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.

Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging. 2015 Apr [Epub]

John A Onofrey, Lawrence H Staib, Saradwata Sarkar, Rajesh Venkataraman, Xenophon Papademetris

Department of Diagnostic Radiology, Yale University, New Haven, CT, USA. , Department of Diagnostic Radiology, Yale University, New Haven, CT, USA ; Department of Biomedical Engineering, Yale University, New Haven, CT, USA ; Department of Electrical Engineering, Yale University, New Haven, CT, USA. , Department of Eigen, Grass Valley, CA, USA. , Department of Eigen, Grass Valley, CA, USA. , Department of Diagnostic Radiology, Yale University, New Haven, CT, USA ; Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

PubMed