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https://github.com/nipreps/smriprep

Structural MRI PREProcessing (sMRIPrep) workflows for NIPreps (NeuroImaging PREProcessing tools)
https://github.com/nipreps/smriprep

image-processing image-registration image-segmentation mri structural-mri surface-reconstruction

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Structural MRI PREProcessing (sMRIPrep) workflows for NIPreps (NeuroImaging PREProcessing tools)

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README 

        
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 `__ or

`dMRIPrep `__.



.. image:: https://github.com/oesteban/smriprep/raw/033a6b4a54ecbd9051c45df979619cda69847cd1/docs/_resources/workflow.png



The workflow is based on `Nipype `__ and encompasses

a combination of tools from well-known software packages, including

`FSL `__,

`ANTs `__,

`FreeSurfer `__,

and `Connectome Workbench `__.



More information and documentation can be found at

https://www.nipreps.org/smriprep/.

Support is provided on `neurostars.org `_.



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

`__ or the Zenodo reference

(doi:`10.5281/zenodo.2650521 `__).