Received: 4 January 2023

Revised: 1 October 2023

Accepted: 4 November 2023

DOI: 10.1002/acm2.14212

R A D I AT I O N O N C O L O G Y P H Y S I C S

Cycle-generative adversarial network-based bone
suppression imaging for highly accurate markerless
motion tracking of lung tumors for cyberknife irradiation
therapy
Zennosuke Mochizuki1
Masahide Saito2
Toshihiro Suzuki1,2
Koji Mochizuki1
Junichi Hasegawa1
Hikaru Nemoto2
Kenichiro Satani1
Hiroshi Takahashi1

Hiroshi Onishi2

1
Department of Radiology,
Kasugai-CyberKnife Rehabilitation Hospital,
Fuefuki-city, Yamanashi, Japan2
Department of Radiology, University of
Yamanashi, Chuo-city, Yamanashi, Japan

Correspondence
Masahide Saito, Department of Radiology,
University of Yamanashi, 1110 Shimokato,Chuo-city, Yamanashi, 409-3898, Japan.
Email: masahides@yamanashi.ac.jp

Abstract
Purpose: Lung tumor tracking during stereotactic radiotherapy with the
CyberKnife can misrecognize tumor location under conditions where similar patterns exist in the search area. This study aimed to develop a technique for bone
signal suppression during kV-x-ray imaging.
Methods: Paired CT images were created with or without bony structuresusing a 4D extended cardiac-torso phantom (XCAT phantom) in 56 cases.
Subsequently, 3020 2D x-ray images were generated. Images with bone were
input into cycle-consistent adversarial network (CycleGAN) and the bone suppressed images on the XCAT phantom (BSIphantom ) were created. They werethen compared to images without bone using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Next, 1000 non-simulated
treatment images from real cases were input into the training model, andbone-suppressed images of the patient (BSIpatient ) were created. Zero means
normalized cross correlation (ZNCC) by template matching between each of
the actual treatment images and BSIpatient were calculated.
Results: BSIphantom values were compared to their paired images withoutbone of the XCAT phantom test data; SSIM and PSNR were 0.90 ± 0.06
and 24.54 ± 4.48, respectively. It was visually confirmed that only bone wasselectively suppressed without significantly affecting tumor visualization. The
ZNCC values of the actual treatment images and BSIpatient were 0.763 ± 0.136
and 0.773 ± 0.143, respectively. The BSIpatient showed improved recognition
accuracy over the actual treatment images.
Conclusions: The proposed bone suppression imaging technique based on
CycleGAN improves image recognition, making it possible to achieve highly
accurate motion tracking irradiation.
KEYWORDS
CycleGAN, deep learning, generative adversarial networks (GAN), lung cancer, radiotherapy,
stereotactic radiotherapy

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.
J Appl Clin Med Phys. 2023;e14212.
https://doi.org/10.1002/acm2.14212

wileyonlinelibrary.com/journal/acm2

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MOCHIZUKI ET AL.

INTRODUCTION

Stereotactic body radiation therapy (SBRT) has been
widely applied as a treatment modality for early stage
non-small cell lung cancer (NSCLC). SBRT combines
the techniques of multidirectional irradiation and precise irradiation of lesions to improve local control oftumors confined to the trunk region and reduce adverse
events in normal tissue nearby. SBRT is also capable of high efficacy with a single large dose, whichcannot be achieved with conventional radiotherapy.1
The CyberKnife (CK) System (Accuray Inc., Sunnyvale, CA, USA) integrates a robotic-positioned linear
accelerator, an image-guided system, and respiratory
tracking systems. CK has two respiratory tracking systems: the fiducial-based target tracking system (FTTS)
and Xsight Lung Tracking System (XLTS).2 XLTS is afiducial-free real-time tracking system used to irradiate
lung tumors that move with respiration.3 This facilitates the minimization of radiation exposure to healthy
pulmonary tissues, thereby enhancing the efficacy oftreatment.4,5 This system, called target locating system(TLS), uses a pair of orthogonal x-ray imagers, whichenable real-time tracking of moving targets by modelingthe correlation between the targets and external surrogate light-emitting diode markers placed on the patient’s
chest.6 With this system, the patient’s treatment area
is tracked using real-time imaging, and the error in theirradiated area is less than 1 mm.7,8Generally, sufficient tumor contrast relative to the surrounding tissue in an x-ray image is essential for accurate soft-tissue tracking.XLTS performs template matching to match the image density pattern of the tumor
region in digitally reconstructed radiography (DRR) withthe most similar region in the live x-ray image. This
may affect visibility, especially in cases with overlapping bone. Because the tumor recognition accuracy is
not universal for all lesions, the author implemented
an approach for bone suppression imaging using atype of general adversarial network (GAN), called cycleconsistent adversarial network (CycleGAN). In recent
years, GANs have shown state-of -the-art performancein many image-processing tasks.9,10 In addition, CycleGAN was proposed to learn translation mappings in theabsence of aligned paired images.10 Recently, CycleGAN was used to synthesize CT images from cone
beam CT (CBCT) images.11 However, no research has
been conducted to date that incorporates CycleGAN
into a CK motion-tracking algorithm. Therefore, thisstudy aimed to use deep learning to develop a bone
suppression imaging technique for kV x-ray images.

generated by CycleGAN (BSIphantom ) were comparedto images without bone. Next, BSIpatient were generated
from the images used for actual CK treatment, and theeffectiveness of deep learning was compared by template matching for each of the actual treatment images
and BSIpatient . The workflow is illustrated in Figure 1.

2.1

2.1.1
XCAT phantom (4D extended
cardiac-torso phantom) images
We first created images from XCAT phantom (Duke
University, Durham, North Carolina)12,13 to project theimages to be used in the deep-learning datasets. XCAT
phantom is a digital anthropomorphic phantom image
database that was developed based on the humananatomy database of the National Library of Medicine.
Based on patient data and using non-uniform rational b-spline surfaces to define the anatomy, the XCAT
phantom combines a voxelized approach with a mathematical approach to offer simulated imaging withrealistic and detailed organs that remain flexible to allow
for anatomical variation and organ deformation.14–16 In
addition, the XCAT phantom is positioned off the center of the image, but patient images in CT images canrotate, shift, or exceed the field of view. Here, we used 56
samples from this phantom,which all differed in age,sex,
nationality, height, and weight. The dataset included 56
men and women aged 18−78 years, weight 52−120 kg,
height 153−190 cm, and BMI:18-39 kg/m2 . The dxcat2
code of the XCATv2 software package was used byturning off the activity unit of the bone to obtain images
with and without bony structures, as shown in Figure 2a.
For settings, bone size parameter of 1, pixel width andslice width of 0.03 cm, and array size of 512 × 512 wereused.
Next, to create 2D CK images based on theseimages, we used 3D Slicer ver.4.11 software (MIT, Massachusetts, USA) to create 45◦ images, as shown in
Figure 2b. Images at 40◦ and 50◦ were created in
the same manner in order to increase the number ofdatasets used, as shown in Figure 2c. The View-Up
vector was set to (x,y,z) = (−1,0,45), Normal vector to(x,y,z) = (any angle,−45,0), and Isocentor position to(x,y,z) = (0,0,0). These were then divided into nine segments, as shown in Figure 2d. This process created a
total of 3020 images, of which, data from 340 images
were used for testing.

2.1.2
2

Data

Treatment images

MATERIALS AND METHODS

After the images with bone were input to the trainedmodel to create bone suppressed images, the images

To evaluate the model, data from actual treated patientswere required. This was obtained from 50 patientswith metastatic lung cancer (tumor size 8–64 mm) and

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2 of 8FIGURE 1

3 of 8Work-flow of the bone suppression and evaluation framework.

F I G U R E 2 Creation of image datasets with and without bone XCAT phantom. (a): With (L)/without (R) bone generated from XCATPhantom, (b): Pseudo-projecting the XCAT phantom, Projections with 5 degree difference, (d): The projected images with (L)/without (R) bone asin (b) were divided into 9 segments.

primary lung cancer (tumor size 10–62 mm) undergoing SBRT at our center between February 2020 andJanuary 2022. 40 patients were male and 10 werefemale. The ages of the patients were between 46
and 93 years (average age, 74.14). All patients weretreated with the CK G4 system using 2-view XLTS(100 kV/200 mA/100 ms).

2.2

Optimization and training methods

The training methods and parameters were based on
CycleGAN. Similar to pix2pix,17 Zhu et al. proposed
CycleGAN, a type of image-generation algorithm using
GAN.10 Figure 3 shows the CycleGAN procedure. The
domain of the images was defined, and images were15269914, 0, Downloaded from https://aapm.onlinelibrary.wiley.com/doi/10.1002/acm2.14212 by Cochrane Japan, Wiley Online Library on [21/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

MOCHIZUKI ET AL.

MOCHIZUKI ET AL.

F I G U R E 3 Procedure of CycleGAN. CycleGAN is a method that achieves image transformation by learning domain relationships such as
fields and regions between two images.

collected for each domain as training data. The mainfeature of CycleGAN is that there is no need to prepare a pair of training data. The set of images for eachdomain is denoted as X and Y, and a generator is prepared to perform X-to-Y and Y-to-X transformations onthem. The default generator architecture of CycleGAN
is ResNet, while the default discriminator architecture isa PatchGAN classifier.17,18 In addition, two discriminators corresponding to both were prepared.The proposed
method enables non-pairwise image transformations bylearning the error (loss) between adversarial loss usedin the GAN and the cycle consistency Loss proposed inthis study. However, it is shown to be less accurate thanpix2pix because of unsupervised learning.
In this study, the algorithm was run on a workstation
with an NVIDIA® GeForce® RTX 2080 SUPER™ 8GB
GPU. Adam19 was used as the optimizer, with β1 = 0.5
and β2 = 0.999. The learning rate was set to 0.0002
for both the generator and discriminator. Both the generator and discriminator were updated once for each
iteration, and the training was terminated when each
network was considered to have been generated witha certain degree of accuracy.

the SSIM for each block, and Equation (2) was used toobtain the average SSIM for all blocks.
(2𝜇x 𝜇y + C1)(2𝜎xy + C2 )SSIM(x, y) = (
)(
)
y
y
x
𝜇2 + 𝜇2 + C1 𝜎2x + 𝜎2 + C2
MSSIM = 1∕M

M∑

SSIM(xj , yj )

(1)

(2)

j=1

where x is one block of the reference image, y is one
block of the test image, and 𝜇 is the average per blockof the Gaussian filtered image. Similarly, 𝜎2 refers to thevariance per block of the Gaussian filtered image: C1and C2 are constants, (0.01 × 255)2 and (0.03 × 255)2 ,respectively. M is the number of blocks.
PSNR is an index obtained by the ratio of the meansquared error (MSE) of the reference image and the test
image to the maximum grayscale value (PS = 255). The
PSNR is shown in Equation (3), and the MSE is shownin Equation (4).
)
(
PS2
(3)
PSNR = 10 log10MSE1∑2(x − yi )
N 1 i
N

2.3

Evaluation methods

The BSIphantom was compared to images without bonefrom the test data, and the similarity was calculated.
We evaluated the models with two different objective
image quality metrics, namely, the structural similarity
index (SSIM)20 and peak signal-to-noise ratio (PSNR).21
SSIM is a method to evaluate image quality basedon the structural information of an image; it is shown inEquations (1) and (2). Equation (1) was used to obtain

MSE =

(4)

where xi represents the reference image; yi represents
the gray scale value of the test image; N represents thetotal number of images.
Next, template matching22,23 was performed on eachof the actual treatment images and BSIpatient to calculate
the zero-mean normalized cross-correlation (ZNCC).24
Therefore, in this study, the search area was the entire
image and the goal was to capture the tumor contour.

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4 of 85 of 8F I G U R E 4 Examples between the test data and BSI. (a): the test data with bone, (b): the test data without bone, (c): BSIphantom , (d–f):
partially enlarged versions of (a–c), (g): actual treatment images and (h): BSIpatient , the blue line in the patient data shows the TTV.

The detection method using template matching is themost basic and determines the location of the target
in the input image by comparing the input image withthe partial image of the target to be tracked, which was
prepared in advance. ZNCC is a measure that uses theaverage luminance values of the template and input
image to compensate for the effects of the lighting environment. The location with the highest value is the target
location. In addition, ZNCC is a general measure ofimage similarity and ranges from −1 to 1, with a value of
1 when the two images are exactly matched. After 1000
actual treatment images were run through CycleGAN to
BSIpatient , template matching was performed on each of
the actual treatment images and BSIpatient to calculate
ZNCC.

2.4

Statistical analysis

All statistical analyses were performed using SPSS
Statistics for Windows (version 27.0; IBM Corp., Armonk,
NY, Version 27.0. Armonk, NY: IBM Corp). Differences
between the groups were compared using independent
T-tests. Alpha was set to 0.05.

3

RESULTS

Figure 4 shows examples of BSIphantom compared to thetest data without bone, and the similarity was calculated.
The bone intensity was generally reduced, althoughsome remained intact. The mean of SSIM and PSNR

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MOCHIZUKI ET AL.

F I G U R E 5 Template matching for each of actual treatment images and BSIpatient . The upper figure shows (a,c): actual treatment imagesand (b,d): BSIpatient . The green box in the patient data shows the region of high similarity in template matching, demarcated by rectangular
windows. The middle figure shows heatmaps for the similarity of each image. The bottom figure shows the subtraction images for both pairs.

were 0.90 ± 0.06 and 24.54 ± 4.48, respectively, indicating high similarity. Figure 4g,h show examples of image
generation using CycleGAN. The blue line indicates atumor tracking volume (TTV). The bone intensity was
suppressed, and tumor contours remained after image
generation.
Next, template matching was performed using theactual treatment images and BSIpatient , and the ZNCC
was calculated for each. Figure 5 shows two examples of template matching. The middle figure shows
heatmaps for the similarity of each image. ZNCC isvectorized each cell in the heatmap and calculated thesimilarity based on its angle. The red color is indicatedhigher similarity; ZNCC was significantly different andhigher for images without bone structures. The heatmap
visualizes the model based on these differences.
Table 1 shows the average ± standard deviation of
ZNCC for the 767 images that could be detected by template matching. The p-value was less than 0.05, which
was considered statistically significant, indicating thatthe BSIpatient had higher ZNCC than the actual treatment
images.

4

DISCUSSION

This is the first report that tried to incorporate CycleGAN
into a CK motion-tracking algorithm, and the promis-

TA B L E 1 ZNCC was calculated by template matching of actual
treatment images and BSIpatient , respectively.
Total
number ofimagesNumber ofimagesidentified
correctly (%)

ZNCCActual treatment
images1000

767 (76.7)

0.763 ± 0.136*

BSIpatient

1000

800 (80.0)

0.773 ± 0.143*

Image types*p < 0.05 significantly different compared with the reference.

ing result is expected to gain worldwide recognition. We
propose the use of CycleGAN to learn to suppress theintensity of bony structures on TLS radiographs. Experimental evaluations on the XCAT phantom datasets
validated the effectiveness of the framework developed
to do this.
Using existing methods, tumors are difficult to detectbecause the visibility of the tumor in the live image
is reduced when the tumor overlaps bony structures,and the image quality is degraded. In this study, deeplearning and evaluation were performed using a dataset
created from the XCAT phantom to generate bonesuppression images for the XLTS. This method holds thepromise of achieving a high level of accuracy throughthe training of the model using images with and without bone. However, in this study, actual treatment images

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6 of 8

were run through CycleGAN to generate images without bone, and there were cases in which bone intensity
reduction was difficult. This may be due to the possibility that the images were not recognized as bone, inwhich case, improved datasets may be required. As ameasure to create data sets that suppress bone intensity, image processing techniques such as dual energy
subtraction have been used recently in order to improvethe detection of pulmonary nodules.25 In a study examining the detection of thoracic masses, the use ofsoft-tissue images significantly improved the detectionof masses.26 In addition, a model was constructed togenerate soft tissue chest images using GAN, and comparison with test data showed that the generated images
had a high degree of similarity, indicating their clinicalusefulness.27
In addition, there was a discrepancy between thelocation of the tumor and that indicated by templatematching. Because template matching searches for thelocation where the similarity between the input image
and template is maximized, it is difficult to accommodatechanges in the appearance of the target, such as rotation. To solve this problem, variable template matching,in which the template is updated when tracking is successful,has been proposed,but there is an accumulationof errors due to background contamination in the template and difficulty in determining the success or failure
itself.28–30
As a limitation, XLTS is used uncertainty values toevaluate detection uncertainty, but the algorithm varies
from version to version and the details are not publicly
available. Therefore, this was addressed in this study by
calculating the similarity using ZNCC.
For images in which tumors could not be recognizedusing this method, the reason for the failure to recognize the tumors was considered to be poor backgroundremoval processing. If the tumor to be recognized iscomplex and contains considerable noise,noise removal
cannot be performed well. Furthermore, recognition
becomes impossible owing to the inclusion of noise andthe tumor.
Therefore, our future studies will investigate a newrecognition method that utilizes new features such as
noise reduction and contrast.

5

CONCLUSION

In this study, we generated and evaluated CycleGAN
images using a dataset created with the XCAT phantom
to generate bone suppression images for the XLTS. The
proposed bone suppression imaging technique basedon CycleGAN improves the image similarity in templatematching, making it possible to achieve highly accurate
markerless motion tracking irradiation. The possibilities
for future studies are included evaluation experiments of

7 of 8the constructed algorithms and their implementation inphantoms on simulated tumors for clinical applications.
AU T H O R C O N T R I B U T I O N S
The authors confirm contribution to the paper as follows:
study conception and design: Zennosuke Mochizuki,
MS, Masahide Saito, PhD; data collection: Zennosuke Mochizuki, MS; analysis and interpretation of
results:Zennosuke Mochizuki,MS,Masahide Saito,PhD;
draft manuscript preparation: Zennosuke Mochizuki, MS,
Masahide Saito, PhD. All authors reviewed the results
and approved the final version of the manuscript.
AC K N OW L E D G E M E N T S
I would like to thank my sincere gratitude to the people described here for their expertise and support in
all aspects of the research and for their help in the
preparation of the manuscript.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no potential conflicts of interest.
ORCID
Masahide Saito
https://orcid.org/0000-0002-8636-4045
Toshihiro Suzuki
https://orcid.org/0000-0003-0414-5218
Hikaru Nemoto
https://orcid.org/0000-0001-6540-2906
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How to cite this article: Mochizuki Z, Saito M,
Suzuki T, et al. Cycle-generative adversarial
network-based bone suppression imaging forhighly accurate markerless motion tracking oflung tumors for cyberknife irradiation therapy. J
Appl Clin Med Phys. 2023;e14212.
https://doi.org/10.1002/acm2.14212

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