Methods and evaluations of MRI content-adaptive finite element mesh generation for bioelectromagnetic problems

Abstract

In studying bioelectromagnetic problems, finite element analysis (FEA) offers several advantages over conventional methods such as the boundary element method. It allows truly volumetric analysis and incorporation of material properties such as anisotropic conductivity. For FEA, mesh generation is the first critical requirement and there exist many different approaches. However, conventional approaches offered by commercial packages and various algorithms do not generate content-adaptive meshes (cMeshes), resulting in numerous nodes and elements in modelling the conducting domain, and thereby increasing computational load and demand. In this work, we present efficient content-adaptive mesh generation schemes for complex biological volumes of MR images. The presented methodology is fully automatic and generates FE meshes that are adaptive to the geometrical contents of MR images, allowing optimal representation of conducting domain for FEA. We have also evaluated the effect of cMeshes on FEA in three dimensions by comparing the forward solutions from various cMesh head models to the solutions from the reference FE head model in which fine and equidistant FEs constitute the model. 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.

Electric Field Model of Transcranial Electric Stimulation in Nonhuman Primates: Correspondence to Individual Motor Threshold

“…The MRI image intensities were corrected for bias field inhomogeneities that otherwise would reduce the accuracy of tissue segmentation [28]. We then applied content-preserving anisotropic diffusion filtering to remove the image noise while preserving content details and enhancing tissue boundaries [29–31]. Finally, non-brain regions were removed using the skull-stripping algorithm BET tool in FSL 4.1 (FMRIB Analysis Group, University of Oxford, UK) [32].…”

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

“…The correlation coefficient (CC) values in Table I show strong correlation between the Structure tensor-driven feature map and MRI, indicating the Structure tensor-driven feature extractor generates better demonstrative features. Also it produced the much lower root mean squared error (RMSE) and residual error (RE) values [9], indicating the reconstructed MRI is much closer to the original MRI.…”

“…In this work, we focus on the former and its effects on cMeshes to generate more efficient and accurate cMesh FE head models for effective neuro-electromagnetic imaging. The detailed methodology of generating cMeshes is given in [9]. …”

“…The details of this content-adaptive FE mesh generation technique are available in [9]. The advantages of our approach include i) accurate and individual-specific head model generation; ii) head representation with fast and automatic adaptive meshing; iii) maintaining the numerical accuracy of FEA with less number of nodes and elements, thus reducing the computational loads.…”

Realistic simulation of transcranial direct current stimulation via 3-d high-resolution finite element analysis: Effect of tissue anisotropy

“…Our adaptive mesh generation process starts with the MRI-content adaptive mesh generation as reported as cMesh in [9]. Then high-density meshes in the white matter region are generated adaptively according the density of fractional anisotropy of DTs (i.e., wMesh) as detailed in [6].…”