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超越有限元:利用无网格方法进行全面、个体化的神经外科模拟。

Beyond finite elements: a comprehensive, patient-specific neurosurgical simulation utilizing a meshless method.

机构信息

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley, Perth, Western Australia 6909, Australia.

出版信息

J Biomech. 2012 Oct 11;45(15):2698-701. doi: 10.1016/j.jbiomech.2012.07.031. Epub 2012 Aug 27.

Abstract

To be useful in clinical (surgical) simulations, a method must use fully nonlinear (both geometric and material) formulations to deal with large (finite) deformations of tissues. The method must produce meaningful results in a short time on consumer hardware and not require significant manual work while discretizing the problem domain. In this paper, we showcase the Meshless Total Lagrangian Explicit Dynamics Method (MTLED) which meets these requirements, and use it for computing brain deformations during surgery. The problem geometry is based on patient-specific MRI data and includes the parenchyma, tumor, ventricles and skull. Nodes are distributed automatically through the domain rendering the normally difficult problem of creating a patient-specific computational grid a trivial exercise. Integration is performed over a simple, regular background grid which does not need to conform to the geometry boundaries. Appropriate nonlinear material formulation is used. Loading is performed by displacing the parenchyma surface nodes near the craniotomy and a finite frictionless sliding contact is enforced between the skull (rigid) and parenchyma. The meshless simulation results are compared to both intraoperative MRIs and Finite Element Analysis results for multiple 2D sections. We also calculate Hausdorff distances between the computed deformed surfaces of the ventricles and those observed intraoperatively. The difference between previously validated Finite Element results and the meshless results presented here is less than 0.2mm. The results are within the limits of neurosurgical and imaging equipment accuracy (~1 mm) and demonstrate the method's ability to fulfill all of the important requirements for surgical simulation.

摘要

为了在临床(手术)模拟中有用,方法必须使用完全非线性(几何和材料)公式来处理组织的大(有限)变形。该方法必须在消费硬件上短时间内产生有意义的结果,并且在离散问题域时不需要大量的手动工作。在本文中,我们展示了满足这些要求的无网格整体拉格朗日显式动力学方法(MTLED),并将其用于计算手术过程中的大脑变形。问题几何形状基于患者特定的 MRI 数据,包括实质、肿瘤、脑室和颅骨。节点通过域自动分布,从而使创建特定于患者的计算网格的通常困难问题变得微不足道。积分是在简单的规则背景网格上进行的,该网格不需要与几何边界一致。使用适当的非线性材料公式。通过在颅骨切开术附近的实质表面节点上进行位移来施加载荷,并在颅骨(刚性)和实质之间强制进行无摩擦的有限滑动接触。将无网格模拟结果与术中 MRI 和多个 2D 切片的有限元分析结果进行比较。我们还计算了计算出的脑室变形表面与术中观察到的变形表面之间的 Hausdorff 距离。这里呈现的无网格结果与先前经过验证的有限元结果之间的差异小于 0.2mm。结果在神经外科和成像设备精度的限制内(约 1 毫米),并证明了该方法满足手术模拟所有重要要求的能力。

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本文引用的文献

1
Real-Time Nonlinear Finite Element Computations on GPU - Application to Neurosurgical Simulation.
Comput Methods Appl Mech Eng. 2010 Dec 15;199(49-52):3305-3314. doi: 10.1016/j.cma.2010.06.037.
2
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Prog Biophys Mol Biol. 2010 Dec;103(2-3):292-303. doi: 10.1016/j.pbiomolbio.2010.09.001. Epub 2010 Sep 22.
3
Image registration of pre-procedural MRI and intra-procedural CT images to aid CT-guided percutaneous cryoablation of renal tumors.
Int J Comput Assist Radiol Surg. 2011 Jan;6(1):111-7. doi: 10.1007/s11548-010-0485-9. Epub 2010 May 25.
4
Computation of intra-operative brain shift using dynamic relaxation.
Comput Methods Appl Mech Eng. 2009 Sep 1;198(41):3313-3320. doi: 10.1016/j.cma.2009.06.012.
5
Current progress in patient-specific modeling.
Brief Bioinform. 2010 Jan;11(1):111-26. doi: 10.1093/bib/bbp049. Epub 2009 Dec 2.
6
IA-FEMesh: an open-source, interactive, multiblock approach to anatomic finite element model development.
Comput Methods Programs Biomed. 2009 Apr;94(1):96-107. doi: 10.1016/j.cmpb.2008.12.003. Epub 2009 Jan 20.
7
On the unimportance of constitutive models in computing brain deformation for image-guided surgery.
Biomech Model Mechanobiol. 2009 Feb;8(1):77-84. doi: 10.1007/s10237-008-0118-1. Epub 2008 Feb 2.
9
Patient-specific model of brain deformation: application to medical image registration.
J Biomech. 2007;40(4):919-29. doi: 10.1016/j.jbiomech.2006.02.021. Epub 2006 May 6.
10
Orbital and maxillofacial computer aided surgery: patient-specific finite element models to predict surgical outcomes.
Comput Methods Biomech Biomed Engin. 2005 Aug;8(4):259-65. doi: 10.1080/10255840500289921.

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