{"id":13676518,"url":"https://github.com/nipreps/mriqc","last_synced_at":"2025-05-14T22:09:45.142Z","repository":{"id":40544065,"uuid":"49015296","full_name":"nipreps/mriqc","owner":"nipreps","description":"Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain","archived":false,"fork":false,"pushed_at":"2025-05-09T13:20:36.000Z","size":277539,"stargazers_count":320,"open_issues_count":72,"forks_count":134,"subscribers_count":15,"default_branch":"master","last_synced_at":"2025-05-09T14:27:06.193Z","etag":null,"topics":["machine-learning","mri","neuroimaging","quality-control","quality-reporter"],"latest_commit_sha":null,"homepage":"http://mriqc.readthedocs.io","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":null,"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,"zenodo":".zenodo.json"}},"created_at":"2016-01-04T18:31:38.000Z","updated_at":"2025-05-08T21:21:37.000Z","dependencies_parsed_at":"2023-10-14T16:33:23.183Z","dependency_job_id":"4b125a5e-4ca7-42e0-9478-448fd1dbdfe0","html_url":"https://github.com/nipreps/mriqc","commit_stats":{"total_commits":2580,"total_committers":43,"mean_commits":60.0,"dds":0.2135658914728682,"last_synced_commit":"93f7670b42a98ba024020734587770e22a9b6c30"},"previous_names":[],"tags_count":112,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Fmriqc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Fmriqc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Fmriqc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nipreps%2Fmriqc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nipreps","download_url":"https://codeload.github.com/nipreps/mriqc/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254235701,"owners_count":22036964,"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":["machine-learning","mri","neuroimaging","quality-control","quality-reporter"],"created_at":"2024-08-02T13:00:28.846Z","updated_at":"2025-05-14T22:09:40.129Z","avatar_url":"https://github.com/nipreps.png","language":"Python","funding_links":[],"categories":["BIDS Apps","Python"],"sub_categories":["others"],"readme":"mriqc: image quality metrics for quality assessment of MRI\n==========================================================\n\n|DOI| |Zenodo| |Package| |Pythons| |DevStatus| |License| |Documentation| |CircleCI| |EOSS|\n\nMRIQC extracts no-reference IQMs (image quality metrics) from\nstructural (T1w and T2w), functional and diffusion MRI (magnetic resonance imaging)\ndata.\n\nMRIQC is an open-source project, developed under the following\nsoftware engineering principles:\n\n#. **Modularity and integrability**: MRIQC implements a\n   `nipype \u003chttps://nipype.readthedocs.io\u003e`_ workflow to integrate modular\n   sub-workflows that rely upon third party software toolboxes such as\n   ANTs and AFNI.\n\n#. **Minimal preprocessing**: the MRIQC workflows should be as minimal\n   as possible to estimate the IQMs on the original data or their minimally\n   processed derivatives.\n\n#. **Interoperability and standards**: MRIQC follows the the `brain imaging data structure\n   (BIDS) \u003chttps://bids.neuroimaging.io\u003e`_, and it adopts the `BIDS-App\n   \u003chttps://bids-apps.neuroimaging.io\u003e`_ standard.\n\n#. **Reliability and robustness**: the software undergoes frequent vetting sprints\n   by testing its robustness against data variability (acquisition parameters,\n   physiological differences, etc.) using images from `OpenfMRI \u003chttps://openfmri.org\u003e`_.\n   Its reliability is permanently checked and maintained with\n   `CircleCI \u003chttps://circleci.com/gh/nipreps/mriqc\u003e`_.\n\nCitation\n--------\n.. topic:: **When using MRIQC, please include the following citation:**\n\n    Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ;\n    *MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites*;\n    PLOS ONE 12(9):e0184661; doi:`10.1371/journal.pone.0184661 \u003chttps://doi.org/10.1371/journal.pone.0184661\u003e`_.\n\nSupport and communication\n-------------------------\nThe documentation of this project is found here: https://mriqc.readthedocs.io/.\n\nUsers can get help using the `mriqc-users google group \u003chttps://groups.google.com/forum/#!forum/mriqc-users\u003e`_.\n\nAll bugs, concerns and enhancement requests for this software can be submitted here:\nhttps://github.com/nipreps/mriqc/issues.\n\nDevelopment\n-----------\nA local development build based on the latest docker build of MRIQC can be built with this command run from \nthe root of this repository::\n\n    docker build -f Dockerfile_devel -t mriqc_devel .\n\nTo test changes the local source code will need to be mounted into the development container::\n\n    docker run --rm -v .:/src/mriqc mriqc_devel\n\nNew Python dependencies can be added in ``pyproject.toml`` under ``dependencies``.\nAny time a dependency is changed or added there the docker image will need to be rebuilt\nusing the above ``docker build`` command.\n\nLicense information\n-------------------\n*MRIQC* adheres to the\n`general licensing guidelines \u003chttps://www.nipreps.org/community/licensing/\u003e`__\nof the *NiPreps framework*.\n\n*MRIQC* originally derives from, and hence is heavily influenced by, the\n`PCP Quality Assessment Protocol\n\u003chttp://preprocessed-connectomes-project.org/quality-assessment-protocol/\u003e`__.\nPlease check the ``NOTICE`` file for further information.\n\nLicense\n~~~~~~~\nCopyright (c) 2021, the *NiPreps* Developers.\n\nAs of the 21.0.x pre-release and release series, *MRIQC* is\nlicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n`http://www.apache.org/licenses/LICENSE-2.0\n\u003chttp://www.apache.org/licenses/LICENSE-2.0\u003e`__.\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nAcknowledgements\n----------------\nThis work is steered and maintained by the `NiPreps Community \u003chttps://www.nipreps.org\u003e`__.\nThe development of this resource was supported by\nthe Laura and John Arnold Foundation (RAP and KJG),\nthe NIBIB (R01EB020740, SSG; 1P41EB019936-01A1SSG, YOH),\nthe NIMH (RF1MH121867, RAP, OE; R24MH114705 and R24MH117179, RAP; 1RF1MH121885 SSG),\nNINDS (U01NS103780, RAP), and NSF (CRCNS 1912266, YOH).\nOE acknowledges financial support from the SNSF Ambizione project\n“*Uncovering the interplay of structure, function, and dynamics of\nbrain connectivity using MRI*” (grant number\n`PZ00P2_185872 \u003chttp://p3.snf.ch/Project-185872\u003e`__).\n\n.. topic:: **Thanks**\n\n    * The QAP developers (C. Craddock, S. Giavasis, D. Clark, Z. Shezhad, and J.\n      Pellman) for the initial base of code which MRIQC was forked from.\n    * W Triplett and CA Moodie for their initial contributions with bugfixes and documentation, and\n    * J Varada for his contributions on the source code.\n\n\n.. |DOI| image:: https://img.shields.io/badge/doi-10.1371%2Fjournal.pone.0184661-blue.svg\n   :target: https://doi.org/10.1371/journal.pone.0184661\n.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2630889.svg\n   :target: https://doi.org/10.5281/zenodo.2630889\n.. |Package| image:: https://img.shields.io/pypi/v/mriqc.svg\n   :target: https://pypi.python.org/pypi/mriqc/\n.. |Pythons| image:: https://img.shields.io/pypi/pyversions/mriqc.svg\n   :target: https://pypi.python.org/pypi/mriqc/\n.. |DevStatus| image:: https://img.shields.io/pypi/status/mriqc.svg\n   :target: https://pypi.python.org/pypi/mriqc/\n.. |License| image:: https://img.shields.io/pypi/l/mriqc.svg\n   :target: https://pypi.python.org/pypi/mriqc/\n.. |Documentation| image:: https://readthedocs.org/projects/mriqc/badge/?version=latest\n   :target: http://mriqc.readthedocs.io/en/latest/?badge=latest\n.. |CircleCI| image:: https://circleci.com/gh/nipreps/mriqc/tree/master.svg?style=shield\n   :target: https://circleci.com/gh/nipreps/mriqc/tree/master\n.. |EOSS| image:: https://chanzuckerberg.github.io/open-science/badges/CZI-EOSS.svg\n  :target: https://czi.co/EOSS\n  :alt: CZI's Essential Open Source Software for Science\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Fmriqc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnipreps%2Fmriqc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Fmriqc/lists"}