Reproducibility Analysis of 4DCT Derived Ventilation Distribution

Data

An Application of a Ventilation Calculation Algorithm based on 4DCT

Geoffrey G. Zhang, Kujtim Latifi, Vladimir Feygelman, Thomas J. Dilling and Eduardo G. Moros

Radiaiton Oncology, Moffitt Cancer Center, Tampa, Florida, U.S.A.

Keywords: Ventilation, Deformable Image Registration, 4DCT, Reproducibility, Lung Cancer.

Abstract: Deriving lung ventilation distribution from 4-dimensional CT (4DCT) using deformable image registration

(DIR) is a recent technical development. In this study, we evaluated the serial reproducibility of ventilation

data derived from two separate 4DCT data sets, collected at different time points. A total of 33 lung cancer

patients were retrospectively analyzed. All patients had two stereotactic body radiotherapy treatment

courses for lung cancer. Seven patients were excluded due to artifacts in the 4DCT data sets. The ventilation

distributions in the lungs for each patient were calculated using the two sets of planning 4DCT data. The

deformation matrices between the expiration and inspiration phases generated by DIR were used to produce

ventilation distributions using the ΔV method. Ventilation in the lung regions that received less than 1 Gy

was analyzed. For the 26 cases, the median Spearman correlation coefficient value was 0.31 (range 0.18 to

0.52, p value < 0.01 for all cases). The median Dice similarity coefficient value between the upper 30%

ventilation regions of the two sets was 0.75 (range 0.71 to 0.81, Figure 1). We conclude that the two

ventilation data sets in each case correlated and the reproducibility over time was reasonably good.

1 INTRODUCTION

Perfusion and ventilation can be used to characterize

lung function. Clinically, ventilation imaging is

mostly performed using SPECT (Harris et al., 2007)

or PET (Melo et al., 2003). Deriving lung ventilation

distribution from 4-dimensional CT (4DCT) using

deformable image registration is a recent technical

development (Zhang et al., 2009; Guerrero et al.,

2005; Reinhardt et al., 2008). Several studies have

shown that this technique agrees reasonably well

with other established techniques such as SPECT,

Xe enhanced dynamic CT or PET (Ding et al., 2012;

Kipritidis et al., 2014; Yamamoto et al., 2014).

Theoretically, one could determine regions of high

lung ventilation in thoracic cancer patients and use

these regions as avoidance structures in radiotherapy

treatment planning, without the need of an additional

imaging procedure such as SPECT or PET. This new

ventilation calculation method using 4DCT has also

been applied in lung disease detection (Castillo et

al., 2012), radiotherapy treatment planning studies

(Huang et al., 2013; Siva et al., 2015) and

assessment of radiotherapy response (Ding et al.,

2010).

In this study, we evaluated the serial

reproducibility of ventilation data derived from two

separate 4DCT data sets in the same patient

collected at different time points.

2 MATERIALS AND METHODS

2.1 Patient Data

A total of 33 lung cancer patients were

retrospectively analyzed following a protocol

approved by our institutional review board. All

patients had two lung cancer stereotactic body

radiotherapy treatment courses (different isocenters)

with a median interval between them of 9.6 months

(range: 0.7-39 mo). Separate treatment planning

4DCT datasets were acquired each treatment course.

Seven patients were excluded due to obvious

mushroom artifacts in the 4DCT datasets, thus 26

valid cases were included in data analysis and

presented in this study.

Zhang, G., Latiﬁ, K., Feygelman, V., Dilling, T. and Moros, E.

Reproducibility Analysis of 4DCT Derived Ventilation Distribution Data - An Application of a Ventilation Calculation Algorithm based on 4DCT.

DOI: 10.5220/0005747400400043

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 2: BIOIMAGING, pages 40-43

ISBN: 978-989-758-170-0

2.2 Deformable Image Registration

Based on a previous DIR selection study (Latifi et

al., 2013c), the Diffeomorphic Morphons (DM)

method (Janssens et al., 2009) was applied for the

deformable image registration (DIR) between the

expiration and inspiration phases in each 4DCT

dataset.

2.3 Ventilation

The ventilation distributions in the lungs for each

patient were calculated using the two sets of 4DCT

data, with the tumor volumes excluded. The

generated deformation matrices were used to

generate ventilation distributions using the ΔV

method, which is a direct geometrical calculation of

the volume change (Zhang et al., 2011; Zhang et al.,

2009). In the expiration phase of a 4DCT dataset,

each voxel is a cuboid defined by 8 vertices. In the

inspiration phase, this cuboid is changed into a 12-

face polyhedron which is still comprised of the

corresponding 8 vertices which can be determined

by the DIR calculation. Any hexahedron or 12-face

polyhedron can be divided into 6 tetrahedrons. The

volume of a tetrahedron can be calculated using

V = (b - a) · [(c - a) × (d - a)] / 6, (1)

where a, b, c, d are the vertices’ coordinates of the

tetrahedron expressed as vectors.

Ventilation is defined as

P = ΔV/V

, (2)

where ΔV is the volume change between expiration

and inspiration and V

is the initial volume at

expiration (Simon, 2000).

In each case, the second set of ventilation

distributions was mapped onto the first one using

DM DIR between the expiration phases of the two

4DCT sets, and the two ventilation distributions

were normalized (Latifi et al., 2013a) and compared.

Since radiation dose can alter regional lung

ventilation (Latifi et al., 2015), the high dose regions

were excluded to prevent introduction of systemic

error in the ventilation calculations. We chose to

analyze lung regions that received less than 1 Gy of

radiation dose to minimize the effect of radiation

changes on ventilation estimates since it has been

established that large dose of radiation reduce

ventilation.

As DIR may introduce errors due to artifacts and

noise in CT images, consequently, errors may be

introduced into ventilation distributions (Latifi et al.,

2013b). Smoothing the ventilation distributions may

reduce such errors. The calculated ventilation

distributions were smoothed with a 9×9×9 mm

average filter and analized.

2.4 Correlation and Reproducibility

The voxel-wise Spearman correlation coefficient

(SCC) between the ventilation data sets in each case

was calculated. The absolute ventilation data

(without normalization) were used in the SCC

analysis. The Dice similarity coefficient (DSC)

(Dice, 1945) was also calculated between the upper

30% ventilation regions of the two sets:

DSC(A,B)







2

(3)

where A and B are the two involved volumes.

3 RESULTS

Figure 1 shows a representative dose distribution.

Figure 1: Dose distribution for a representative case.

Figure 2 shows the ventilation distributions for

the case shown in Figure 1.

For the 26 cases, the median SCC value was 0.31

(range 0.18 to 0.52, p < 0.01 for all cases; original in

Figure 3). The median DSC value was 0.75 (range

0.71 to 0.81; original in Figure 4).

After the ventilation distributions were smoothed

with 9x9x9 mm

average filter, the SCC and DSC

improved, with median values of 0.44 (smooth in

Figure 3) and 0.77 (smooth in Figure 4),

respectively.

Reproducibility Analysis of 4DCT Derived Ventilation Distribution Data - An Application of a Ventilation Calculation Algorithm based on

4DCT

Figure 2: Ventilation distributions for a representative

case.

Figure 3: SCC for all cases. Blue points are the original

data points, while the red ones are after smoothing.

Figure 4: DSC for all cases. Blue points are the original

data points, while the red ones are after smoothing.

4 DISCUSSION

Mushroom artifacts often appear in 4DCT data due

to irregular diaphragmatic motion. This imaging

artifact introduces errors in DIR and consequently

errors in the derived ventilation distributions.

Because of these errors, the SCC value could be

very low, close to 0. This was the reason why 7

cases were excluded from the analysis.

The DIR errors due to noise in CT images are the

other major concern in ventilation calculation using

DIR on 4DCT (Latifi et al., 2013b). Smoothing can

reduce the effect of such errors as demonstrated in

Figure 3 and 4. Improving the quality of 4DCT

should improve the accuracy of ventilation

calculation using this technique, and the

reproducibility may be higher as a consequence.

In this study, post-treatment ventilation was mapped

to pre-treatment ventilation using DIR. This DIR

application may introduce additional errors in the

final results.

5 CONCLUSIONS

Based on the SCC and DSC values, we conclude

that the two ventilation data sets in each case

correlated and the reproducibility over time,

especially for the high ventilation regions, was

reasonably good when there were no obvious

artifacts in the 4DCT. Ventilation data smoothing

can reduce errors introduced in the DIR and thus

improve the reproducibility. High quality 4DCT is

essential for good reproducibility in ventilation

distributions.

BIOIMAGING 2016 - 3rd International Conference on Bioimaging

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Reproducibility Analysis of 4DCT Derived Ventilation Distribution Data - An Application of a Ventilation Calculation Algorithm based on

4DCT