Metadata-Version: 2.1
Name: smriprep
Version: 0.7.2
Summary: sMRIPrep (Structural MRI PREProcessing) pipeline
Home-page: https://github.com/nipreps/smriprep
Maintainer: The NiPreps developers
Maintainer-email: nipreps@gmail.com
License: 3-clause BSD
Description: sMRIPrep: Structural MRI PREProcessing pipeline
        ===============================================
        
        .. image:: https://img.shields.io/badge/docker-nipreps/smriprep-brightgreen.svg?logo=docker&style=flat
          :target: https://hub.docker.com/r/nipreps/smriprep/tags/
          :alt: Docker image available!
        
        .. image:: https://circleci.com/gh/nipreps/smriprep/tree/master.svg?style=shield
          :target: https://circleci.com/gh/nipreps/smriprep/tree/master
          
        .. image:: https://codecov.io/gh/nipreps/smriprep/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/nipreps/smriprep
          :alt: Coverage report
        
        .. image:: https://img.shields.io/pypi/v/smriprep.svg
          :target: https://pypi.python.org/pypi/smriprep/
          :alt: Latest Version
          
        .. image:: https://img.shields.io/badge/doi-10.1038%2Fs41592--018--0235--4-blue.svg
          :target: https://doi.org/10.1038/s41592-018-0235-4
          :alt: Published in Nature Methods
        
        
        *sMRIPrep* is a structural magnetic resonance imaging (sMRI) data
        preprocessing pipeline that is designed to provide an easily accessible,
        state-of-the-art interface that is robust to variations in scan acquisition
        protocols and that requires minimal user input, while providing easily
        interpretable and comprehensive error and output reporting.
        It performs basic processing steps (subject-wise averaging, B1 field correction,
        spatial normalization, segmentation, skullstripping etc.) providing
        outputs that can be easily connected to subsequent tools such as
        `fMRIPrep <https://github.com/nipreps/fmriprep>`__ or
        `dMRIPrep <https://github.com/nipreps/dmriprep>`__.
        
        .. image:: https://github.com/oesteban/smriprep/raw/033a6b4a54ecbd9051c45df979619cda69847cd1/docs/_resources/workflow.png
        
        The workflow is based on `Nipype <https://nipype.readthedocs.io>`__ and encompases
        a combination of tools from well-known software packages, including
        `FSL <https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>`__,
        `ANTs <https://stnava.github.io/ANTs/>`__,
        `FreeSurfer <https://surfer.nmr.mgh.harvard.edu/>`__,
        and `AFNI <https://afni.nimh.nih.gov/>`__.
        
        More information and documentation can be found at
        https://www.nipreps.org/smriprep/.
        Support is provided on `neurostars.org <https://neurostars.org/tags/smriprep>`_.
        
        Principles
        ----------
        
        *sMRIPrep* is built around three principles:
        
        1. **Robustness** - The pipeline adapts the preprocessing steps depending on
           the input dataset and should provide results as good as possible
           independently of scanner make, scanning parameters or presence of additional
           correction scans (such as fieldmaps).
        2. **Ease of use** - Thanks to dependence on the BIDS standard, manual
           parameter input is reduced to a minimum, allowing the pipeline to run in an
           automatic fashion.
        3. **"Glass box"** philosophy - Automation should not mean that one should not
           visually inspect the results or understand the methods.
           Thus, *sMRIPrep* provides visual reports for each subject, detailing the
           accuracy of the most important processing steps.
           This, combined with the documentation, can help researchers to understand
           the process and decide which subjects should be kept for the group level
           analysis.
        
        
        Acknowledgements
        ----------------
        
        Please acknowledge this work by mentioning explicitly the name of this software
        (sMRIPrep) and the version, along with a link to the `GitHub repository
        <https://github.com/nipreps/smriprep>`__ or the Zenodo reference
        (doi:`10.5281/zenodo.2650521 <https://doi.org/10.5281/zenodo.2650521>`__).
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: datalad
Provides-Extra: doc
Provides-Extra: docs
Provides-Extra: duecredit
Provides-Extra: resmon
Provides-Extra: style
Provides-Extra: test
Provides-Extra: tests
Provides-Extra: all
