Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Med Eng Phys. 2012 Jan;34(1):85-98. doi: 10.1016/j.medengphy.2011.07.002. Epub 2011 Aug 4.
This study proposes an advanced finite element (FE) head modeling technique through which high-resolution FE meshes adaptive to the degree of tissue anisotropy can be generated. Our adaptive meshing scheme (called wMesh) uses MRI structural information and fractional anisotropy maps derived from diffusion tensors in the FE mesh generation process, optimally reflecting electrical properties of the human brain. We examined the characteristics of the wMeshes through various qualitative and quantitative comparisons to the conventional FE regular-sized meshes that are non-adaptive to the degree of white matter anisotropy. We investigated numerical differences in the FE forward solutions that include the electrical potential and current density generated by current sources in the brain. The quantitative difference was calculated by two statistical measures of relative difference measure (RDM) and magnification factor (MAG). The results show that the wMeshes are adaptive to the anisotropic density of the WM anisotropy, and they better reflect the density and directionality of tissue conductivity anisotropy. Our comparison results between various anisotropic regular mesh and wMesh models show that there are substantial differences in the EEG forward solutions in the brain (up to RDM=0.48 and MAG=0.63 in the electrical potential, and RDM=0.65 and MAG=0.52 in the current density). Our analysis results indicate that the wMeshes produce different forward solutions that are different from the conventional regular meshes. We present some results that the wMesh head modeling approach enhances the sensitivity and accuracy of the FE solutions at the interfaces or in the regions where the anisotropic conductivities change sharply or their directional changes are complex. The fully automatic wMesh generation technique should be useful for modeling an individual-specific and high-resolution anisotropic FE head model incorporating realistic anisotropic conductivity distributions towards more accurate analysis of bioelectromagnetic problems.
本研究提出了一种先进的有限元（FE）头部建模技术，通过该技术可以生成自适应组织各向异性程度的高分辨率 FE 网格。我们的自适应网格方案（称为 wMesh）在 FE 网格生成过程中使用 MRI 结构信息和来自扩散张量的各向异性分数图，最佳地反映人脑的电特性。我们通过与非自适应白质各向异性程度的常规 FE 规则尺寸网格的各种定性和定量比较来检查 wMesh 的特性。我们研究了 FE 正解的数值差异，其中包括大脑中电流源产生的电场电位和电流密度。通过两个统计度量相对差异度量（RDM）和放大因子（MAG）来计算定量差异。结果表明，wMesh 自适应于 WM 各向异性的各向异性密度，并且更好地反映了组织电导率各向异性的密度和方向性。我们在各种各向异性规则网格和 wMesh 模型之间的比较结果表明，大脑中的 EEG 正解存在很大差异（在电场电位中高达 RDM=0.48 和 MAG=0.63，在电流密度中高达 RDM=0.65 和 MAG=0.52）。我们的分析结果表明，wMesh 产生的正解与常规规则网格不同。我们提出了一些结果，即 wMesh 头部建模方法增强了在各向异性电导率急剧变化或其方向变化复杂的界面或区域中的 FE 解的灵敏度和准确性。全自动 wMesh 生成技术对于建模包含现实各向异性电导率分布的个体特定和高分辨率各向异性 FE 头部模型，以更准确地分析生物电磁问题应该是有用的。
