This paper introduces a highly adaptive and automated approach for generating Finite Element (FE) discretization for a given realistic multi-compartment human head model obtained through magnetic resonance imaging (MRI) dataset. We aim at obtaining accurate tetrahedral FE meshes for electroencephalographic source localization. We present recursive solid angle labeling for the surface segmentation of the model and then adapt it with a set of smoothing, inflation, and optimization routines to further enhance the quality of the FE mesh. The results show that our methodology can produce FE mesh with an accuracy greater than 1 millimeter, significant with respect to both their 3D structure discretization outcome and electroencephalographic source localization estimates. FE meshes can be achieved for the human head including complex deep brain structures. Our algorithm has been implemented using the open Matlab-based Zeffiro Interface toolbox with it effective time-effective parallel computing system.

翻译：本文介绍了一种高度自适应且自动化的方法，用于通过磁共振成像(MRI)数据集获得的真实多隔室人体头部模型生成有限元(FE)离散化。我们的目标是获得用于脑电图源定位的精确四面体有限元网格。我们提出递归立体角标记法用于模型表面分割，随后结合一系列平滑、膨胀和优化程序以进一步提升有限元网格质量。结果表明，我们的方法可生成精度高于1毫米的有限元网格，其在三维结构离散化结果和脑电图源定位估算方面均具有显著优势。该方法能实现包括复杂深部脑结构在内的人体头部有限元网格生成。我们的算法已基于开源Matlab平台Zeffiro接口工具箱实现，并配合其高效的时间并行计算系统。