Abstract

Automatic computation of cortical thickness based on T1 MR images is a critical step for neuroanatomical population studies. Limited spatial resolution and partial volume effects, in which more than one tissue type is represented in each voxel, have a significant impact on the accuracy of thickness estimates, particularly if a hard intensity threshold is used to delineate cortical boundaries. This paper describes a novel method based on the anisotropic heat equation that explicitly accounts for the presence of partial tissue fraction maps to more accurately estimate cortical thickness. The anisotropic term uses gray matter fractions to incorporate partial tissue voxels into the thickness calculation, as demonstrated through simulations and experiments. It is also shown that the proposed method is robust to the effects of finite voxel resolution and blurring by a unit integral kernel. In comparison to methods based on hard intensity thresholds, the heat equation based method yields results with in-vivo data that are more consistent with histological findings reported in literature. We also performed test-retest study across scanners and sessions. The proposed method showed good reliability in these studies indicating robustness to scanner differences.