Automatic procedure for realistic 3D finite element modelling of human brain for bioelectromagnetic computations
Aristovich, K. Y. & Khan, S. (2010). Automatic procedure for realistic 3D finite element modelling of human brain for bioelectromagnetic computations. Journal of Physics: Conference Series, 238(1), article number 012065. doi: 10.1088/1742-6596/238/1/012065
Abstract
Realistic computer modelling of biological objects requires building of very accurate and realistic computer models based on geometric and material data, type, and accuracy of numerical analyses. This paper presents some of the automatic tools and algorithms that were used to build accurate and realistic 3D finite element (FE) model of whole-brain. These models were used to solve the forward problem in magnetic field tomography (MFT) based on Magnetoencephalography (MEG). The forward problem involves modelling and computation of magnetic fields produced by human brain during cognitive processing. The geometric parameters of the model were obtained from accurate Magnetic Resonance Imaging (MRI) data and the material properties – from those obtained from Diffusion Tensor MRI (DTMRI). The 3D FE models of the brain built using this approach has been shown to be very accurate in terms of both geometric and material properties. The model is stored on the computer in Computer-Aided Parametrical Design (CAD) format. This allows the model to be used in a wide a range of methods of analysis, such as finite element method (FEM), Boundary Element Method (BEM), Monte-Carlo Simulations, etc. The generic model building approach presented here could be used for accurate and realistic modelling of human brain and many other biological objects.
Publication Type: | Article |
---|---|
Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Departments: | School of Science & Technology > Engineering |
SWORD Depositor: |
Available under License Creative Commons: Attribution 3.0.
Download (932kB) | Preview
Export
Downloads
Downloads per month over past year