Learning nonrigid deformations for constrained point-based registration for image-guided MR-TRUS prostate intervention

This paper presents and validates a low-dimensional nonrigid registration method for fusing magnetic resonance imaging (MRI) and trans-rectal ultrasound (TRUS) in image-guided prostate biopsy. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. Conventional clinical practice uses TRUS to guide prostate biopsies when there is a suspicion of cancer. Pre-procedural MRI information can reveal lesions and may be fused with intra-procedure TRUS imaging to provide patient-specific, localization of lesions for targeting. The state-of-the-art MRI-TRUS nonrigid image fusion process relies upon semi-automated segmentation of the prostate in both the MRI and TRUS images. In this paper, we develop a fast, automated nonrigid registration approach to MRI-TRUS fusion based on a statistical deformation model of intra-procedural deformations derived from a clinical sample.