Diffusion MRI Head Motion Correction Methods are Highly Accurate but Impacted by Denoising and Sampling Scheme

Correcting head motion artifacts in diffusion-weighted MRI (dMRI) scans is particularly challenging due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSLs Eddy. Recently, the 3dSHORE-based SHORELine method was introduced to correct any dMRI sequence that has more than one shell. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme, the pervasiveness of head motion, and the denoising strategy applied before head motion correction. Our study also provides an open and fully-reproducible workflow that could be used to accelerate evaluation studies of other dMRI processing methods in the future. HIGHLIGHTSO_LIBoth Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data C_LIO_LIDenoising with MP-PCA can improve head motion correction performance when Eddy is used C_LIO_LISHORELine effectively corrects motion in non-shelled acquisitions C_LI