PURPOSE: Current clinical application of cone-beam CT (CBCT) is limited to patient setup. Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT-based adaptive planning presently impractical. In this study, we developed a deep-learning-based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT-guided pancreatic adaptive radiotherapy. METHODS: Thirty patients previously treated with pancreas SBRT were included. The CBCT acquired prior to the first fraction of treatment was registered to the planning CT for training and generation of synthetic CT (sCT). A self-attention cycle generative adversarial network (cycleGAN) was used to generate CBCT-based sCT. For the cohort of 30 patients, the CT-based contours and treatment plans were transferred to the first fraction CBCTs and sCTs for dosimetric comparison. RESULTS: At the site of abdomen, mean absolute error (MAE) between CT and sCT was 56.89 ± 13.84 HU, comparing to 81.06 ± 15.86 HU between CT and the raw CBCT. No significant differences (P > 0.05) were observed in the PTV and OAR dose-volume-histogram (DVH) metrics between the CT- and sCT-based plans, while significant differences (P < 0.05) were found between the CT- and the CBCT-based plans. CONCLUSIONS: The image similarity and dosimetric agreement between the CT and sCT-based plans validated the dose calculation accuracy carried by sCT. The CBCT-based sCT approach can potentially increase treatment precision and thus minimize gastrointestinal toxicity.
PURPOSE: Current clinical application of cone-beam CT (CBCT) is limited to patient setup. Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT-based adaptive planning presently impractical. In this study, we developed a deep-learning-based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT-guided pancreatic adaptive radiotherapy. METHODS: Thirty patients previously treated with pancreas SBRT were included. The CBCT acquired prior to the first fraction of treatment was registered to the planning CT for training and generation of synthetic CT (sCT). A self-attention cycle generative adversarial network (cycleGAN) was used to generate CBCT-based sCT. For the cohort of 30 patients, the CT-based contours and treatment plans were transferred to the first fraction CBCTs and sCTs for dosimetric comparison. RESULTS: At the site of abdomen, mean absolute error (MAE) between CT and sCT was 56.89 ± 13.84 HU, comparing to 81.06 ± 15.86 HU between CT and the raw CBCT. No significant differences (P > 0.05) were observed in the PTV and OAR dose-volume-histogram (DVH) metrics between the CT- and sCT-based plans, while significant differences (P < 0.05) were found between the CT- and the CBCT-based plans. CONCLUSIONS: The image similarity and dosimetric agreement between the CT and sCT-based plans validated the dose calculation accuracy carried by sCT. The CBCT-based sCT approach can potentially increase treatment precision and thus minimize gastrointestinal toxicity.
Authors: Toshiyuki Okada; Marius George Linguraru; Masatoshi Hori; Ronald M Summers; Noriyuki Tomiyama; Yoshinobu Sato Journal: Med Image Anal Date: 2015-07-04 Impact factor: 8.545
Authors: Tonghe Wang; Yang Lei; Sibo Tian; Xiaojun Jiang; Jun Zhou; Tian Liu; Sean Dresser; Walter J Curran; Hui-Kuo Shu; Xiaofeng Yang Journal: Med Phys Date: 2019-05-21 Impact factor: 4.071
Authors: Yang Zhang; Ning Yue; Min-Ying Su; Bo Liu; Yi Ding; Yongkang Zhou; Hao Wang; Yu Kuang; Ke Nie Journal: Med Phys Date: 2021-05-14 Impact factor: 4.071
Authors: Richard L J Qiu; Yang Lei; Joseph Shelton; Kristin Higgins; Jeffrey D Bradley; Walter J Curran; Tian Liu; Aparna H Kesarwala; Xiaofeng Yang Journal: Biomed Phys Eng Express Date: 2021-10-29
Authors: Joshua S Niedzielski; Yufei Liu; Sylvia S W Ng; Rachael M Martin; Luis A Perles; Sam Beddar; Neal Rebueno; Eugene J Koay; Cullen Taniguchi; Emma B Holliday; Prajnan Das; Grace L Smith; Bruce D Minsky; Ethan B Ludmir; Joseph M Herman; Albert Koong; Gabriel O Sawakuchi Journal: Int J Radiat Oncol Biol Phys Date: 2021-08-13 Impact factor: 7.038
Authors: Ana Barragán-Montero; Umair Javaid; Gilmer Valdés; Dan Nguyen; Paul Desbordes; Benoit Macq; Siri Willems; Liesbeth Vandewinckele; Mats Holmström; Fredrik Löfman; Steven Michiels; Kevin Souris; Edmond Sterpin; John A Lee Journal: Phys Med Date: 2021-05-09 Impact factor: 2.685
Authors: Xianjin Dai; Yang Lei; Jacob Wynne; James Janopaul-Naylor; Tonghe Wang; Justin Roper; Walter J Curran; Tian Liu; Pretesh Patel; Xiaofeng Yang Journal: Med Phys Date: 2021-10-13 Impact factor: 4.071
Authors: José S Enriquez; Yan Chu; Shivanand Pudakalakatti; Kang Lin Hsieh; Duncan Salmon; Prasanta Dutta; Niki Zacharias Millward; Eugene Lurie; Steven Millward; Florencia McAllister; Anirban Maitra; Subrata Sen; Ann Killary; Jian Zhang; Xiaoqian Jiang; Pratip K Bhattacharya; Shayan Shams Journal: JMIR Med Inform Date: 2021-06-17