Lee W H, Kim T-S, Cho M H, Ahn Y B, Lee S Y
Department of Biomedical Engineering, Kyung Hee University, Korea.
Phys Med Biol. 2006 Dec 7;51(23):6173-86. doi: 10.1088/0031-9155/51/23/016. Epub 2006 Nov 9.
In studying bioelectromagnetic problems, finite element analysis (FEA) offers several advantages over conventional methods such as the boundary element method. It allows truly volumetric analysis and incorporation of material properties such as anisotropic conductivity. For FEA, mesh generation is the first critical requirement and there exist many different approaches. However, conventional approaches offered by commercial packages and various algorithms do not generate content-adaptive meshes (cMeshes), resulting in numerous nodes and elements in modelling the conducting domain, and thereby increasing computational load and demand. In this work, we present efficient content-adaptive mesh generation schemes for complex biological volumes of MR images. The presented methodology is fully automatic and generates FE meshes that are adaptive to the geometrical contents of MR images, allowing optimal representation of conducting domain for FEA. We have also evaluated the effect of cMeshes on FEA in three dimensions by comparing the forward solutions from various cMesh head models to the solutions from the reference FE head model in which fine and equidistant FEs constitute the model. The results show that there is a significant gain in computation time with minor loss in numerical accuracy. We believe that cMeshes should be useful in the FEA of bioelectromagnetic problems.
在研究生物电磁问题时，与传统方法（如边界元法）相比，有限元分析（FEA）具有若干优势。它允许进行真正的体积分析，并纳入诸如各向异性电导率等材料特性。对于有限元分析而言，网格生成是首要关键要求，并且存在许多不同的方法。然而，商业软件包提供的传统方法以及各种算法不会生成内容自适应网格（cMeshes），这导致在对导电域进行建模时出现大量节点和单元，从而增加了计算负荷和需求。在这项工作中，我们针对磁共振图像的复杂生物体积提出了高效的内容自适应网格生成方案。所提出的方法是完全自动的，并且生成的有限元网格能自适应磁共振图像的几何内容，从而允许对有限元分析的导电域进行最优表示。我们还通过比较各种内容自适应网格头部模型的正向解与由精细且等距的有限元构成的参考有限元头部模型的解，评估了三维情况下内容自适应网格对有限元分析的影响。结果表明，计算时间有显著减少，而数值精度仅有轻微损失。我们认为内容自适应网格在生物电磁问题的有限元分析中应会很有用。
