Prostate Dose Prediction in HDR Brachytherapy using Unsupervised Multi-Atlas Fusion

摘要

In this study, we propose a new deep learning-based method to predict radiation dose for prostate cancer patients undergoing high-dose-rate (HDR) brachytherapy. The proposed framework consists of three major steps, which are deformable registration via registration network (Reg-Net), consolidation and needle regression. To model the global spatial relationship among multiple organs, binary masks of the target and organs at risk were transformed into distance maps which describe the distance of each local voxel to the organ surfaces. Then, Reg-Net is utilized to deformably register the distance maps and contours of multi-atlas to match those of an arrival patient. By spatial transformation and consolidation, the corresponding dose plans of top-ranked multiple atlases are registered and fused to generate a synthetic HDR dose distribution of an arrival patient. A retrospective study on 40 patients was used to evaluate the proposed method's efficiency. Comparison of dose volume histogram metrics of predicted dose and clinical delivered dose shows that no statistically significant difference is found. These results demonstrate the feasibility and efficacy of our deep learning-based method for HDR prostate dose prediction.