Multi-compartment human head modeling: generating adaptive tetrahedral mesh with GPU acceleration

TL;DR

This paper presents an automated, GPU-accelerated method for generating highly accurate tetrahedral finite element meshes of human head models from MRI data, improving EEG source localization accuracy.

Contribution

The authors introduce a novel recursive surface segmentation and mesh optimization pipeline that produces detailed, adaptive FE meshes including deep brain structures, with GPU acceleration for efficiency.

Findings

Achieves FE mesh accuracy greater than 1 mm.

Successfully models complex deep brain structures.

Utilizes GPU acceleration for faster mesh generation.

Abstract

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.