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Apps","Building"],"sub_categories":["others","Workflows"],"readme":"sMRIPrep: Structural MRI PREProcessing pipeline\n===============================================\n\n.. image:: https://img.shields.io/badge/docker-nipreps/smriprep-brightgreen.svg?logo=docker\u0026style=flat\n  :target: https://hub.docker.com/r/nipreps/smriprep/tags/\n  :alt: Docker image available!\n\n.. image:: https://circleci.com/gh/nipreps/smriprep/tree/master.svg?style=shield\n  :target: https://circleci.com/gh/nipreps/smriprep/tree/master\n\n.. image:: https://codecov.io/gh/nipreps/smriprep/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/nipreps/smriprep\n  :alt: Coverage report\n\n.. image:: https://img.shields.io/pypi/v/smriprep.svg\n  :target: https://pypi.python.org/pypi/smriprep/\n  :alt: Latest Version\n\n.. image:: https://img.shields.io/badge/doi-10.1038%2Fs41592--018--0235--4-blue.svg\n  :target: https://doi.org/10.1038/s41592-018-0235-4\n  :alt: Published in Nature Methods\n\n\n*sMRIPrep* is a structural magnetic resonance imaging (sMRI) data\npreprocessing pipeline that is designed to provide an easily accessible,\nstate-of-the-art interface that is robust to variations in scan acquisition\nprotocols and that requires minimal user input, while providing easily\ninterpretable and comprehensive error and output reporting.\nIt performs basic processing steps (subject-wise averaging, B1 field correction,\nspatial normalization, segmentation, skullstripping etc.) providing\noutputs that can be easily connected to subsequent tools such as\n`fMRIPrep \u003chttps://github.com/nipreps/fmriprep\u003e`__ or\n`dMRIPrep \u003chttps://github.com/nipreps/dmriprep\u003e`__.\n\n.. image:: https://github.com/oesteban/smriprep/raw/033a6b4a54ecbd9051c45df979619cda69847cd1/docs/_resources/workflow.png\n\nThe workflow is based on `Nipype \u003chttps://nipype.readthedocs.io\u003e`__ and encompasses\na combination of tools from well-known software packages, including\n`FSL \u003chttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\u003e`__,\n`ANTs \u003chttps://stnava.github.io/ANTs/\u003e`__,\n`FreeSurfer \u003chttps://surfer.nmr.mgh.harvard.edu/\u003e`__,\nand `Connectome Workbench \u003chttps://humanconnectome.org/software/connectome-workbench\u003e`__.\n\nMore information and documentation can be found at\nhttps://www.nipreps.org/smriprep/.\nSupport is provided on `neurostars.org \u003chttps://neurostars.org/tags/smriprep\u003e`_.\n\nPrinciples\n----------\n\n*sMRIPrep* is built around three principles:\n\n1. **Robustness** - The pipeline adapts the preprocessing steps depending on\n   the input dataset and should provide results as good as possible\n   independently of scanner make, scanning parameters or presence of additional\n   correction scans (such as fieldmaps).\n2. **Ease of use** - Thanks to dependence on the BIDS standard, manual\n   parameter input is reduced to a minimum, allowing the pipeline to run in an\n   automatic fashion.\n3. **\"Glass box\"** philosophy - Automation should not mean that one should not\n   visually inspect the results or understand the methods.\n   Thus, *sMRIPrep* provides visual reports for each subject, detailing the\n   accuracy of the most important processing steps.\n   This, combined with the documentation, can help researchers to understand\n   the process and decide which subjects should be kept for the group level\n   analysis.\n\n\nAcknowledgements\n----------------\n\nPlease acknowledge this work by mentioning explicitly the name of this software\n(sMRIPrep) and the version, along with a link to the `GitHub repository\n\u003chttps://github.com/nipreps/smriprep\u003e`__ or the Zenodo reference\n(doi:`10.5281/zenodo.2650521 \u003chttps://doi.org/10.5281/zenodo.2650521\u003e`__).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Fsmriprep","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnipreps%2Fsmriprep","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Fsmriprep/lists"}