ECE Department, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America.
J Neural Eng. 2019 Apr;16(2):024001. doi: 10.1088/1741-2552/aafbb9. Epub 2019 Jan 3.
A study pertinent to the numerical modeling of cortical neurostimulation is conducted in an effort to compare the performance of the finite element method (FEM) and an original formulation of the boundary element fast multipole method (BEM-FMM) at matched computational performance metrics.
We consider two problems: (i) a canonic multi-sphere geometry and an external magnetic-dipole excitation where the analytical solution is available and; (ii) a problem with realistic head models excited by a realistic coil geometry. In the first case, the FEM algorithm tested is a fast open-source getDP solver running within the SimNIBS 2.1.1 environment. In the second case, a high-end commercial FEM software package ANSYS Maxwell 3D is used. The BEM-FMM method runs in the MATLAB 2018a environment.
In the first case, we observe that the BEM-FMM algorithm gives a smaller solution error for all mesh resolutions and runs significantly faster for high-resolution meshes when the number of triangular facets exceeds approximately 0.25 M. We present other relevant simulation results such as volumetric mesh generation times for the FEM, time necessary to compute the potential integrals for the BEM-FMM, and solution performance metrics for different hardware/operating system combinations. In the second case, we observe an excellent agreement for electric field distribution across different cranium compartments and, at the same time, a speed improvement of three orders of magnitude when the BEM-FMM algorithm used.
This study may provide a justification for anticipated use of the BEM-FMM algorithm for high-resolution realistic transcranial magnetic stimulation scenarios.
进行皮质神经刺激的数值建模研究,以努力比较有限元方法(FEM)和边界元快速多极方法(BEM-FMM)的原始公式在匹配的计算性能指标上的性能。
我们考虑两个问题:(i)具有多球体几何形状和外部磁偶极子激励的标准模型,其中存在解析解;(ii)具有由实际线圈几何形状激励的真实头部模型的问题。在第一种情况下,测试的 FEM 算法是在 SimNIBS 2.1.1 环境中运行的快速开源 getDP 求解器。在第二种情况下,使用高端商业 FEM 软件包 ANSYS Maxwell 3D。BEM-FMM 方法在 MATLAB 2018a 环境中运行。
在第一种情况下,我们观察到对于所有网格分辨率,BEM-FMM 算法给出的解误差较小,并且当三角形面的数量超过大约 0.25M 时,对于高分辨率网格,运行速度明显更快。我们还展示了其他相关的模拟结果,例如 FEM 的体网格生成时间、BEM-FMM 的位势积分计算所需的时间以及不同硬件/操作系统组合的解决方案性能指标。在第二种情况下,我们观察到不同颅骨隔间的电场分布非常吻合,同时,当使用 BEM-FMM 算法时,速度提高了三个数量级。
这项研究可能为预期使用 BEM-FMM 算法进行高分辨率的真实经颅磁刺激场景提供依据。