A learning-based nonrigid MRI-CBCT image registration method for MRI-guided prostate cancer radiotherapy

Radiation dose escalation to the dominant intraprostatic lesions in prostate cancer could improve tumor control. However, it is difficult to identify the dominant intraprostatic lesions on CT images. Multiparametric MRI has superior soft tissue contrast and is often used to detect the intraprostatic lesions. In this study, we developed a deep learning-based point cloud matching network to register the multiparametric MRI to the CBCT images for dominant lesion identification for prostate cancer radiotherapy. Prostate in both the CBCT and MRI was first automatically contoured and then meshed in to point clouds. A point cloud matching network was trained using point cloud pairs that were generated using finite element analysis. The trained network was able to perform MRI-CBCT prostate image registration with inherent biomechanical constraints. The mean and standard deviation of our method were 0.93±0.01, 1.66±0.10mm and 2.68±1.91mm for Dice similarity coefficient, mean surface distance and target registration error, respectively.