A Combined Approach for the Elimination of Partial Volume Effects in Diffusion MRI

Klaus H. Fritzsche¹, ², Bram Stieltjes², Thomas van Bruggen², Hans-Peter Meinzer², Carl-Fredrik Westin¹, Ofer Pasternak¹

Partial volume is a major confounding factor in the analysis of diffusion tensor imaging datasets. The mixing effects of different compartments within each voxel are non-linear, acquisition dependent, and likely to exceed microstructural effects of interest. In this work, we combine two approaches to ameliorate these problems: Free-water elimination that eliminates intra-voxel CSF contamination and partial volume clustering that classifies and probabilistically selects all non-contaminated voxels. We demonstrate the increased sensitivity of this method in a tract specific analysis of the corpus callosum, recognizing abnormalities on a clinical dataset of Alzheimers disease, compared with matched controls.