Model-based registration to correct for motion between acquisitions in diffusion MR imaging

Abstract

In diffusion tensor MRI, a number of diffusion-weighted images with different diffusion-weighting gradient directions are acquired during scanning. The tensor calculation assumes that each voxel corresponds to the same anatomical location in all the measurements. Movements and distortions violate this assumption and typically the images are realigned before model fitting. The traditional method uses a non-diffusion- weighted image as the reference for registration, but the differences between diffusion-weighted images and the non- diffusion weighted reference image can cause mismatching to occur during registration, even using metrics like the mutual information (MI) that accounts for non-linear contrast differences. We propose alternative model-based methods to improve motion correction and avoid the errors that the traditional method introduces. We demonstrate quantitative improvements using the new approaches on a full data with slight, but typical, movement during acquisition.