Vigneron Lara M, Duflot Marc P, Robe Pierre A, Warfield Simon K, Verly Jacques G
Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
Comput Aided Surg. 2009;14(1-3):1-20. doi: 10.3109/10929080903052677.
This paper considers an approach to improving outcomes for neurosurgery patients by enhancing intraoperative navigation and guidance. Currently, intraoperative navigation systems do not accurately account for brain shift or tissue resection. We describe how preoperative images can be incrementally updated to take into account any type of brain tissue deformation that may occur during surgery, and thus to improve the accuracy of image-guided navigation systems. For this purpose, we have developed a non-rigid image registration technique using a biomechanical model, which deforms based on the Finite Element Method (FEM). While the FEM has been used successfully for dealing with deformations such as brain shift, it has difficulty with tissue discontinuities. Here, we describe a novel application of the eXtended Finite Element Method (XFEM) in the field of image-guided surgery in order to model brain deformations that imply tissue discontinuities. In particular, this paper presents a detailed account of the use of XFEM for dealing with retraction and successive resections, and demonstrates the feasibility of the approach by considering 2D examples based on intraoperative MR images. To evaluate our results, we compute the modified Hausdorff distance between Canny edges extracted from images before and after registration. We show that this distance decreases after registration, and thus demonstrate that our approach improves alignment of intraoperative images.
本文探讨了一种通过增强术中导航与引导来改善神经外科手术患者治疗效果的方法。目前，术中导航系统无法准确考虑脑移位或组织切除情况。我们描述了如何逐步更新术前图像，以考虑手术过程中可能发生的任何类型的脑组织变形，从而提高图像引导导航系统的准确性。为此，我们开发了一种使用生物力学模型的非刚性图像配准技术，该模型基于有限元方法（FEM）变形。虽然有限元方法已成功用于处理诸如脑移位等变形，但它在处理组织不连续性方面存在困难。在此，我们描述扩展有限元方法（XFEM）在图像引导手术领域的一种新应用，以便对意味着组织不连续性的脑变形进行建模。特别是，本文详细介绍了使用扩展有限元方法处理牵拉和连续切除的情况，并通过基于术中磁共振图像的二维示例展示了该方法的可行性。为了评估我们的结果，我们计算配准前后从图像中提取的Canny边缘之间的修正豪斯多夫距离。我们表明配准后该距离减小，从而证明我们的方法改善了术中图像的对齐。
