A fast algorithm for generating large tetrahedral 3D finite element meshes from magnetic resonance tomograms

Abstract

Addresses the problem of generating three-dimensional (3D) finite element (FE) meshes from medical voxel datasets. With their background in cognitive neuroscience, the authors deal with brain MR tomograms of up to 256/sup 3/ voxels which contain a multitude of incompletely definable, complex-shaped objects. The authors describe an algorithm that allows the fast and stable creation of very large 3D meshes with well-defined geometric properties. The task of generating anisotropic meshes consisting of up to one million tetrahedra is fulfilled within minutes on a standard workstation. As the angles of the tetrahedra have a direct influence on the stability of the finite element analysis, special care has been taken to assess the element quality. The authors' algorithm is based on the idea of an image-based spatial decomposition of the problem domain yielding smaller subproblems that can efficiently be handled. The authors' primary purpose is to set up mechanical and electro-magnetical finite element models of the brain. However, their FE meshes could also be useful in other types of finite element analyses or as deformable volume models for shape descriptions and shape comparisons.