Methods and evaluations of MRI content-adaptive finite element mesh generation for bioelectromagnetic problems.
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.
Lee
W H
WH
Department of Biomedical Engineering, Kyung Hee University, Korea.
Kim
T-S
TS
Cho
M H
MH
Ahn
Y B
YB
Lee
S Y
SY
D000465:Algorithms; D016880:Anisotropy; D055592:Biophysical Phenomena; D001703:Biophysics; D001921:Brain / Q000033:anatomy & histology; D004553:Electric Conductivity; D017097:Electric Impedance; D055590:Electromagnetic Phenomena / Q000706:statistics & numerical data*; D020342:Finite Element Analysis; D006257:Head / Q000033:anatomy & histology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008953:Models, Anatomic; D017711:Nonlinear Dynamics