table 4. Mean 4D-CT ventilation values in emphysematous and in non-emphysematous lungregions of 12 patients for the four combinations of volumetric deformable image registration(DIRvol) with three elasticity settings and two ventilation metrics. The results are shown for thetwo thresholds of lung parenchyma segmentation (!600 and !250 HU).

Ventilation, mean ± SD

Threshold Elasticity setting Metric Emphysema Non-emphysema p-value

!600 HU Elastica VHU 0.12 ± 0.12 0.25 ± 0.13 <0.01VJac 0.19 ± 0.10 0.20 ± 0.10 0.37

Baselineb VHU 0.13 ± 0.13 0.27 ± 0.15 <0.01VJac 0.17 ± 0.08 0.19 ± 0.09 0.30

Stiffc VHU 0.12 ± 0.13 0.29 ± 0.15 <0.01VJac 0.15 ± 0.07 0.17 ± 0.07 0.33

!250 HU Elastica VHU 0.11 ± 0.06 0.15 ± 0.07 0.07VJac 0.19 ± 0.10 0.18 ± 0.09 0.53

Baselineb VHU 0.13 ± 0.07 0.18 ± 0.08 0.06VJac 0.17 ± 0.08 0.18 ± 0.09 0.36

Stiffc VHU 0.13 ± 0.08 0.21 ± 0.10 0.02VJac 0.15 ± 0.07 0.16 ± 0.07 0.35

a Elastic setting with µ = 0.0025/4 and ! = 0.b Baseline setting with µ = 0.0025 and ! = 0.c Stiff setting with µ = 0.0025 " 4 and ! = 0.

Table 5. Mean 4D-CT ventilation values in emphysematous and in non-emphysematous lungregions of 12 patients for the four combinations of two deformable image registration (DIR)algorithms and the HU metric with two smoothing levels. The results are shown for the twothresholds of lung parenchyma segmentation (!600 and !250 HU).

Ventilation, mean ± SD

Threshold DIR algorithm Smoothing Emphysema Non-emphysema p-value

!600 HU DIRsur Smalla 0.14 ± 0.14 0.29 ± 0.16 0.01Largeb 0.19 ± 0.14 0.25 ± 0.15 0.15

DIRvol Smalla 0.13 ± 0.13 0.27 ± 0.15 <0.01Largeb 0.18 ± 0.14 0.24 ± 0.14 0.16

!250 HU DIRsur Smalla 0.13 ± 0.08 0.22 ± 0.12 0.03Largeb 0.16 ± 0.08 0.20 ± 0.10 0.17

DIRvol Smalla 0.13 ± 0.07 0.18 ± 0.08 0.06Largeb 0.15 ± 0.07 0.16 ± 0.07 0.34

a Gaussian filter kernel with the smaller variance, # 2 = 1.5 mm2.b Gaussian filter kernel with the larger variance, # 2 = 5 mm2.

ventilation values than the small smoothing level (# 2 = 1.5 mm2) in emphysematous regions,however resulted in smaller mean ventilation values in non-emphysematous regions for bothDIRsur and DIRvol. Nevertheless, the mean ventilation values in emphysematous regions werestill lower than in non-emphysematous regions.

2292 T Yamamoto et al

4. Discussion

Four-dimensional CT ventilation imaging was evaluated by correlating ventilation withemphysematous lung regions, which are less ventilated and were used as surrogates forlow ventilation, in a 12-patient study. A total of four combinations of two DIR algorithms(DIRsur and DIRvol) and two metrics (VHU and VJac) were investigated. VHU was found tobe significantly lower in emphysematous regions than in non-emphysematous regions. VJac

was also found to be lower in emphysematous regions than in non-emphysematous regions;however, the differences were not significant. Recently, Castillo et al (2010) demonstratedsignificantly higher Dice similarity coefficients between the HU-based 4D-CT ventilation andthe SPECT ventilation than those between the Jacobian-based 4D-CT and SPECT ventilationfor seven patients (p < 10!4), which is consistent with our results. Reinhardt et al (2008)found reasonably high correlations (average, R2 = 0.73) between the average ventilation valuesin 4 mm thick sub-regions determined by the Jacobian-based 4D-CT and Xe-CT ventilationfor five anesthetized sheep. They might have obtained higher correlations in the HU-basedventilation than the Jacobian-based ventilation if both metrics were evaluated, as with ourfindings. More recently, Ding et al (2010) proposed a hybrid ventilation metric combiningthe HU and Jacobian metrics and demonstrated consistently higher correlations with the Xe-CT ventilation than the HU-based ventilation for three anesthetized sheep. The hybrid metricmight result in higher correlations than VHU and/or VJac in our study as well. Both VHU and VJacrepresent the ventilation values independent of the initial air volume and, in principle, shouldachieve a similar performance to each other. Differences between the two metrics mightbe due to (1) residual DIR errors, (2) 4D-CT image noise, (3) 4D-CT artifacts and/or (4)inhomogeneous changes in the blood distribution during respiration. Given that an agreementbetween the two metrics means that the volume change estimated from the HU density changebetween the voxels at the peak-exhale and peak-inhale phases agrees with that estimated fromthe displacement vectors in and around the corresponding voxel, residual DIR errors wouldresult in disagreements between the two metrics. Even spatially small DIR errors could resultin large disagreements. A general discussion point is that the accuracy of 4D-CT ventilationimaging will always be limited by that of the DIR algorithms, which in turn are very difficult toquantify for individual cases or even away from landmarks in validation studies. It is thereforedifficult to identify a specific cause of the disagreements between the two metrics. However,improvements in DIR algorithm performance are likely to improve the quality of ventilationimages for future studies. For the 4D-CT image noise, the smoothing level for VHU had aconsiderable impact on the statistical significance of the differences in ventilation betweenemphysematous and non-emphysematous regions, though the ventilation in emphysematousregions was consistently lower than in non-emphysematous regions for both smoothing levels.The optimal smoothing level remains an open question and will be investigated in a futurestudy. The 4D-CT artifacts may also influence ventilation computation and the effect may bedifferent between the two metrics. Possible impact of 4D-CT artifacts are discussed in moredetail below. A mass correction was applied in the VHU calculation to account for the differencein CT-derived lung mass due to respiration, where we assumed homogeneous changes in theblood distribution. However, the distribution may be inhomogeneous and could influence theVHU calculation.

The DIR algorithms have been found to have a small impact on the statistical significanceof the differences in ventilation between emphysematous and non-emphysematous regions,which is most likely due to similar performance of DIRsur and DIRvol. The differences betweenDVFsur and DVFvol were smaller than the voxel dimension of the image set on average. Kabuset al (2009) demonstrated relatively remarkable differences in DVFs between DIRvol and three

4D-CT lung ventilation imaging in emphysema patients 2293

other volumetric DIR algorithms compared to the differences between DIRvol and DIRsur or theother volumetric DIR algorithm for one case, despite having similar and small mean landmarkregistration errors ranging from 1.0 to 1.4 mm for all of these algorithms. It should be notedthat the two algorithms were chosen in the current study independently of these results andthere were still considerable differences between DVFsur and DIRvol. Nevertheless, there maybe larger impact of DIR algorithms if another DIR algorithm was used in the current study.The elasticity parameter of DIRvol has been found to have considerable impact for VHU onlywhen using the !250 HU threshold, which is likely attributed to non-optimal registration ofhigh-contrast structures.

In principle, only lung parenchyma volumes should be included in the analysis ofventilation imaging. The lung parenchyma volumes were segmented by thresholding voxels ateither !600 or !250 HU within the lung outlines generated by the model-based segmentationin this study. High-contrast structures (e.g. small vessels) would still remain in the segmentedvolumes mostly because of partial volume effects. Such structures cause erroneous VHU values.Visually, the !600 HU threshold resulted in less high-contrast structures (e.g. small vessels)in the segmented volumes than the !250 HU threshold. Several discrepancies in the resultsbetween !600 and !250 HU thresholds were most likely due to high-contrast structures asdescribed above in section 3.4. It is difficult to segment lung parenchyma accurately, given thatCT lung density is influenced by factors including subject tissue volume, air volume, physicalmaterial properties of the lung parenchyma, transpulmonary pressure, and image acquisitionprotocol. In particular, optimal thresholds vary by subject as demonstrated by Hu et al (2001).Improvements in lung parenchyma segmentation and also HRCT imaging are likely to makethe analyses more accurate for future studies.

There are several limitations in 4D-CT ventilation imaging, including the artifacts in4D-CT images and a potential bias in V vol

HU. Artifacts are observed in the 4D-CT images at analarmingly high frequency (i.e. 90%) (Yamamoto et al 2008). Several artifacts were observedin the images used in this study. As artifacts of non-lung structures (e.g. diaphragm) havehigh-contrast, they are masked out from the lung parenchyma volumes after thresholding.There should be artifacts in the lung volumes as well; however, it is difficult to segmentthem because of low-contrast. Future studies will investigate the possible impact of 4D-CTartifacts on regional ventilation computation, possibly by focusing on the CT data segmentswhere artifacts of non-lung structures occur. Given that DIRvol tries to minimize the sumof squared difference (see section 2.2) which is essentially the same metric as VHU, V vol

HUmay underestimate the actual HU change, which is considered as a limitation. However, thisbias would be small because DIRvol is based on not only the sum of squared difference, butalso a regularizing term (i.e. elastic regularizer). Other similarity functions including mutualinformation may avoid this issue. Furthermore, we used the 4D-CT images at the peak-exhaleand peak-inhale phases only in this study. Guerrero et al (2006) and Christensen et al (2007)calculated ventilation images with different pairs of 4D-CT images, i.e. each phase pairedwith the peak-exhale phase (Guerrero et al 2006) or adjacent phase (Christensen et al 2007).Both studies demonstrated spatially varying distributions of regional ventilation, indicatingthat regional ventilation is not necessarily constant during a breathing period and may varywith phase. Considering the lung pressure–volume hysteresis, integrating the ventilationcalculated from all 4D-CT images over a full respiratory cycle would more appropriatelyrepresent breathing process and may give more physiologically relevant ventilation images fora future study.

In this study, the density masking technique with the threshold value of !910 HU wasused to detect emphysema in 4D-CT images acquired with the following scanning parameters:120 kV, #100 mAs and 2.5 mm slice thickness as used clinically in our radiation oncology

2294 T Yamamoto et al

department. The justification for the use of these parameters was that, if ventilation on theseimage sets proved physiologically accurate, the current practice would not need to be changedto use the image sets for functional avoidance in radiotherapy treatment planning. However,the density masking technique was investigated mostly based on HRCT images acquiredby a helical scan during a single breath-hold at peak exhale or peak inhale. Large clinicalCOPD studies such as the evaluation of copd longitudinally to identify predictive surrogateendpoints (ECLIPSE) study (Vestbo et al 2008) and feasibility of retinoids for the treatment ofemphysema (FORTE) study (Roth et al 2006) were also based on breath-hold HRCT images.For example, HRCT scans were acquired with 120 kV, 40 mA and 1.00 or 1.25 mm slicethickness during a breath-hold at full inhalation in the ECLIPSE study (Vestbo et al 2008).There were several major differences in the CT acquisition methods between this study andother HRCT studies, including the slice thickness and breathing during a scan. The slicethickness used in this study (2.5 mm) was significantly larger than the slice thickness used inlarge clinical COPD studies or recent HRCT studies (!1 mm) (Bankier et al 1999, Baldi et al2001, Zaporozhan et al 2005, Camiciottoli et al 2006). Madani et al (2007) demonstrated thatdensity masking was significantly influenced by slice thickness. The criterion for emphysemaon the HU scale depends on the slice thickness (Friedman 2008, Lynch and Newell 2009).Until recently, !910 HU used in this study was the most frequently used threshold valuefor density masking (Muller et al 1988, Friedman 2008). More recently, lower thresholdvalues (e.g. !950 HU) have been used for thinner slice thicknesses !1 mm (Bankier et al1999, Baldi et al 2001, Zaporozhan et al 2005, Camiciottoli et al 2006). Moreover, as yetthere is no clear consensus on the threshold value. We considered that using !910 HU for2.5 mm slice thickness was reasonable for density masking. For breathing, 4D-CT scanswere acquired during free breathing without any patient respiratory control (breathing trainingor breath-hold), which were subsequently sorted by correlating with the respiratory signalinto 10 respiratory phase-based bins, while HRCT scans are normally acquired during abreath-hold. Only the peak-exhale CT images (typically 50%) were used for emphysemaquantification. We assumed that the respiratory status at peak exhale during a 4D-CT scanwas comparable to the respiratory status during a breath-hold HRCT scan, and hence therewas no significant impact on emphysema quantification.

In this study, we used emphysematous lung regions as surrogates for low ventilationas suggested by Ley-Zaporozhan et al (2007). There is an uncertainty in the relationshipbetween ventilation and emphysema as reflected by several conflicting results in the literature.There is destruction of alveolar septa in emphysema, leading to decreased elastic recoil of thealveoli and less radial traction, and hence low ventilation (Zaporozhan et al 2004, Spectoret al 2005). Zaporozhan et al (2004) showed that emphysema was strongly associated witha reduction of ventilation based on hyperpolarized 3He MR imaging. Morrell et al (1994)showed increased collateral ventilation (i.e. ventilation of alveolar structures through passagesor channels that bypass the normal airways) by a factor of 10 in emphysema comparedto normal volunteers. However, ventilation was still four times less than that in lungsshowing normal ventilation. Furthermore, Spector et al (2005) demonstrated remarkablylower ventilation in emphysematous rats than in healthy rats with clearly separated peaks ofthe probability density functions of ventilation using hyperpolarized 3He MR imaging. Whilethey compared ventilation in the whole lungs of emphysematous and healthy rats rather than insegmented emphysematous and non-emphysematous lung regions, their results were similarto our results. They also observed large overlaps between the probability density functionsfor emphysematous and healthy rats, similarly to our VHU results. In contrast, Johansson et al(2004) found poor relationships between ventilation and emphysema in 6 of 20 patients, while13 patients showed significant relationships. They compared the 2D ventilation scintigrams

4D-CT lung ventilation imaging in emphysema patients 2295

and 3D-CT scans (used for emphysema quantification), which could have led to inaccuratealignment between the two images and could explain the poor relationships as discussedby the authors. In addition to the uncertainty in the relationship between ventilation andemphysema, we cannot rule out other possible causes of low ventilation in the study patients,including airway narrowing (Nakano et al 2000), small-airway diseases (Gelb et al 1998),chronic persistent asthma (Gelb and Zamel 2000) or airway obstruction due to a tumor.The density masking-defined non-emphysematous lung regions might have been affected bythese causes and might have low ventilation. However, these possible effects may be small,considering a larger volume of non-emphysematous lung (i.e. emphysematous regions: themean%LAA = 21.9 ± 15.2%). Emphysematous lung regions are still expected to havelower ventilation on average compared with non-emphysematous regions. It is difficult toevaluate the small airway narrowing in the 4D-CT images (2.5 mm thickness), given thatLynch and Newell (2009) recommended a submillimeter slice thickness for evaluation ofairway narrowing. The hypothesis tested in this study was a necessary condition, but nota sufficient condition, for physiologically accurate 4D-CT ventilation imaging. A furtherstudy is necessary to investigate the physiologic accuracy. Our future studies will focuson the comparison with the SPECT ventilation (assumed ground truth) and optimization ofthe algorithm parameters to determine an appropriate class of DIR algorithm with optimalparameters as well as appropriate ventilation metric. This comparison also has limitationsincluding central airway depositions of aerosol particles (Magnant et al 2006, Castillo et al2010, Yamamoto et al 2010), temporal differences between 4D-CT and SPECT scans whichcould lead to image registration uncertainties (i.e. misalignment) and temporal changes inventilation itself. This study was based on the same data set acquired at the same time,and hence has advantages over the SPECT comparisons, i.e. no registration uncertainty andtemporal change.

5. Conclusion

This study has demonstrated the correlation between the 4D-CT ventilation and emphysemafor 12 patients. The correlation was sensitive to the ventilation metric, and the HU metric wasfound to have a higher correlation than the Jacobian metric. Significantly lower ventilation inemphysematous lung regions than in non-emphysematous regions indicates the potential forHU-based 4D-CT ventilation imaging to achieve high physiologic accuracy. A further studyis needed to confirm these results.

Acknowledgments

Drs Ann Weinacker, Glenn Rosen and Ann Leung at Stanford provided advice and informationduring the design of this study. Julie Baz from the University of Sydney carefully reviewedand improved the clarity of this manuscript.

References

Albert M S, Cates G D, Driehuys B, Happer W, Saam B, Springer C S Jr and Wishnia A 1994 Biological magneticresonance imaging using laser-polarized 129Xe Nature 370 199–201

Alderson P O and Line B R 1980 Scintigraphic evaluation of regional pulmonary ventilation Semin. Nucl.Med. 10 218–42

Arakawa A, Yamashita Y, Nakayama Y, Kadota M, Korogi H, Kawano O, Matsumoto M and Takahashi M 2001Assessment of lung volumes in pulmonary emphysema using multidetector helical CT: comparison withpulmonary function tests Comput. Med. Imaging Graph. 25 399–404

2296 T Yamamoto et al

Baldi S, Miniati M, Bellina C R, Battolla L, Catapano G, Begliomini E, Giustini D and Giuntini C 2001 Relationshipbetween extent of pulmonary emphysema by high-resolution computed tomography and lung elastic recoil inpatients with chronic obstructive pulmonary disease Am. J. Respir. Crit. Care Med. 164 585–9

Bankier A A, De Maertelaer V, Keyzer C and Gevenois P A 1999 Pulmonary emphysema: subjective visual gradingversus objective quantification with macroscopic morphometry and thin-section CT densitometry Radiology211 851–8

Brock K K 2009 Results of a multi-institution deformable registration accuracy study (MIDRAS) Int. J. Radiat.Oncol. Biol. Phys. 76 583–96

Camiciottoli G, Bartolucci M, Maluccio N M, Moroni C, Mascalchi M, Giuntini C and Pistolesi M 2006Spirometrically gated high-resolution CT findings in COPD: lung attenuation vs lung function and dyspneaseverity Chest 129 558–64

Castillo R, Castillo E, Guerra R, Johnson V E, McPhail T, Garg A K and Guerrero T 2009 A framework for evaluationof deformable image registration spatial accuracy using large landmark point sets Phys. Med. Biol. 54 1849–70

Castillo R, Castillo E, Martinez J and Guerrero T 2010 Ventilation from four-dimensional computed tomography:density versus Jacobian methods Phys. Med. Biol. 55 4661–85

Christensen G E, Song J H, Lu W, El Naqa I and Low D A 2007 Tracking lung tissue motion andexpansion/compression with inverse consistent image registration and spirometry Med. Phys. 34 2155–63

Christian J A, Partridge M, Nioutsikou E, Cook G, McNair H A, Cronin B, Courbon F, Bedford J L and Brada M2005 The incorporation of SPECT functional lung imaging into inverse radiotherapy planning for non-smallcell lung cancer Radiother. Oncol. 77 271–7

de Jong P A, Lindblad A, Rubin L, Hop W C, de Jongste J C, Brink M and Tiddens H A 2006 Progression oflung disease on computed tomography and pulmonary function tests in children and adults with cystic fibrosisThorax 61 80–5

de Lange E E, Altes T A, Patrie J T, Gaare J D, Knake J J, Mugler J P 3rd and Platts-Mills T A 2006 Evaluation ofasthma with hyperpolarized helium-3 MRI: correlation with clinical severity and spirometry Chest 130 1055–62

Ding K, Cao K, Amelon R E, Christensen G E, Raghavan M L and Reinhardt J M 2010 Comparison of intensity- andJacobian-based estimates of lung regional ventilation Proc. Third Int. Workshop on Pulmonary Image Analysis,MICCAI 2010 pp 49–60

Friedman P J 2008 Imaging studies in emphysema Proc. Am. Thorac. Soc. 5 494–500Fuld M K, Easley R B, Saba O I, Chon D, Reinhardt J M, Hoffman E A and Simon B A 2008 CT-measured regional

specific volume change reflects regional ventilation in supine sheep J. Appl. Physiol. 104 1177–84Gelb A F and Zamel N 2000 Unsuspected pseudophysiologic emphysema in chronic persistent asthma Am. J. Respir.

Crit. Care Med. 162 1778–82Gelb A F, Zamel N, Hogg J C, Muller N L and Schein M J 1998 Pseudophysiologic emphysema resulting from severe

small-airways disease Am. J. Respir. Crit. Care Med. 158 815–9Gevenois P A, De Vuyst P, de Maertelaer V, Zanen J, Jacobovitz D, Cosio M G and Yernault J C 1996 Comparison

of computed density and microscopic morphometry in pulmonary emphysema Am. J. Respir. Crit. Care Med.154 187–92

Gould G A, Redpath A T, Ryan M, Warren P M, Best J J, Flenley D C and MacNee W 1991 Lung CT densitycorrelates with measurements of airflow limitation and the diffusing capacity Eur. Respir. J. 4 141–6

Guerrero T, Castillo R, Noyola-Martinez J, Torres M, Zhou X, Guerra R, Cody D, Komaki R and Travis E 2007Reduction of pulmonary compliance found with high-resolution computed tomography in irradiated mice Int.J. Radiat. Oncol. Biol. Phys. 67 879–87

Guerrero T, Sanders K, Castillo E, Zhang Y, Bidaut L, Pan T and Komaki R 2006 Dynamic ventilation imaging fromfour-dimensional computed tomography Phys. Med. Biol. 51 777–91

Guerrero T, Sanders K, Noyola-Martinez J, Castillo E, Zhang Y, Tapia R, Guerra R, Borghero Y and Komaki R 2005Quantification of regional ventilation from treatment planning CT Int. J. Radiat. Oncol. Biol. Phys. 62 630–4

Gur D, Drayer B P, Borovetz H S, Griffith B P, Hardesty R L and Wolfson S K 1979 Dynamic computed tomographyof the lung: regional ventilation measurements J. Comput. Assist. Tomogr. 3 749–53

Gur D, Shabason L, Borovetz H S, Herbert D L, Reece G J, Kennedy W H and Serago C 1981 Regional pulmonaryventilation measurements by xenon enhanced dynamic computed tomography: an update J. Comput. Assist.Tomogr. 5 678–83

Haraguchi M, Shimura S, Hida W and Shirato K 1998 Pulmonary function and regional distribution of emphysemaas determined by high-resolution computed tomography Respiration 65 125–9

Harris R S and Schuster D P 2007 Visualizing lung function with positron emission tomography J. Appl.Physiol. 102 448–58

Hayhurst M D, MacNee W, Flenley D C, Wright D, McLean A, Lamb D, Wightman A J and Best J 1984 Diagnosisof pulmonary emphysema by computerised tomography Lancet 2 320–2

4D-CT lung ventilation imaging in emphysema patients 2297

Hoffman E A and Ritman E L 1985 Effect of body orientation on regional lung expansion in dog and sloth J. Appl.Physiol. 59 481–91

Hu S, Hoffman E A and Reinhardt J M 2001 Automatic lung segmentation for accurate quantitation of volumetricx-ray CT images IEEE Trans. Med. Imaging 20 490–8

Johansson A, Vikgren J, Moonen M, Tylen U and Bake B 2004 Regional ventilation and distribution of emphysema—aquantitative comparison Clin. Physiol. Funct. Imaging 24 58–64

Kabus S, Klinder T, Murphy K, van Ginneken B, Lorenz C and Pluim J P W 2009 Evaluation of 4D-CT lungregistration Proc. MICCAI 2009 ed G Z Yang, D J Hawkes, D Rueckert, J A Noble and C J Taylor (London:Springer) pp 747–54

Kabus S and Lorenz C 2010 Fast elastic image registration Proc. Medical Image Analysis For The Clinic—A GrandChallenge, MICCAI 2010 pp 81–9

Kabus S, von Berg J, Yamamoto T, Opfer R and Keall P J 2008 Lung ventilation estimation based on 4D-CT imagingProc. First International Workshop on Pulmonary Image Analysis, MICCAI 2008 pp 73–81

Kashani R et al 2008 Objective assessment of deformable image registration in radiotherapy: a multi-institution studyMed. Phys. 35 5944–53

Kauczor H, Surkau R and Roberts T 1998 MRI using hyperpolarized noble gases Eur. Radiol. 8 820–7Kauczor H U, Hast J, Heussel C P, Schlegel J, Mildenberger P and Thelen M 2000 Focal airtrapping at expiratory

high-resolution CT: comparison with pulmonary function tests Eur. Radiol. 10 1539–46Kauczor H U, Hast J, Heussel C P, Schlegel J, Mildenberger P and Thelen M 2002 CT attenuation of paired HRCT

scans obtained at full inspiratory/expiratory position: comparison with pulmonary function tests Eur. Radiol.12 2757–63

Kinsella M, Muller N L, Abboud R T, Morrison N J and DyBuncio A 1990 Quantitation of emphysema by computedtomography using a “density mask” program and correlation with pulmonary function tests Chest 97 315–21

Levitzky M G 2007 Pulmonary Physiology (New York: McGraw-Hill)Ley-Zaporozhan J, Ley S, Eberhardt R, Weinheimer O, Fink C, Puderbach M, Eichinger M, Herth F and Kauczor H U

2007 Assessment of the relationship between lung parenchymal destruction and impaired pulmonary perfusionon a lobar level in patients with emphysema Eur. J. Radiol. 63 76–83

Lynch D A and Newell J D 2009 Quantitative imaging of COPD J. Thorac. Imaging 24 189–94Madani A, De Maertelaer V, Zanen J and Gevenois P A 2007 Pulmonary emphysema: radiation dose and section

thickness at multidetector CT quantification—comparison with macroscopic and microscopic morphometryRadiology 243 250–7

Magnant J, Vecellio L, de Monte M, Grimbert D, Valat C, Boissinot E, Guilloteau D, Lemarie E and Diot P2006 Comparative analysis of different scintigraphic approaches to assess pulmonary ventilation J. AerosolMed. 19 148–59

Marcucci C, Nyhan D and Simon B A 2001 Distribution of pulmonary ventilation using Xe-enhanced computedtomography in prone and supine dogs J. Appl. Physiol. 90 421–30

Marks L B, Spencer D P, Sherouse G W, Bentel G, Clough R, Vann K, Jaszczak R, Coleman R E andProsnitz L R 1995 The role of three dimensional functional lung imaging in radiation treatment planning:the functional dose–volume histogram Int. J. Radiat. Oncol. Biol. Phys. 33 65–75

McGuire S M, Zhou S, Marks L B, Dewhirst M, Yin F F and Das S K 2006 A methodology for using SPECTto reduce intensity-modulated radiation therapy (IMRT) dose to functioning lung Int. J. Radiat. Oncol. Biol.Phys. 66 1543–52

McMahon C J, Dodd J D, Hill C, Woodhouse N, Wild J M, Fichele S, Gallagher C G, Skehan S J, van Beek E Jand Masterson J B 2006 Hyperpolarized 3helium magnetic resonance ventilation imaging of the lung in cysticfibrosis: comparison with high resolution CT and spirometry Eur. Radiol. 16 2483–90

Morrell N W, Wignall B K, Biggs T and Seed W A 1994 Collateral ventilation and gas exchange in emphysema Am.J. Respir. Crit. Care Med. 150 635–41

Muller N L, Staples C A, Miller R R and Abboud R T 1988 ‘Density mask’. An objective method to quantitateemphysema using computed tomography Chest 94 782–7

Nakano Y et al 2000 Computed tomographic measurements of airway dimensions and emphysema in smokers.Correlation with lung function Am. J. Respir. Crit. Care Med. 162 1102–8

Newell J D Jr 2008 Quantitative computed tomography of lung parenchyma in chronic obstructive pulmonary disease:an overview Proc. Am. Thorac. Soc. 5 915–8

Park K J, Bergin C J and Clausen J L 1999 Quantitation of emphysema with three-dimensional CT densitometry:comparison with two-dimensional analysis, visual emphysema scores, and pulmonary function test resultsRadiology 211 541–7

Parr D G, Sevenoaks M, Deng C, Stoel B C and Stockley R A 2008 Detection of emphysema progression in alpha1-antitrypsin deficiency using CT densitometry; methodological advances Respir. Res. 9 21

2298 T Yamamoto et al

Parr D G, Stoel B C, Stolk J and Stockley R A 2006 Validation of computed tomographic lung densitometry formonitoring emphysema in alpha1-antitrypsin deficiency Thorax 61 485–90

Reinhardt J M, Ding K, Cao K, Christensen G E, Hoffman E A and Bodas S V 2008 Registration-based estimates oflocal lung tissue expansion compared to xenon CT measures of specific ventilation Med. Image Anal. 12 752–63

Rietzel E, Pan T and Chen G T 2005 Four-dimensional computed tomography: image formation and clinical protocolMed. Phys. 32 874–89

Roth M D et al 2006 Feasibility of retinoids for the treatment of emphysema study Chest 130 1334–45Seppenwoolde Y et al 2002 Optimizing radiation treatment plans for lung cancer using lung perfusion information

Radiother. Oncol. 63 165–77Shikata H, Hoffman E A and Sonka M 2004 Automated segmentation of pulmonary vascular tree from 3D CT images

Proc. SPIE 5369 107–16Shioyama Y et al 2007 Preserving functional lung using perfusion imaging and intensity-modulated radiation therapy

for advanced-stage non-small cell lung cancer Int. J. Radiat. Oncol. Biol. Phys. 68 1349–58Simon B A 2000 Non-invasive imaging of regional lung function using x-ray computed tomography J. Clin. Monit.

Comput. 16 433–42Spector Z Z et al 2005 Quantitative assessment of emphysema using hyperpolarized 3He magnetic resonance imaging

Magn. Reson. Med. 53 1341–6Spiropoulos K, Trakada G, Kalamboka D, Kalogeropoulou C, Petsas T, Efremidis G, Tsiamita M, Trakada A

and Dimopoulos I 2003 Can high resolution computed tomography predict lung function in patients withchronic obstructive pulmonary disease? Lung 181 169–81

Suga K 2002 Technical and analytical advances in pulmonary ventilation SPECT with xenon-133 gas and Tc-99m-Technegas Ann. Nucl. Med. 16 303–10

Swift A J, Wild J M, Fichele S, Woodhouse N, Fleming S, Waterhouse J, Lawson R A, Paley M N andVan Beek E J 2005 Emphysematous changes and normal variation in smokers and COPD patients usingdiffusion 3He MRI Eur. J. Radiol. 54 352–8

Tajik J K, Chon D, Won C, Tran B Q and Hoffman E A 2002 Subsecond multisection CT of regional pulmonaryventilation Acad. Radiol. 9 130–46

Vestbo J et al 2008 Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE) Eur.Respir. J. 31 869–73

Vik T, Kabus S, von Berg J, Ens K, Dries S, Klinder T and Lorenz C 2008 Validation and comparison of registrationmethods for free-breathing 4D lung CT Proc. SPIE 69142 1–10

von Berg J, Barschdorf H, Blaffert T, Kabus S and Lorenz C 2007 Surface based cardiac and respiratory motionextraction for pulmonary structures from multi-phase CT Proc. SPIE 6511 65110Y

Yamamoto T, Kabus S, von Berg J, Lorenz C, Goris M L, Loo B W Jr and Keall P J 2010 Evaluation of four-dimensional(4D) computed tomography (CT) pulmonary ventilation imaging by comparison with single photon emissioncomputed tomography (SPECT) scans for a lung cancer patient Proc. 3rd Int. Workshop on Pulmonary ImageAnalysis, MICCAI 2010 pp 117–28

Yamamoto T, Kabus S, von Berg J, Lorenz C and Keall P J 2011 Impact of four-dimensional computed tomographypulmonary ventilation imaging-based functional avoidance for lung cancer radiotherapy Int. J. Radiat. Oncol.Biol. Phys. 79 279–88

Yamamoto T, Langner U, Loo B W Jr, Shen J and Keall P J 2008 Retrospective analysis of artifacts in four-dimensionalCT images of 50 abdominal and thoracic radiotherapy patients Int. J. Radiat. Oncol. Biol. Phys. 72 1250–8

Yaremko B P, Guerrero T M, Noyola-Martinez J, Guerra R, Lege D G, Nguyen L T, Balter P A, Cox J D and KomakiR 2007 Reduction of normal lung irradiation in locally advanced non-small-cell lung cancer patients, usingventilation images for functional avoidance Int. J. Radiat. Oncol. Biol. Phys. 68 562–71

Zaporozhan J, Ley S, Eberhardt R, Weinheimer O, Iliyushenko S, Herth F and Kauczor H U 2005 Pairedinspiratory/expiratory volumetric thin-slice CT scan for emphysema analysis: comparison of differentquantitative evaluations and pulmonary function test Chest 128 3212–20

Zaporozhan J, Ley S, Gast K K, Schmiedeskamp J, Biedermann A, Eberle B and Kauczor H U 2004 Functional analysisin single-lung transplant recipients: a comparative study of high-resolution CT, 3He-MRI, and pulmonaryfunction tests Chest 125 173–81