A deep learning method for generating pseudo-CT by cone beam CT in radiotherapy
10.3760/cma.j.cn113030-20211103-00450
- VernacularTitle:锥形束CT生成伪CT的深度学习方法
- Author:
Yuxiang LIU
1
;
Bining YANG
;
Ran WEI
;
Yueping LIU
;
Xinyuan CHEN
;
Rui XIONG
;
Kuo MEN
;
Hong QUAN
;
Jianrong DAI
Author Information
1. 武汉大学物理科学与技术学院，武汉 430072
- Keywords:
Cone-beam computed tomography;
Deep learning;
Prostate neoplasms;
Pseudo-CT;
Adaptive radiotherapy
- From:
Chinese Journal of Radiation Oncology
2023;32(1):42-47
- CountryChina
- Language:Chinese
-
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.
