Cortical surface reconstruction using a topology preserving geometric deformable model

Accurate reconstruction of the cortical surface of the brain from magnetic resonance images is an important objective in biomedical image analysis. Parametric deformable surface models are usually used because they incorporate prior information, yield subvoxel accuracy, and automatically preserve topology. These algorithms are very computationally costly, however, particularly if self-intersection prevention is imposed. Geometric deformable surface models, implemented using level set methods, are computationally fast and are automatically free from self-intersections, but are unable to guarantee the correct topology. This paper describes both a new geometric deformable surface model which preserves topology and an overall strategy for reconstructing the inner, central, and outer surfaces of the brain cortex. The resulting algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self intersections. We ran the algorithm on 21 data sets and show detailed results for a typical data set. We also show a preliminary validation using landmarks manually placed as a truth model on six of the data sets.