Title :
Computed Basis Functions for Finite Element Analysis Based on Tomographic Data
Author :
Gu, Huanhuan ; Gotman, Jean ; Webb, Jon P.
Author_Institution :
Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada
Abstract :
In bioelectromagnetics, the structures in which the electromagnetic field is to be computed are sometimes defined by a fine grid of voxels (3-D cells) whose tissue types are obtained by tomography. A novel finite element method is proposed for such cases. A simple, regular mesh of cube elements is constructed, each containing the same, integer number of voxels. There may be several different tissues present within an element, but this is accommodated by computing element basis functions that approximately respect the interface conditions between different tissues. Results are presented for a test model of 1283 voxels, consisting of nested dielectric cubes, driven by specified charges. The electrostatic potential computed with the new method agrees well with that of a conventional finite element code: the rms difference along the sample line is 1.5% of the highest voltage. Results are also presented for the potential due to a current dipole placed in a brain model of 181 × 217 × 181 voxels, derived from MRI data. The new method gives potentials that are different to those obtained by treating each voxel as an element by 1% of the peak voltage, yet the global finite element matrix has a dimension which is more than 50 times smaller.
Keywords :
biological tissues; biomedical MRI; brain models; cellular biophysics; data analysis; electrostatics; mesh generation; 3D cells; MRI data; bioelectromagnetics; biological tissues; brain model; computing element basis functions; electrostatic potential; finite element analysis; interface conditions; nested dielectric cubes; regular mesh construction; rms difference; tomographic data; voxels; Boundary conditions; Electric potential; Equations; Face; Finite element methods; Materials; Mathematical model; Bioelectromagnetism; MRI; computational modeling; electroencephalography; electrostatics; finite element method (FEM); Brain; Computer Simulation; Finite Element Analysis; Humans; Magnetic Resonance Imaging; Models, Biological; Signal Processing, Computer-Assisted; Static Electricity;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2011.2158212