MR to Ultrasound Image Registration for Guiding Prostate Biopsy and Interventions

Authors:
Yipeng Hu;Hashim Uddin Ahmed;Clare Allen;Doug Pendsé;Mahua Sahu;Mark Emberton;David Hawkes;Dean Barratt
Affiliations:
Centre for Medical Image Computing, University College London, London, UK;Department of Urology, Division of Surgery & Interventional Science, University College London, London, UK;Department of Radiology, University College London Hospital (UCLH), London, UK;Department of Radiology, University College London Hospital (UCLH), London, UK;Department of Urology, Division of Surgery & Interventional Science, University College London, London, UK;Department of Urology, Division of Surgery & Interventional Science, University College London, London, UK;Centre for Medical Image Computing, University College London, London, UK;Centre for Medical Image Computing, University College London, London, UK
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

A method is described for registering preoperative magnetic resonance (MR) to intraoperative transrectal ultrasound (TRUS) images of the prostate gland. A statistical motion model (SMM) of the prostate is first built using training data provided by biomechanical simulations of the motion of a patient-specific finite element model, derived from a preoperative MR image. The SMM is then registered to a 3D TRUS image by maximising the likelihood of the shape of an SMM instance given a voxel-intensity-based feature, which represents an estimate of normal vector at the surface of the prostate gland. Using data acquired from 7 patients, the accuracy of registering T2 MR to 3D TRUS images was evaluated using anatomical landmarks inside the gland. The results show that the proposed registration method has a root-mean-square target registration error of 2.66 mm.