Mesh Quality Analysis of MRI Content-adaptive FE Head Models for Neuro-Electromagnetic Imaging

Realistic finite element (FE) head models for neuro-electromagnetic imaging are getting more attention due to their analytic advantages over conventional models. To improve the numerical efficiency, we have previously developed a novel mesh generation scheme that produces FE head models automatically that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with less number of nodes and elements, thus lessen the computational loads. In general, the cMesh generation is affected by the selection of feature maps derived from MRI. In this study, we have tested the effects of various feature maps on the generation of cMesh FE head models. Also we have evaluated the quality of cMesh FE head models to check their suitability for neuro-electromagnetic imaging using EEG and MEG. The results suggest that the cMesh FE head models with properly selected feature maps do show acceptable quality to be used in neuro-electromagnetic imaging.