nipreps / eddymotion

Open-source eddy-current and head-motion correction for dMRI.

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 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. UNDISTORT¹ (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 Sotiropoulos² 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 SHORE⁴ 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 DIPY⁵.

---

1. S. Ben-Amitay et al., Motion correction and registration of high b-value diffusion weighted images, Magnetic Resonance in Medicine 67:1694–1702 (2012)↩

2. J. L. R. Andersson. et al., An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging, NeuroImage 125 (2016) 1063–1078↩

3. M. Cieslak et al., QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI data. Nature Methods, 18(7), 775–778 (2021)↩

4. E. Ozarslan et al., Simple Harmonic Oscillator Based Reconstruction and Estimation for Three-Dimensional Q-Space MRI. in Proc. Intl. Soc. Mag. Reson. Med. vol. 17 1396 (2009)↩

5. E. Garyfallidis et al., Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8, 8 (2014)↩