What is DIPY?

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

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


See some of our Past Announcements

Highlights


DIPY 1.2.0 is now available. New features include:

  • New command line interfaces for group analysis: BUAN.

  • Added b-tensor encoding for gradient table.

  • Better support for single shell or multi-shell response functions.

  • Stats module refactored.

  • Numpy minimum version is 1.2.0.

  • Fixed compatibilities with FURY 0.6+, VTK9+, CVXPY 1.1+.

  • Added multiple tutorials for DIPY command line interfaces.

  • Updated SH basis convention.

  • Improved performance of tissue classification.

  • Fixed a memory overlap bug (multi_median).

  • Large documentation update (typography / references).

  • Closed 256 issues and merged 94 pull requests.

See Older Highlights.

Tweets

Thu Sep 10

RT @arokem: If you use DIPY in your research or learning, we would REALLY appreciate your input on this survey. It will help us direct our…

Thu Sep 10

RT @dipymri: Hello @dipymri community! We are looking to get feedback for future directions and would love your opinions/ suggestions as th…

Thu Sep 10

Hello @dipymri community! We are looking to get feedback for future directions and would love your opinions/ sugges… https://t.co/DkWKPdSBUh

Sat Jun 06

RT @arokem: @patrickmineault @figshare works well for this, giving you a permanent URL you can wget from. We use that all the time in @dipy…

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…

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