eddymotion 0.1.15
Pure python eddy-current and head-motion correction for dMRI, an extension of QSIprep's SHOREline algorithm (Cieslak, 2020) to multiple diffusion models.
Requires Python: >=3.10

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

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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 [#r1]_ (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 [#r2]_ 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. [#r3]_ 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 [#r4]_ 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 [#r5]_.


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   :alt: The eddymotion flowchart


.. [#r1] S. Ben-Amitay et al., Motion correction and registration of high b-value diffusion weighted images, Magnetic
   Resonance in Medicine 67:1694–1702 (2012)
.. [#r2] 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
.. [#r3] M. Cieslak et al., QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI data.
   Nature Methods, 18(7), 775–778 (2021)
.. [#r4] 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)
.. [#r5] E. Garyfallidis et al., Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8, 8
   (2014)