Skip to main content
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.

Metadata

Release files for dmriprep 0.5.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dmriprep 0.5.0
File Size Uploaded
dmriprep-0.5.0.tar.gz 74.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for dmriprep 0.5.0
File Interpreter ABI Platform
dmriprep-0.5.0-py3-none-any.whl Python 3 none any Details

Total release size: 151.8 kB

Release files / dmriprep-0.5.0.tar.gz

Download URL dmriprep-0.5.0.tar.gz
Size 74.7 kB
Tags Source
SHA-256 checksum
How to use checksums
b6c7f8bc777d3301319b8c1c4d397e1d3c7e99607b285f03e85e2a4af1fef40f
BLAKE2b-256 checksum
How to use checksums
b5b5b9a17478827f99a49fe0daad605617e8bbc0d588a72174f9d89865cf5ff4
Upload date
Uploaded using Trusted Publishing?
What is 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/4.59.0 CPython/3.7.4

Release files / dmriprep-0.5.0-py3-none-any.whl

Download URL dmriprep-0.5.0-py3-none-any.whl
Size 77.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a390f1cf09789a6c6282dbb3caf1d41a576914d178493d78344014d35ddc0834
BLAKE2b-256 checksum
How to use checksums
4fe892ec9036e14c081c025f42bf5d74dcfe5899c5ee0c2a45059744300f92da
Upload date
Uploaded using Trusted Publishing?
What is 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/4.59.0 CPython/3.7.4

Release history Release notifications | RSS feed

This release

0.5.0 This release

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.2

2 release files

0.2.1

2 release files

0.2.0

1 release file

0.1.1

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page