Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the\r\nhuman brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction\r\napproaches are typically optimized for standard resolution (~1mm) data and are not directly applicable to higher resolution\r\nimages. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and\r\nscales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The\r\nmethod uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping,\r\nhas nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow\r\nsulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained\r\nby topology-preserving level set approach. The methodââ?¬â?¢s performance is illustrated on exvivo images with 0.25ââ?¬â??0.35mm isotropic\r\nvoxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data\r\nsets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects.
Loading....