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A nonrigid registration method for correcting brain deformation induced by tumor resection

机译:一种非刚性配准方法,用于纠正肿瘤切除引起的脑部变形

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Purpose: This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model.Methods: The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables.Results: To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics-based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points.Conclusions: The proposed method can effectively correct brain deformation induced by tumor resection.
机译:目的:本文提出了一种非刚性配准方法,以使术前MRI与术中MRI对齐以补偿肿瘤切除过程中的脑部变形。该方法从两个方面扩展了传统的基于点的非刚性配准:(1)允许输入数据不完整;(2)使用异构生物力学模型模拟基础变形。方法:该方法将配准公式化为三变量(点对应,变形场和后方交会区域)功能最小化问题,其中点对应由模糊分配矩阵表示;变形场由分段线性函数表示,该分段线性函数由异质生物力学模型的应变能规整。切除区域由最大简单连接的四面体网格表示。开发了一个嵌套的期望和最大化框架来同时解决这三个变量。结果:为了评估这种方法,作者对合成数据和临床MRI数据进行了实验。合成实验证实了他们的假设,即从生物力学模型中删除其他元素可以提高配准的准确性。对25名患者的临床MRI实验表明,该方法优于ITK实施的基于物理的非刚性配准方法。当用鲁棒的Hausdorff距离度量在Canny边缘点上测量误差时,该方法平均可将精度提高2.88 mm,而通过六个解剖学点测量误差时,则平均可将精度提高1.56 mm。该方法可有效纠正肿瘤切除引起的脑变形。

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