Developing a framework for CBCT-to-CT synthesis inpaediatric abdominal radiotherapy
Adam Szmul1, Sabrina Taylor1, Pei Lim2, Jessica Cantwell2, Derek D’Souza2, SyedMoinuddin2, Mark Gaze2, Jennifer Gains2, and Catarina Veiga1
1 University College London, London, United Kingdom
2 University College London Hospitals NHS Foundation Trust, London, United Kingdom

Abstract. We proposed a CBCT-to-CT synthesis framework tailored for
paediatric abdominal patients. Our approach was based on the cycle-consistent
generative adversarial network (cycleGAN) modified to preserve structuralconsistency. To adjust for differences in field-of-view and body size fromdifferent patient groups, our training data was spatially co-registered to acommon field-of-view and normalised to a fixed size. The proposed framework
showed improvements in generating synthetic CTs from CBCTs compared to theoriginal implementation of cycleGAN without field-of-view adjustments andstructural consistency constrain.
Keywords: Cone-beam computed tomography (CBCT), computer tomography(CT), cycle-consistent generative adversarial networks (cycleGANs), syntheticimages, childhood cancer, abdominal neuroblastoma, paediatric radiotherapy

1Introduction

Synthesizing computed tomography (CT) images from cone-beam CT (CBCT)
using cycle-consistent generative adversarial networks (cycleGANs)1 is becoming a
popular method to verify and adapt radiotherapy treatments. However, current
approaches still cannot guarantee sufficient structural consistency between source andsynthetic images2-4. Furthermore, application of this methodology to abdominal imagesof children is particularly challenging5. The abdominal region is complex due to theinter-fractional variability in bowel filling. The paediatric cancer population issimultaneously diverse (ranging from infants to adolescents, therefore exhibiting large
anatomical variability across development ages) and rare, making it difficult to collectlarge datasets for data-driven approaches. This work proposes a novel framework for
CBCT-to-CT synthesis tailored for paediatric abdominal patients aiming to addresses
these challenges.

2

Materials and Methods

Fully anonymised scans from 21 patients aged 2 to 22 treated at University College
London Hospital were used in this study. Subjects treated for malignancies in the2thoracic-abdominal-pelvic region were combined to increase the dataset size. The
inclusion of young adults provides additional variability in patient size. One planning
CT and 2 to 6 CBCTs were available per patient. The data were split as 15/6 patientsfor training and evaluation, respectively.
The synthetic CT (synCT) generation pipeline with the proposed field-of-view
adjustment and structural consistency included the following key steps:(1) Smart slice selection: The CT/CBCT training set was spatially normalized to areference anatomy using an atlas-based method developed in-house6. From the spatial
and anatomical distribution of the slices available, a region of interest was defined fromfusing the mapped body contours (50% majority voting). This adjusted field-of-view
approach reduced the number of slices from 4291 to 3740 for CBCT.
(2) Image pre-processing: To prevent the networks from adding elements such as
tubes and internal lines to unseen synthetic images, these external elements and otherartifacts were semiautomatically removed from the training images. Then, the images
were resized to a fixed size of 256x256 (axial normalization) to account for variationin body size across ages.
(3) Network design: A 2D cycleGAN approach for CT-to-CBCT synthesis wasapplied, known for its good performance in unpaired data style conversion 1. To promotepreservation of the CBCT anatomy, the original formulation was extended byintroducing a structure consistency loss in the form of locally normalized crosscorrelation (LNCC), between the original images and their synthesized counterparts.
We trained six cycleGAN networks with combinations of including or not axial
normalization, consistent field-of-view and structure consistency loss in the pipeline.
The networks were trained for 150 epochs.
Evaluating the quality and plausibility of synthetic images is a challenging task due
to the lack of a ground-truth (i.e., pairs of simultaneously acquired CT/CBCT scans).
To address this, we have generated a virtual CT (vCT) scan matched to the anatomy ofthe CBCTs. The vCT was generated by deformably registering the planning CT to theCBCT. The vCT was then corrected by replacing gas regions deformed from theoriginal CT as water, and gas regions directly from the CBCT as air. Thegastrointestinal gas was semi-automatically delineated in all scans using ITK-Snap7.
The synCT were globally compared to the vCT by computing three image similarity
metrics between the two images: sum of squared differences (SSD), normalised crosscorrelation (NCC) and normalised mutual information (NMI). Voxels outside the body
contour were excluded from analysis based on semiautomatic binary masks. Intensities
in all images were clipped to [-1000, 1000].

3

Results and Discussion

We summarized the obtained numerical results in Table 1, where we compared theraw CBCTs versus vCTs, as well as different approaches for synCTs generation.
Considering the cycleGAN with default data selection approach as baseline,
improvements were observed when the proposed improved data presentation wasapplied. For each data representation, the structure consistency constrain term

3
contributed to further improvements in the network performance. In fact, the biggest
improvement was observed between the original cycleGAN method with and without
structure constrains.
Table 1. Quantitative evaluation between vCT, CBCT and synCT with different methods.
MethodOriginal CBCT
cycleGANs
cycleGANs+LNCC
cycleGANs
cycleGANs+LNCC
cycleGANs
cycleGANs+LNCC

DatasetN/A
No data selection
Axial normalisation
Axial normalisation &consistent field-of-view

SSD
66±9
146±17
62±8
72±10
61±7
69±7
63±6

NCC
0.956±0.008
0.710±0.095
0.956±0.009
0.934±0.015
0.958±0.008
0.943±0.010
0.955±0.007

NMI
0.585±0.011
0.925±0.008
0.930±0.008
0.933±0.008
0.931±0.008
0.931±0.009
0.933±0.008

Examples of vCT, CBCT and synCT slices are shown in Fig. 1. The synCTs generated
with our improved network represented well the anatomy of the CBCTs and reduced
imaging artifacts. The original cycleGANs algorithm had poorer performance in termsof anatomical realism and consistency across adjacent slices. Upon visual inspection,
the images generated with LNCC structure constrain were more accurate anatomically.
Typical patterns of failure were also visually identified, and included the algorithm
removing/adding vertebrae to the synCTs as well as adding inexistent bowel contrast.

Fig. 1. Example 2D slices of vCT, CBCT and synCT (generated with consistent field-of-viewand structure consistency), along with cross-sectional intensity profiles and 3D histograms.

44Conclusions

The proposed framework showed improved quality of synCTs generated fromCBCTs when employing strategies to preserve structural consistency and to account forvariable field-of-view in the training dataset. Our study demonstrated the advantagesof a thought-through data pre-processing and presentation to the AI method to improve
its performance on challenging real-world applications, with scarce and diverse data.
Further evaluation using metrics of anatomical plausibility and realism, as well as
impact on dose calculations, is need to provide insights into clinical utility.
Acknowledgments
AS was supported by the National Institute for Health Research University College
London Hospitals Biomedical Research Centre and by the Wellcome Trust Institutional
Strategic Support Fund (WISSF). CV was supported by the Royal Academy ofEngineering under the Research Fellowship scheme (RF\201718\17140). This work
was supported by the Radiation Research Unit at the Cancer Research UK City ofLondon Centre Award [C7893/A28990].
References
1. Zhu, J.-Y. et al,. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks,” IEEE International Conference on Computer Vision (ICCV) (2017)
2. Liang, X. et al, “Generating synthesized computed tomography (CT) from cone-beamcomputed tomography (CBCT) using CycleGAN for adaptive radiation therapy,” Phys Med
Biol. 64(12):125002 (2019)
3. Kurz, C. et al, “CBCT correction using a cycle-consistent generative adversarial network
and unpaired training to enable photon and proton dose calculation,” Phys. Med. Biol.
64(2):225004 (2019)
4. Liu, Y. et al, “CBCT-based synthetic CT generation using deep-attention cycleGAN for
pancreatic adaptive radiotherapy,” Med. Phys 47(6):2472-83 (2020)
5. Uh, J., et al, “Training a deep neural network coping with diversities in abdominal and pelvic
images of children and young adults for CBCT-based adaptive proton therapy,” Radiother.
Oncol. (in press, 2021)
6. Veiga, C. et al: Atlas construction and spatial normalisation to facilitate radiation-induced
late effects research in childhood cancer,” Phys. Med. Biol. 66(10):105005 (2021)
7. Yushkevich, P. et al: “User-guided 3D active contour segmentation of anatomical structures:
Significantly improved efficiency and reliability,” Neuroimage 31(3):1116-28 (2006)