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3D XFEM-based modeling of retraction for preoperative image update

机译:基于3D XFEM的撤回建模以进行术前图像更新

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Outcomes for neurosurgery patients can be improved by enhancing intraoperative navigation and guidance. Current navigation systems do not accurately account for intraoperative brain deformation. So far, most studies of brain deformation have focused on brain shift, whereas this paper focuses on the brain deformation due to retraction. The heart of our system is a 3D nonrigid registration technique using a biomechanical model driven by the deformations of key surfaces tracked between two intraoperative images. The key surfaces, e.g., the whole-brain region boundary and the lips of the retraction cut, thus deform due to the combination of gravity and retractor deployment. The tissue discontinuity due to retraction is handled via the eXtended Finite Element Method (XFEM), which has the appealing feature of being able to handle arbitrarily shaped discontinuity without any remeshing. Our approach is shown to significantly improve the alignment of intraoperative MRI.
机译:通过加强术中导航和指导,可以改善神经外科患者的治疗效果。当前的导航系统不能准确地说明术中大脑的变形。到目前为止,大多数关于脑变形的研究都集中在脑移位,而本文主要研究由于回缩引起的脑变形。我们系统的核心是使用生物力学模型的3D非刚性配准技术,该模型由两个术中图像之间跟踪的关键表面的变形驱动。因此,关键表面,例如全脑区域边界和缩回切口的唇缘,由于重力和牵开器展开的结合而变形。通过回缩有限元方法(XFEM)处理因回缩引起的组织间断,该方法具有吸引人的特征,即能够处理任意形状的间断而无需重新镶嵌。我们的方法显示可以显着改善术中MRI的对齐方式。

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