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Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:1592-1595.

LEARNING NONRIGID DEFORMATIONS FOR CONSTRAINED POINT-BASED REGISTRATION FOR IMAGE-GUIDED MR-TRUS PROSTATE INTERVENTION.

Author information

1
Department of Diagnostic Radiology, Yale University, New Haven, CT, USA.
2
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.
3
Department of Eigen, Grass Valley, CA, USA.
4
Department of Diagnostic Radiology, Yale University, New Haven, CT, USA ; Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Abstract

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.

KEYWORDS:

image-guided intervention; nonrigid registration; prostate biopsy; robust point matching; statistical deformation model

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