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Med Image Anal. 2017 Jul;39:29-43. doi: 10.1016/j.media.2017.04.001. Epub 2017 Apr 12.

Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention.

Author information

1
Department of Radiology & Biomedical Imaging, USA. Electronic address: john.onofrey@yale.edu.
2
Department of Radiology & Biomedical Imaging, USA; Department of Electrical Engineering, USA; Department of Biomedical Engineering, USA. Electronic address: lawrence.staib@yale.edu.
3
Eigen, Grass Valley, California, USA. Electronic address: sarad.sarkar@eigen.com.
4
Eigen, Grass Valley, California, USA. Electronic address: rajesh.venkataraman@eigen.com.
5
Department of Urology, Yale University, New Haven, Connecticut, USA. Electronic address: cayce.nawaf@yale.edu.
6
Department of Urology, Yale University, New Haven, Connecticut, USA. Electronic address: preston.sprenkle@yale.edu.
7
Department of Radiology & Biomedical Imaging, USA; Department of Biomedical Engineering, USA. Electronic address: xenophon.papademetris@yale.edu.

Abstract

Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. 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. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland. Segmentation of the prostate in TRUS imaging is itself a challenging task and prone to high variability. These segmentation errors can lead to poor registration and subsequently poor localization of biopsy targets, which may result in false-negative cancer detection. In this paper, we present a non-rigid surface registration approach to MR-TRUS fusion based on a statistical deformation model (SDM) of intra-procedural deformations derived from clinical training data. Synthetic validation experiments quantifying registration volume of interest overlaps of the PI-RADS parcellation standard and tests using clinical landmark data demonstrate that our use of an SDM for registration, with median target registration error of 2.98 mm, is significantly more accurate than the current clinical method. Furthermore, we show that the low-dimensional SDM registration results are robust to segmentation errors that are not uncommon in clinical TRUS data.

KEYWORDS:

Dimensionality reduction; Image-guided biopsy; Magnetic resonance imaging; Non-rigid registration; Prostate; Statistical deformation model; Trans-rectal ultrasound

PMID:
28431275
PMCID:
PMC5514316
DOI:
10.1016/j.media.2017.04.001
[Indexed for MEDLINE]
Free PMC Article

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