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Biomechanical model as a registration tool for image-guided neurosurgery: evaluation against BSpline registration

机译:生物力学模型作为图像引导神经外科手术的注册工具:针对BSpline注册的评估

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摘要

In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the Bspline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.
机译:在本文中,我们评估了通过使用基于特定于患者的生物力学模型预测的脑部变形与使用基于BSpline的自由形式变形算法进行配准的大脑变形预测的神经图像变形的准确性。与Bspline算法不同,基于生物力学的配准不需要术中MR图像,因为它非常昂贵且难以获取。仅大脑表面上的稀疏术中数据足以计算整个大脑的变形。在此贡献中,定性地比较了从两种方法获得的变形场,并检查了从图像中提取出的Canny边缘的重叠情况。我们定义了基于边缘的Hausdorff距离度量,以定量评估这两种算法的配准精度。定性和定量评估表明,尽管基于生物力学的配准算法使用的输入数据少得多,但配准精度至少与BSpline算法一样高。

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