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.