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

Pre-Processing

Denoising : NLmeans, Local PCA
Brain extraction

SNR estimation / Reslice Datasets

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Registration

Streamlines based Registration

Affine Registration

Diffeomorphic 2D/3D Registration

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Reconstruction

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

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

More than 20 Models...

Tractography

Tissue Classification

Many tracking algorithms

Apply different operations on streamlines

Simplify large datasets via streamlines clustering.

Calculate distances/correspondences between streamlines.

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Visualization

Simple interactive visualization of ODFs

Streamlines interactive visualization

Advanced widget

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Command Line Interfaces

Many algorithms available via command line

Create your owns command line!

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Announcements


  • 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

Highlights


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.

Tweets

Mon May 25

RT @arokem: @Tomdonoghue There's a list of these in the @dipymri docs: https://t.co/4TcVKF8z0E, originally formulated by Matthew Brett and…

Fri May 22

RT @g_kiar: Happy to announce that my recent paper exploring the stability of pipelines has been officially published by the International…

Fri May 22

RT @g_kiar: Pipeline of choice: @dipymri deterministic tractography + connectome estimation Perturbation methods: Operating system, 1-voxe…

Thu May 07

RT @arokem: Slides for my talk @UWPsychology this afternoon are at: https://t.co/r9zBpvXFWw

Fri Mar 20

RT @keshjordan: I have been so lucky in the python open source communities I have had the privilege of being a part of. Here are my two cen…

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.

Sponsors


DIPY - NIH / NIBIB
DIPY - Indiana university
DIPY - GSOC
IU
DIPY - EWU