MICCAI 2013 - LNCS 8149-8151

Efficient Convex Optimization Approach to 3D Non-rigid MR-TRUS Registration

Yue Sun¹, Jing Yuan¹, Martin Rajchl¹, Wu Qiu¹, Cesare Romagnoli², and Aaron Fenster^{1, 2}

¹Imaging Research Labs, Robarts Research Institute, Western University, Canada

²Department of Medical Imaging, Western University, Canada

Abstract. In this study, we propose an efficient non-rigid MR-TRUS deformable registration method to improve the accuracy of targeting suspicious locations during a 3D ultrasound (US) guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighbourhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization based algorithmic scheme is introduced to extract the deformations which align the two MIND descriptors. The registration accuracy was evaluated using 10 patient images by measuring the TRE of manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone, and performance metrics (DSC, MAD and MAXD) for the apex, mid-gland and base of the prostate were also calculated by comparing two manually segmented prostate surfaces in the registered 3D MR and TRUS images. Experimental results show that the proposed method yielded an overall mean TRE of 1.74 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.

Keywords: Non-rigid Image Registration, Convex Optimization, MR-TRUS prostate registration, MIND Similarity Measurement

LNCS 8149, p. 195 ff.

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