Reconstruction of thoracic CT based on single-view projection with a cycle dual-task network in radiotherapy
10.3760/cma.j.cn113030-20220905-00298
- VernacularTitle:放疗中基于双任务循环网络的单投影重建胸部CT方法研究
- Author:
Jiawei SUN
1
;
Sai ZHANG
;
Heng ZHANG
;
Kai XIE
;
Liugang GAO
;
Tao LIN
;
Jianfeng SUI
;
Xinye NI
Author Information
1. 南京医科大学附属常州第二人民医院放疗科，南京医科大学医学物理研究中心，江苏省医学物理工程研究中心，常州 213003
- Keywords:
Thoracic neoplasms;
Adaptive radiotherapy;
CycleGAN;
Synthetic computed tomography;
Multi-task
- From:
Chinese Journal of Radiation Oncology
2023;32(9):829-835
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a cycle dual-task network based on cycleGAN to implement 3D CT synthesis from single-view projection for adaptive radiotherapy of thoracic tumor and then evaluate image quality and dose accuracy.Methods:A total of 45 thoracic tumor patients admitted to the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University were collected, and 991 cases were also selected from public dataset as pretrained dataset. Multi-view projections were acquired by ASTRA algorithm. The public dataset was divided into a training set of 800 cases, a validation set of 160 cases and a test set of 31 cases. The dataset obtained from patients in our hospital was divided into a training set of 40 cases and a test set of 5 cases. The network included synthetic CT model and multi-view projection prediction model and achieved the dual-task training. The final test only used the synthetic CT model to acquire the predicted CT images and deliver image quality [mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)] and dose evaluation.Results:Image quality evaluation metrics for synthetic CT showed high image synthesis accuracy with MAE of 0.05±0.01, PSNR of 19.08±1.69, SSIM of 0.75±0.04, respectively. The dose distribution calculated on synthetic CT was also close to the actual dose distribution. The mean 3%/3 mm γ pass rate for synthetic CT was 93.1%.Conclusions:A dual-task cycle network modified on cycleGAN has been implemented to rapidly and accurately predict 3D CT from single-view projection, which can be applied to the workflow of adaptive radiotherapy for thoracic cancer. Both image generation quality and dosimetric evaluation demonstrate that synthetic CT can meet the clinical requirements for radiotherapy.
