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Comparative Study of Brain Deformation Estimation Methods

机译:脑变形估计方法的比较研究

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摘要

Shift of brain tissues during surgical procedures affects the precision of image-guided neurosurgery (IGNS). To improve the accuracy of the alignment between the patient and images, finite element model-based non-rigid registration methods have been investigated. The best prior estimate (BPE), the forced displacement method (FDM), the weighted basis solutions (WBS), and the adjoint equations method (AEM) are versions of this approach that have appeared in the literature. In this paper, we present a quantitative comparison study on a set of three patient cases. Three-dimensional displacement data from the surface and subsurface was extracted using the intra-operative ultrasound (iUS) and intraoperative stereovision (iSV). These data are then used as the "ground truth" in a quantitative study to evaluate the accuracy of estimates produced by the finite element models. Different types of clinical cases are presented, including distension and combination of sagging and distension. In each case, a comparison of the performance is made with the four methods. The AEM method which recovered 26-62% of surface brain motion and 20-43% of the subsurface deformation, produced the best fit between the measured data and the model estimates.
机译:外科手术过程中脑组织的移位会影响图像引导神经外科手术(IGNS)的精度。为了提高患者和图像之间对准的准确性,已经研究了基于有限元模型的非刚性配准方法。最佳先验估计(BPE),强制位移法(FDM),加权基础解(WBS)和伴随方程法(AEM)是文献中出现的这种方法的版本。在本文中,我们对一组三例患者进行了定量比较研究。使用术中超声(iUS)和术中立体视觉(iSV)提取表面和次表面的三维位移数据。然后将这些数据用作定量研究中的“基本事实”,以评估有限元模型产生的估计值的准确性。介绍了不同类型的临床病例,包括扩张以及下垂和扩张的结合。在每种情况下,都使用四种方法对性能进行比较。 AEM方法可恢复26-62%的表面大脑运动和20-43%的表面下变形,在测量数据和模型估计值之间达到最佳拟合。

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