| Hu Huimin,Dong Zhengkun,Yu Shutong,et al.A method to establish reference benchmarks for in vivo dose monitoring for radiotherapy based on dual-energy cone beam CT and deep learning[J].Chinese Journal of Radiological Medicine and Protection,2025,45(2):129-136 |
| A method to establish reference benchmarks for in vivo dose monitoring for radiotherapy based on dual-energy cone beam CT and deep learning |
| Received:August 24, 2024 |
| DOI：10.3760/cma.j.cn112271-20240824-00319 |
| KeyWords:Online adaptive radiotherapy In vivo dose monitoring Dual-energy cone-beam computed temography Generative adversarial network (GAN) |
| FundProject:国家自然科学基金(12475309,12275012,12411530076,82202941);北京市自然科学基金(Z210008);中央高校基本科研业务费/北京大学临床医学+X青年专项(PKU2024LCXQ033);教育部内地与港澳高等学校师生交流计划项目(万人计划7111400049);国家重点研发计划(2019YFF01014405);内蒙古自治区科技计划(2022YFSH0064) |
| Author Name | Affiliation | E-mail | | Hu Huimin | Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China | | | Dong Zhengkun | College of Future Technology, Peking University, Beijing 100871, China | | | Yu Shutong | Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China | | | Lin Chen | State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China | | | Li Tian | Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China | | | Zhang Yibao | Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | zhangyibao@pku.edu.cn |
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| Abstract:: |
| Objective To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian's onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE). Results SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics (Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P < 0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.9977±0.0007 and 0.9971±0.0016, PSNRs of 39.6250±4.6844 and 36.2722±5.5663, and RMSEs of 0.0041±0.0027 and 0.0063±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.9968±0.0010 and 0.9962±0.0015, PSNRs of 38.5487±3.6374 and 36.0073±4.4378, and RMSEs of 0.0043±0.0022 and 0.0061±0.0037, respectively. Conclusions This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments. |
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