Numerical Evaluation of MRI Content-adaptive Finite Element Head Models via EEG Forward Solutions

Abstract

Finite element analysis (FEA) is an effective tool for the analysis of bioelectromagnetism. It has been successfully applied to various problems over other conventional methods such as boundary element analysis and finite difference analysis. However, its use has been limited due to overwhelming computational load despite of its analytical power. We have previously developed a novel mesh generation scheme that produces FE meshes that are content-adaptive to given images. These image content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with less numbers of nodes and elements, thus lessen the computational load. However, their impacts on FEA have not been assessed yet. In this study, we evaluated the impact of cMeshes on FEA via comparing the forward solutions with various cMesh head models to the solutions from the reference FE head model in which fine and equidistant FEs constitute the model. Correlation coefficient (CC), relative error (RE), and computation time (CT) are used as performance indices. The results show that there is a significant gain in computation time with minor loss in numerical accuracy. We believe that cMeshes should be useful in the FEA of bioelectromagnetic problems.