Warning: This document is for an old version of dmriprep. The main version is master.

dMRIPrep

[Documentation] [Support at neurostars.org]

About

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 and encompasses a large set of tools from other neuroimaging packages. 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.

Getting involved

We welcome all contributions! We’d like to ask you to familiarize yourself with our contributing guidelines. For ideas for contributing to dMRIPrep, please see the current list of issues. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon and also in the Making a change section of our guidelines. If you’re still not sure where to begin, feel free to pop into Mattermost 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 developing dMRIPrep? Please take a look at our milestones board and project roadmap.

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

Contents

• Installation
  • Container technologies: Docker and Singularity
    • Docker (recommended for PC/laptop and commercial Cloud)
    • Singularity (recommended for HPC)
  • Manually Prepared Environment (Python 3.5+)
    • External Dependencies
• Usage
  • Execution and the BIDS format
  • Command-Line Arguments
    • Positional Arguments
    • Named Arguments
    • Options for filtering BIDS queries
    • Options to handle performance
    • Workflow configuration
    • Specific options for ANTs registrations
    • Specific options for SyN distortion correction
    • Specific options for FreeSurfer preprocessing
    • Surface preprocessing options
    • Other options
  • The FreeSurfer license
  • Usage tracking with Google Analytics
• Library API (application program interface)
  • dmriprep.config package
    • Configuration sections
    • Usage
    • Logging
    • Other responsibilities
  • dmriprep.interfaces package
    • Submodules
  • dmriprep.utils package
    • Submodules
  • dmriprep.workflows package
    • Subpackages
    • Submodules
• What’s new?
  • 0.5.0 (TBD)
  • 0.4.0 (December 10, 2020)
  • 0.3.0 (October 13, 2020)
  • 0.2.2 (January 15, 2020)
  • 0.2.1 (December 12, 2019)
  • 0.2.0 (September 06, 2019)
  • 0.1.1a0 (September 05, 2019)
  • 0.1.1 (September 05, 2019)
  • 0.1.0 (November 21, 2018)
• Development road-map
  • Version 0.4 (Before end of 2020)
  • Version 0.5 (January, 2021)
  • Version 0.6 (February, 2021)
  • Version 0.7 (March, 2021)
  • Version 0.8 (April, 2021)
  • Version 0.9 (May, 2021)
  • Version 1.0 (Targetted for September 2021)
  • Long-term plans

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