Eddymotion¶
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
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].
