Approaches for correcting motion in diffusion-weighted imaging acquired using diffusion gradient cycling

Motivation: Diffusion gradient cycling (DGC) enables more efficient diffusion-weighted imaging (DWI) scanning, but it is not compatible with typical preprocssing pipelines. Goal(s): Our goal is to propose two approaches for correcting motion in DWI data acquired using DGC: slice-to-volume registration and volume-to-volume registration with the assistance of slice reordering. Approach: Intentional motion was introduced during the DWI acquisition, and the proposed approaches were implemented and applied to remove artifacts caused by this motion. Results: Both approaches effectively eliminated motion-induced artifacts, and the intentional motion was estimated correctly. Impact: Motion-induced artifacts can be eliminated, and correct motion estimation can be achieved in DWI acquired with DGC. Our proposed approaches are publicly available and can be easily integrated into preprocessing pipelines.