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BMC Cancer
Published in: BMC Cancer 1/2023

Open Access 01-12-2023 | Digital Volume Tomography | Research

Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators

Authors: Zhenkai Li, Qingxian Zhang, Haodong Li, Lingke Kong, Huadong Wang, Benzhe Liang, Mingming Chen, Xiaohang Qin, Yong Yin, Zhenjiang Li

Published in: BMC Cancer | Issue 1/2023

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Abstract

Background

The goal was to investigate the feasibility of the registration generative adversarial network (RegGAN) model in image conversion for performing adaptive radiation therapy on the head and neck and its stability under different cone beam computed tomography (CBCT) models.

Methods

A total of 100 CBCT and CT images of patients diagnosed with head and neck tumors were utilized for the training phase, whereas the testing phase involved 40 distinct patients obtained from four different linear accelerators. The RegGAN model was trained and tested to evaluate its performance. The generated synthetic CT (sCT) image quality was compared to that of planning CT (pCT) images by employing metrics such as the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Moreover, the radiation therapy plan was uniformly applied to both the sCT and pCT images to analyze the planning target volume (PTV) dose statistics and calculate the dose difference rate, reinforcing the model’s accuracy.

Results

The generated sCT images had good image quality, and no significant differences were observed among the different CBCT modes. The conversion effect achieved for Synergy was the best, and the MAE decreased from 231.3 ± 55.48 to 45.63 ± 10.78; the PSNR increased from 19.40 ± 1.46 to 26.75 ± 1.32; the SSIM increased from 0.82 ± 0.02 to 0.85 ± 0.04. The quality improvement effect achieved for sCT image synthesis based on RegGAN was obvious, and no significant sCT synthesis differences were observed among different accelerators.

Conclusion

The sCT images generated by the RegGAN model had high image quality, and the RegGAN model exhibited a strong generalization ability across different accelerators, enabling its outputs to be used as reference images for performing adaptive radiation therapy on the head and neck.
Literature
1.
Farjam R, Nagar H, Kathy Zhou X, et al. Deep learning-based synthetic CT generation for MR-only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator. J Appl Clin Med Phys. 2021;22(8):93–104.CrossRefPubMedPubMedCentral
2.
Chen L, Liang X, Shen C, et al. Synthetic CT generation from CBCT images via deep learning. Med Phys. 2020;47(3):1115–25.CrossRefPubMed
3.
Kazemifar S, Barragan Montero AM, Souris K, et al. Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors. J Appl Clin Med Phys. 2020;21(5):76–86.CrossRefPubMedPubMedCentral
4.
Siewerdsen JH, Jaffray DA. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. Med Phys. 2001;28(2):220–31.CrossRefPubMed
5.
Hu W, Ye J, Wang J, et al. Use of kilovoltage X-ray volume imaging in patient dose calculation for head-and-neck and partial brain radiation therapy. Radiat Oncol. 2010;5:29.CrossRefPubMedPubMedCentral
6.
Wang H, Du K, Qu J, et al. Dosimetric evaluation of magnetic resonance-generated synthetic CT for radiation treatment of rectal cancer. PLoS ONE. 2018;13(1):e0190883.CrossRefPubMedPubMedCentral
7.
Jia X, Yan H, Cervino L, et al. A GPU tool for efficient, accurate, and realistic simulation of cone beam CT projections. Med Phys. 2012;39(12):7368–78.CrossRefPubMedPubMedCentral
8.
Sun M, Star-Lack JM. Improved scatter correction using adaptive scatter kernel superposition. Phys Med Biol. 2010;55(22):6695–720.CrossRefPubMed
9.
Zbijewski W, Beekman FJ. Efficient Monte Carlo based scatter artifact reduction in cone-beam micro-CT. IEEE Trans Med Imaging. 2006;25(7):817–27.CrossRefPubMed
10.
Xu Y, Bai T, Yan H, et al. A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy. Phys Med Biol. 2015;60(9):3567–87.CrossRefPubMedPubMedCentral
11.
Siewerdsen JH, Moseley DJ, Bakhtiar B, et al. The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors. Med Phys. 2004;31(12):3506–20.CrossRefPubMed
12.
Li Y, Zhu J, Liu Z, et al. A preliminary study of using a deep convolution neural network to generate synthesized CT images based on CBCT for adaptive radiotherapy of nasopharyngeal carcinoma. Phys Med Biol. 2019;64(14):145010.CrossRefPubMed
13.
Sonke JJ, Aznar M, Rasch C. Adaptive radiotherapy for anatomical changes. Semin Radiat Oncol. 2019;29(3):245–57.CrossRefPubMed
14.
Dewan A, Sharma S, Dewan A, et al. Impact of adaptive Radiotherapy on locally Advanced Head and Neck Cancer - A Dosimetric and Volumetric Study. Asian Pac. J Cancer Prev. 2016;17(3):985–92.
15.
Chen AM, Daly ME, Cui J, et al. Clinical outcomes among patients with head and neck cancer treated by intensity-modulated radiotherapy with and without adaptive replanning. Head Neck. 2014;36(11):1541–6.CrossRefPubMed
16.
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 edn.; 2015: 234–241.
17.
Chen L, Liang X, Shen C et al. Synthetic CT generation from CBCT images via unsupervised deep learning. Phys Med Biol 2021, 66(11).
18.
Yuan N, Rao S, Chen Q, et al. Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following image gently protocol using deep neural network. Med Phys. 2022;49(5):3263–77.CrossRefPubMed
19.
Rossi M, Belotti G, Paganelli C, et al. Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning. Med Phys. 2021;48(11):7112–26.CrossRefPubMed
20.
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks Communications of the ACM. 2020;63(11):139–44.
21.
Dahiya N, Alam SR, Zhang P, et al. Multitask 3D CBCT-to-CT translation and organs-at-risk segmentation using physics-based data augmentation. Med Phys. 2021;48(9):5130–41.CrossRefPubMed
22.
Gao L, Xie K, Wu X, et al. Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy. Radiat Oncol. 2021;16(1):202.CrossRefPubMedPubMedCentral
23.
Wang H, Liu X, Kong L, et al. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy. Strahlenther Onkol; 2023.
24.
Gao L, Xie K, Sun J, et al. Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy. Med Phys. 2023;50(2):879–93.CrossRefPubMed
25.
Qiu RLJ, Lei Y, Shelton J et al. Deep learning-based thoracic CBCT correction with histogram matching. Biomed Phys Eng Express 2021, 7(6).
26.
Suwanraksa C, Bridhikitti J, Liamsuwan T et al. CBCT-to-CT translation using Registration-Based generative adversarial networks in patients with Head and Neck Cancer. Cancers (Basel) 2023, 15(7).
27.
Deng L, Hu J, Wang J, et al. Synthetic CT generation based on CBCT using respath-cycleGAN. Med Phys. 2022;49(8):5317–29.CrossRefPubMed
Metadata
Title
Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators
Authors
Zhenkai Li
Qingxian Zhang
Haodong Li
Lingke Kong
Huadong Wang
Benzhe Liang
Mingming Chen
Xiaohang Qin
Yong Yin
Zhenjiang Li
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2023
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-023-11274-7

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