About¶

The preprocessing of diffusion MRI (dMRI) involves numerous steps to clean and standardizethe 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 inacquisition and processing.dMRIPrep is an analysis-agnostic tool that addresses the challenge of robust andreproducible preprocessing for whole-brain dMRI data.dMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies ofvirtually any dataset, ensuring high-quality preprocessing without manual intervention.dMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessingworkflow, which can help ensure the validity of inference and the interpretabilityof results.

The workflow is based onNipype andencompasses a large set of tools from other neuroimaging packages.This pipeline was designed to provide the best software implementation for each state ofpreprocessing, and will be updated as newer and better neuroimaging softwarebecomes 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.

Getting involved¶

We welcome all contributions!We’d like to ask you to familiarize yourself with ourcontributing guidelines.For ideas for contributing todMRIPrep, please see the current list ofissues.For making your contribution, we use the GitHub flow, which isnicely explained in the chapterContributing to a Project in Pro Gitby Scott Chacon and also in theMaking a change section of our guidelines.If you’re still not sure where to begin, feel free to pop intoMattermost and introduce yourself!Our project maintainers will do their best to answer any question or concerns and will be happy to help you find somewhere to get started.

Want to learn more about our future plans for developingdMRIPrep?Please take a look at ourmilestones board andproject roadmap.

We ask that all contributors todMRIPrep across all project-related spaces (including but not limited to: GitHub, Mattermost, and project emails), adhere to ourcode of conduct.

Contents¶

The overall philosophy of the NiPreps and some examples are explained in this video:

About theNiPreps framework licensing¶

Please checkhttps://www.nipreps.org/community/licensing/ for detailedinformation on the criteria we use to licensefMRIPrep and otherprojects of the framework.

License information¶

Copyright (c) 2021, theNiPreps Developers.

dMRIPrep is licensed under the Apache License, Version 2.0 (the “License”);you may not use this file except in compliance with the License.You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an “AS IS” BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.