Deep learning method for tomotherapy delivery quality assurance: prediction of three-dimensional dose distribution and performance evaluation on phantom

High-tech radiotherapy capable to provide complex dose delivery modalities is one of the most important treatment modalities for cancer patients, making essential to evaluate with accuracy the clinical machine performances and the quality of the treatment plans [1–3]. The operation of Delivery Quality Assurance (DQA) is repetitive and involving both workforce and Linac bunker occupational time. To work around this problem, we developed new deep neural network models capable of predicting passing rates a priori for Helical Tomotherapy (HT) DQA in 3D voxel-by-voxel dose prediction. In this paper we evaluated net performances, focusing on learning quality in function of specific machine parameters.