Comparative Evaluation Of Voxel Similarity Measures For Affine Registration Of Diffusion Tensor Mr Images

Deriving an accurate cost function for tensor valued data has been one of the main difficulties in diffusion tensor image (DTI) registration. In this work, we evaluate and compare five voxel similarity measures: Euclidean distance (ED), Log-Euclidean distance (LOG), distance based on diffusion profiles (DP), diffusion mode based similarity (MBS), and multichannel version of sum of squared differences (SSD). In evaluation we used an optimization-independent evaluation protocol to assess the capture range, the number of local minima, and cyclic registrations to evaluate consistency. Statistically significant differences were observed: DP and MBS were found to be the most consistent similarity measures, ED had the least number of local minima, and SSD was inferior to other similarity measures in all evaluations.