A deep learning method for generating pseudo-CT by cone beam CT in radiotherapy
Liu Yuxiang1, Yang Bining2, Wei Ran2, Liu Yueping2, Chen Xinyuan2, Xiong Rui1, Men Kuo2, Quan Hong1, Dai Jianrong2
1School of Physics and Technology, Wuhan University, Wuhan 430072, China; 2Department of Radiation Oncology, National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Abstract：Objective To investigate the pseudo-CT generation from cone beam CT (CBCT) by a deep learning method for the clinical need of adaptive radiotherapy. Methods CBCT data from 74 prostate cancer patients collected by Varian On-Board Imager and their simulated positioning CT images were used for this study. The deformable registration was implemented by MIM software. And the data were randomly divided into the training set (n=59) and test set (n=15). U-net, Pix2PixGAN and CycleGAN were employed to learn the mapping from CBCT to simulated positioning CT. The evaluation indexes included mean absolute error (MAE), structural similarity index (SSIM) and peak signal to noise ratio (PSNR), with the deformed CT chosen as the reference. In addition, the quality of image was analyzed separately, including soft tissue resolution, image noise and artifacts, etc. Results The MAE of images generated by U-net, Pix2PixGAN and CycleGAN were (29.4±16.1) HU, (37.1±14.4) HU and (34.3±17.3) HU, respectively. In terms of image quality, the images generated by U-net and Pix2PixGAN had excessive blur, resulting in image distortion; while the images generated by CycleGAN retained the CBCT image structure and improved the image quality. Conclusion CycleGAN is able to effectively improve the quality of CBCT images, and has potential to be used in adaptive radiotherapy.
Liu Yuxiang,Yang Bining,Wei Ran et al. A deep learning method for generating pseudo-CT by cone beam CT in radiotherapy[J]. Chinese Journal of Radiation Oncology, 2023, 32(1): 42-47.
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2023, Vol. 32 
