多腔室人体头部建模：利用GPU加速生成自适应四面体网格
Multi-compartment human head modeling: generating adaptive tetrahedral mesh with GPU acceleration
AI总结：
针对MRI多腔室头部模型的脑电图源定位需求，提出递归立体角标记与平滑、膨胀及优化流程，并在Zeffiro Interface中实现，可生成包含深部脑结构、精度高于1毫米的自适应四面体FE网格。
摘要：
本文介绍了一种高度自适应且自动化的方法，用于为通过磁共振成像（MRI）数据集获得的给定真实多腔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FE网格。我们提出了用于模型表面分割的递归立体角标记，随后通过一组平滑、膨胀和优化例程对其进行调整，以进一步提升FE网格质量。结果表明，我们的方法能够生成精度高于1毫米的FE网格，这在其三维结构离散化结果和脑电图源定位估计两方面均具有显著意义。所获得的人体头部FE网格可包含复杂的深部脑结构。我们的算法已在基于Matlab的开源Zeffiro Interface工具箱中实现，该工具箱具有高效的并行计算系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
