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3D Prostate MR-TRUS Non-Rigid Registration Using Dual Optimization with Volume-Preserving Constraint

机译:使用具有体积保留约束的双重优化实现3D前列腺MR-TRUS非刚性配准

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摘要

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.
机译:我们针对3D前列腺磁共振(MR)和经直肠超声(TRUS)图像的具有挑战性的非刚性配准引入了一种有效且新颖的基于凸优化的方法,该方法结合了新的体积保留约束条件,从而从根本上提高了针对可疑区域的准确性在3D TRUS引导下进行前列腺活检。尤其是,我们提出了一种快速顺序凸优化方案,以使用跨MR和TRUS两种模态的稳健的多通道模态无关邻域描述符(MIND)有效地最小化所采用的高度非线性图像保真度函数。使用手动识别的整个前列腺中的相应固有基准,通过计算目标配准错误(TRE),使用10张患者图像评估配准准确性。我们还根据Dice相似系数(DSC),平均绝对表面距离(MAD)和最大绝对表面距离(MAXD),比较了已注册图像中MR和TRUS手动分割的前列腺表面。实验结果表明,与没有体积保留约束的方法相比,引入了保留体积先验的方法显着提高了配准精度,其总体平均TRE为2.0±0.7 mm,平均DSC为86.5±3.5 %,MAD为1.4±0.6毫米,MAXD为6.5±3.5毫米。

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