VMAT dose prediction in radiotherapy by using progressive refinement UNet

Abstract

The prediction of dose distribution in volumetric modulated arc therapy (VMAT) plays a curial role in radiotherapy. Accurate VMAT dose prediction for prostate cancer is always a great challenge because of the complexity of the VMAT dose distributions and the low contrast of the organs and tissues in the male pelvic CT images. In this paper, a novel progressive refinement UNet (PRUNet) with rank loss is proposed to address the aforementioned problem. On the one hand, the proposed PRUNet extends the traditional UNet with a novel progressive refinement module to generate more realistic dose distributions. The progressive refinement module generates multiple dose predictions with different resolutions in one forward pass and refines the dose prediction with predicted details at finer levels from lower resolution to higher resolution. On the other hand, it turns out that the dosimetric metrics are sensitive to the order relation among the dose values in dose distributions. A new rank loss function is proposed to optimize the similarity between the order relations among the dose values in dose predictions and that in real dose distributions. The proposed PRUNet is trained in a multi-task framework to jointly learn the dose distribution and the order relation among dose values. To evaluate the performance of the proposed PRUNet, the VMAT dose distributions and anatomical information of 64 prostate cancer patients are collected. The proposed model generates more accurate dose distributions with better quantitative dosimetric metrics than the state of the art UNet models do.