Metadata-Version: 2.1
Name: dmriprep
Version: 0.3.0
Summary: dMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data.
Home-page: UNKNOWN
Author: The NiPreps developers
Author-email: nipreps@gmail.com
License: Apache License, Version 2.0
Project-URL: Home, https://github.com/nipreps/dmriprep
Project-URL: Documentation, https://www.nipreps.org/dmriprep
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.6
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========
dMRIPrep
========

.. image:: https://badgen.net/badge/chat/on%20mattermost/blue
    :target: https://mattermost.brainhack.org/brainhack/channels/dmriprep

.. image:: https://img.shields.io/pypi/v/dmriprep.svg
    :target: https://pypi.python.org/pypi/dmriprep

.. image:: https://circleci.com/gh/nipreps/dmriprep.svg?style=svg
    :target: https://circleci.com/gh/nipreps/dmriprep

.. image:: https://travis-ci.org/nipreps/dmriprep.svg?branch=master
    :target: https://travis-ci.org/nipreps/dmriprep

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3392201.svg
    :target: https://doi.org/10.5281/zenodo.3392201

[`Documentation <https://www.nipreps.org/dmriprep/>`__]
[`Support at neurostars.org <https://neurostars.org/tags/dmriprep>`__]

The preprocessing of diffusion MRI (dMRI) involves numerous steps to clean and standardize
the data before fitting a particular model.
Generally, researchers create ad-hoc preprocessing workflows for each dataset,
building upon a large inventory of available tools.
The complexity of these workflows has snowballed with rapid advances in
acquisition and processing.
dMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and
reproducible preprocessing for whole-brain dMRI data.
dMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of
virtually any dataset, ensuring high-quality preprocessing without manual intervention.
dMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing
workflow, which can help ensure the validity of inference and the interpretability
of results.

The workflow is based on `Nipype <https://nipype.readthedocs.io>`__ and
encompases a large set of tools from other neuroimaging package. This pipeline
was designed to provide the best software implementation for each state of
preprocessing, and will be updated as newer and better neuroimaging software
becomes available.

dMRIPrep performs basic preprocessing steps such as head-motion correction,
susceptibility-derived distortion correction, eddy current correction, etc.
providing outputs that can be easily submitted to a variety of diffusion models.


