Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction - Huang, Ying; Pi, Yifei; Ma, Kui; Miao, Xiaojuan; Fu, Sichao; Chen, Hua; Wang, Hao; Gu, Hengle; Shao, Yan; Duan, Yanhua; Feng, Aihui; Wang, Jiyong; et al. (Frontiers in Oncology, 2021)
The dose verification in radiotherapy quality assurance is time-consuming and places a heavy workload on medical physicists. To provide clinical tool to perform patient specific QA accurately, the Unet++ is investigated classify fail or pass fields, predict gamma passing rates for different gamma criteria, and predict dose difference from virtual patient specific quality assurance in radiotherapy. UNet++ was trained and tested in 109 IMRT plans were selected including 568 fields. All plans used Portal Dosimetry for dose verification pre-treatment. Planar dose distribution of each field was used as input for Unet++, QA classification results, gamma passing rates of different gamma criteria, and dose difference were used as output. In test set, the accuracy of the classification model was 95.5%. The MAE were 0.92, 1.12, 2.26, 2.52 and the RMSE 1.52, 1.73, 3.21, 3.60 for 3%/3mm、3%/2 mm、2%/3 mm、2%/2 mm , respectively. The trend and position of predicted dose difference were consistent with the measured dose difference. In conclusion, the Virtual QA based on Unet++ can be used to classify the field passed or not, predict gamma pass rate for different gamma criteria, and predict dose difference. The results show that Unet++ based Virtual QA is promising in quality assurance for radiotherapy.