eddymotion 0.1.15

Estimating head-motion and deformations derived from eddy-currents in diffusion MRI data.

Retrospective estimation of head-motion between diffusion-weighted images (DWI) acquired within diffusion MRI (dMRI) experiments renders exceptionally challenging1 for datasets including high-diffusivity (or “high b”) images. These “high b” (b > 1000s/mm2) DWIs enable higher angular resolution, as compared to more traditional diffusion tensor imaging (DTI) schemes. UNDISTORT [1] (Using NonDistorted Images to Simulate a Template Of the Registration Target) was the earliest method addressing this issue, by simulating a target DW image without motion or distortion from a DTI (b=1000s/mm2) scan of the same subject. Later, Andersson and Sotiropoulos [2] proposed a similar approach (widely available within the FSL eddy tool), by predicting the target DW image to be registered from the remainder of the dMRI dataset and modeled with a Gaussian process. Besides the need for less data, eddy has the advantage of implicitly modeling distortions due to Eddy currents. More recently, Cieslak et al. [3] integrated both approaches in SHORELine, by (i) setting up a leave-one-out prediction framework as in eddy; and (ii) replacing eddy’s general-purpose Gaussian process prediction with the SHORE [4] diffusion model.

Eddymotion is an open implementation of eddy-current and head-motion correction that builds upon the work of eddy and SHORELine, while generalizing these methods to multiple acquisition schemes (single-shell, multi-shell, and diffusion spectrum imaging) using diffusion models available with DIPY [5].