3D prostate MR-TRUS non-rigid registration using dual optimization with volume-preserving constraint

Wu Qiu; Jing Yuan; Aaron Fenster

doi:10.1117/12.2217079

21 March 2016 3D prostate MR-TRUS non-rigid registration using dual optimization with volume-preserving constraint

Wu Qiu, Jing Yuan, Aaron Fenster

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97841P (2016) https://doi.org/10.1117/12.2217079
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

We introduce an efficient and novel convex optimization-based approach to the challenging non-rigid registration of 3D prostate magnetic resonance (MR) and transrectal ultrasound (TRUS) images, which incorporates a new volume preserving constraint to essentially improve the accuracy of targeting suspicious regions during the 3D TRUS guided prostate biopsy. Especially, we propose a fast sequential convex optimization scheme to efficiently minimize the employed highly nonlinear image fidelity function using the robust multi-channel modality independent neighborhood descriptor (MIND) across the two modalities of MR and TRUS. The registration accuracy was evaluated using 10 patient images by calculating the target registration error (TRE) using manually identified corresponding intrinsic fiducials in the whole prostate gland. We also compared the MR and TRUS manually segmented prostate surfaces in the registered images in terms of the Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). Experimental results showed that the proposed method with the introduced volume-preserving prior significantly improves the registration accuracy comparing to the method without the volume-preserving constraint, by yielding an overall mean TRE of 2:0±0:7 mm, and an average DSC of 86:5±3:5%, MAD of 1:4±0:6 mm and MAXD of 6:5±3:5 mm.

Citation Download Citation

Wu Qiu, Jing Yuan, and Aaron Fenster "3D prostate MR-TRUS non-rigid registration using dual optimization with volume-preserving constraint", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841P (21 March 2016); https://doi.org/10.1117/12.2217079

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