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.
40
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
Copyright
c
2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
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
ex
, (2)
where ΔV is the volume change between expiration
and inspiration and V
ex
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
3
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:
BA
BA
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
3
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
41
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
42
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