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https://github.com/nipreps/eddymotion

Open-source eddy-current and head-motion correction for dMRI.
https://github.com/nipreps/eddymotion

diffusion-mri dipy distortion-correction dmriprep eddy-curr headmotion machine-learning nipype

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

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*Eddymotion*

============

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



.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4680599.svg

   :target: https://doi.org/10.5281/zenodo.4680599

   :alt: DOI



.. image:: https://img.shields.io/badge/License-Apache_2.0-blue.svg

   :target: https://github.com/nipreps/eddymotion/blob/main/LICENSE

   :alt: License



.. image:: https://img.shields.io/pypi/v/eddymotion.svg

   :target: https://pypi.python.org/pypi/eddymotion/

   :alt: Latest Version



.. image:: https://github.com/nipreps/eddymotion/actions/workflows/test.yml/badge.svg

   :target: https://github.com/nipreps/eddymotion/actions/workflows/test.yml

   :alt: Testing



.. image:: https://github.com/nipreps/eddymotion/actions/workflows/pages/pages-build-deployment/badge.svg

   :target: https://www.nipreps.org/eddymotion/main/index.html

   :alt: Documentation



.. image:: https://github.com/nipreps/eddymotion/actions/workflows/pythonpackage.yml/badge.svg

   :target: https://github.com/nipreps/eddymotion/actions/workflows/pythonpackage.yml

   :alt: Python package



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]_.



.. image:: https://raw.githubusercontent.com/nipreps/eddymotion/507fc9bab86696d5330fd6a86c3870968243aea8/docs/_static/eddymotion-flowchart.svg

   :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)