{"id":19807846,"url":"https://github.com/nipreps/eddymotion","last_synced_at":"2025-08-28T14:32:28.681Z","repository":{"id":37863437,"uuid":"341632045","full_name":"nipreps/eddymotion","owner":"nipreps","description":"Open-source eddy-current and head-motion correction for dMRI.","archived":false,"fork":false,"pushed_at":"2024-12-08T20:20:54.000Z","size":15907,"stargazers_count":15,"open_issues_count":43,"forks_count":16,"subscribers_count":11,"default_branch":"main","last_synced_at":"2024-12-18T12:50:11.119Z","etag":null,"topics":["diffusion-mri","dipy","distortion-correction","dmriprep","eddy-curr","headmotion","machine-learning","nipype"],"latest_commit_sha":null,"homepage":"https://nipreps.org/eddymotion","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nipreps.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGES.rst","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-02-23T17:19:32.000Z","updated_at":"2024-12-08T20:18:53.000Z","dependencies_parsed_at":"2023-02-11T22:01:14.987Z","dependency_job_id":"afc5546c-05e9-45f3-bd30-f908e459bf12","html_url":"https://github.com/nipreps/eddymotion","commit_stats":{"total_commits":286,"total_committers":14,"mean_commits":"20.428571428571427","dds":0.5559440559440559,"last_synced_commit":"bfb8fcc68d4f71ed7bb100b4cde018fdd02baa20"},"previous_names":["nipreps/eddymotioncorrection"],"tags_count":8,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Feddymotion","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Feddymotion/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Feddymotion/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Feddymotion/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nipreps","download_url":"https://codeload.github.com/nipreps/eddymotion/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":231278823,"owners_count":18351935,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["diffusion-mri","dipy","distortion-correction","dmriprep","eddy-curr","headmotion","machine-learning","nipype"],"created_at":"2024-11-12T09:12:03.909Z","updated_at":"2025-08-28T14:32:28.666Z","avatar_url":"https://github.com/nipreps.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":".. important:: *eddymotion* will be continued as *NiFreeze*\n\n   In November 2024, the *NiPreps Steering Committee* brought to the *Bi-monthly Roundup*\n   the discussion about re-branding *eddymotion* to better reflect its aspirations to\n   perform on diverse modalities.\n\n   The project has been moved to `nipreps/nifreeze \u003chttps://github.com/nipreps/nifreeze\u003e`__.\n\n*Eddymotion*\n============\nEstimating head-motion and deformations derived from eddy-currents in diffusion MRI data.\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4680599.svg\n   :target: https://doi.org/10.5281/zenodo.4680599\n   :alt: DOI\n\n.. image:: https://img.shields.io/badge/License-Apache_2.0-blue.svg\n   :target: https://github.com/nipreps/eddymotion/blob/main/LICENSE\n   :alt: License\n\n.. image:: https://img.shields.io/pypi/v/eddymotion.svg\n   :target: https://pypi.python.org/pypi/eddymotion/\n   :alt: Latest Version\n\n.. image:: https://github.com/nipreps/eddymotion/actions/workflows/test.yml/badge.svg\n   :target: https://github.com/nipreps/eddymotion/actions/workflows/test.yml\n   :alt: Testing\n\n.. image:: https://github.com/nipreps/eddymotion/actions/workflows/pages/pages-build-deployment/badge.svg\n   :target: https://www.nipreps.org/eddymotion/main/index.html\n   :alt: Documentation\n\n.. image:: https://github.com/nipreps/eddymotion/actions/workflows/pythonpackage.yml/badge.svg\n   :target: https://github.com/nipreps/eddymotion/actions/workflows/pythonpackage.yml\n   :alt: Python package\n\nRetrospective estimation of head-motion between diffusion-weighted images (DWI) acquired within\ndiffusion MRI (dMRI) experiments renders exceptionally challenging1 for datasets including\nhigh-diffusivity (or “high b”) images.\nThese “high b” (b \u003e 1000s/mm2) DWIs enable higher angular resolution, as compared to more traditional\ndiffusion tensor imaging (DTI) schemes.\nUNDISTORT [#r1]_ (Using NonDistorted Images to Simulate a Template Of the Registration Target)\nwas the earliest method addressing this issue, by simulating a target DW image without motion\nor distortion from a DTI (b=1000s/mm2) scan of the same subject.\nLater, Andersson and Sotiropoulos [#r2]_ proposed a similar approach (widely available within the\nFSL ``eddy`` tool), by predicting the target DW image to be registered from the remainder of the\ndMRI dataset and modeled with a Gaussian process.\nBesides the need for less data, ``eddy`` has the advantage of implicitly modeling distortions due\nto Eddy currents.\nMore recently, Cieslak et al. [#r3]_ integrated both approaches in *SHORELine*, by\n(i) setting up a leave-one-out prediction framework as in eddy; and\n(ii) replacing eddy’s general-purpose Gaussian process prediction with the SHORE [#r4]_ diffusion model.\n\n*Eddymotion* is an open implementation of eddy-current and head-motion correction that builds upon\nthe work of ``eddy`` and *SHORELine*, while generalizing these methods to multiple acquisition schemes\n(single-shell, multi-shell, and diffusion spectrum imaging) using diffusion models available with DIPY [#r5]_.\n\n.. BEGIN FLOWCHART\n\n.. image:: https://raw.githubusercontent.com/nipreps/eddymotion/507fc9bab86696d5330fd6a86c3870968243aea8/docs/_static/eddymotion-flowchart.svg\n   :alt: The eddymotion flowchart\n\n.. END FLOWCHART\n\n.. [#r1] S. Ben-Amitay et al., Motion correction and registration of high b-value diffusion weighted images, Magnetic\n   Resonance in Medicine 67:1694–1702 (2012)\n.. [#r2] J. L. R. Andersson. et al., An integrated approach to correction for off-resonance effects and subject movement\n   in diffusion MR imaging, NeuroImage 125 (2016) 1063–1078\n.. [#r3] M. Cieslak et al., QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI data.\n   Nature Methods, 18(7), 775–778 (2021)\n.. [#r4] E. Ozarslan et al., Simple Harmonic Oscillator Based Reconstruction and Estimation for Three-Dimensional Q-Space\n   MRI. in Proc. Intl. Soc. Mag. Reson. Med. vol. 17 1396 (2009)\n.. [#r5] E. Garyfallidis et al., Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8, 8\n   (2014)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Feddymotion","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnipreps%2Feddymotion","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Feddymotion/lists"}