Skip to main content

dMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data.

Project description

https://img.shields.io/badge/chat-mattermost-blue https://img.shields.io/badge/docker-nipreps/dmriprep-brightgreen.svg?logo=docker&style=flat https://img.shields.io/pypi/v/dmriprep.svg https://circleci.com/gh/nipreps/dmriprep.svg?style=svg https://github.com/nipreps/dmriprep/workflows/Python%20package/badge.svg https://zenodo.org/badge/DOI/10.5281/zenodo.3392201.svg

[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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dmriprep-0.5.0rc0.tar.gz (74.0 kB view details)

Uploaded Source

Built Distribution

dmriprep-0.5.0rc0-py3-none-any.whl (76.3 kB view details)

Uploaded Python 3

File details

Details for the file dmriprep-0.5.0rc0.tar.gz.

File metadata

  • Download URL: dmriprep-0.5.0rc0.tar.gz
  • Upload date:
  • Size: 74.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.7.4

File hashes

Hashes for dmriprep-0.5.0rc0.tar.gz
Algorithm Hash digest
SHA256 c5906d17c286dc23ba018d7a3ae978307956f67950de9dd91a3a0c28d5f23a0f
MD5 9d1e8a7451c57ad305cc975813d3f345
BLAKE2b-256 737ccfcda85338bf460092f7659ddb1ec351e6c627d199417449af9054e63d2a

See more details on using hashes here.

File details

Details for the file dmriprep-0.5.0rc0-py3-none-any.whl.

File metadata

  • Download URL: dmriprep-0.5.0rc0-py3-none-any.whl
  • Upload date:
  • Size: 76.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.7.4

File hashes

Hashes for dmriprep-0.5.0rc0-py3-none-any.whl
Algorithm Hash digest
SHA256 f253a1d7245bca8fa9b53c62c6595e120879f4e9b131dee592dc93222bbd6393
MD5 429bdd91c864be990a5bd9f778c8a9bf
BLAKE2b-256 26b659ad202cd400638eac196ec7b14697b3fa43270f6736825c46d45578b545

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page