What is DIPY?

DIPY is a free and open source software project for computational neuroanatomy, focusing mainly on diffusion magnetic resonance imaging (dMRI) analysis. It implements a broad range of algorithms for denoising, registration, reconstruction, tracking, clustering, visualization, and statistical analysis of MRI data.

DIPY is easy to install:

Available on many operating systems


Denoising : NLmeans, Local PCA
Brain extraction

SNR estimation / Reslice Datasets



Streamlines based Registration

Affine Registration

Diffeomorphic 2D/3D Registration



Single Shell: DTI, CSD, SDT, SFM, Q-Ball CSA...

Multi Shell: GQI, DTI, DKI, SHORE and MAPMRI...

More than 20 Models...


Tissue Classification

Many tracking algorithms

Apply different operations on streamlines

Simplify large datasets via streamlines clustering.

Calculate distances/correspondences between streamlines.



Simple interactive visualization of ODFs

Streamlines interactive visualization

Advanced widget


Command Line Interfaces

Many algorithms available via command line

Create your owns command line!



  • DIPY Workshop - Titanium Edition (March 16-20, 2020) is now open for registration:

  • DIPY 1.1.1 released January 10, 2020.

  • DIPY 1.0 released August 5, 2019.

  • DIPY 0.16 released March 10, 2019.

See some of our Past Announcements


DIPY 1.1.1 is now available. New features include:

  • New module for deep learning DIPY.NN (uses TensorFlow 2.0).

  • Improved DKI performance and increased utilities.

  • Non-linear and RESTORE fits from DTI compatible now with DKI.

  • Numerical solutions for estimating axial, radial and mean kurtosis.

  • Added Kurtosis Fractional Anisotropy by Glenn et al. 2015.

  • Added Mean Kurtosis Tensor by Hansen et al. 2013.

  • Nibabel minimum version is 3.0.0.

  • Azure CI added and Appveyor CI removed.

  • New command line interfaces for LPCA, MPPCA and Gibbs Unringing.

  • New MTMS CSD tutorial added.

  • Horizon refactored and updated to support StatefulTractograms.

  • Speeded up all cython modules by using a smarter configuration setting.

  • All tutorials updated to API changes and 2 new tutorials added.

  • Large documentation update.

  • Closed 126 issues and merged 50 pull requests.

See Older Highlights.


Tue Jan 21

Did you notice DIPY Release 1.1.1😁? Here some Highlights: New module for deep learning DIPY.NN, Added Mean Kurtosis… https://t.co/rUOWr5CzUi

Fri Jan 17

Given the large growth of DIPY and the large need for sub-projects, DIPY moved to its own organization in Github. L… https://t.co/4qoYRNTQve

Thu Jan 09

RT @arokem: The tentative schedule for the @dipymri workshop @IULuddy in March is up: https://t.co/CRMh7crxbe and it is super! Looking forw…

Thu Jan 02

RT @arokem: New software for the new year! The first release of pyAFQ: https://t.co/KPLwbNnF4h. This is a Python reimplementation of the tr…

Thu Dec 19

RT @reiinakano: At #NeurIPS2019, talking to a guy from DeepMind about how I can't reproduce their results because it's so resource intensiv…

Cite Us !

Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I and Dipy Contributors (2014). DIPY, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, vol.8, no.8.


DIPY - Indiana university