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 11-15, 2019) is now open for registration:

See some of our Past Announcements

Highlights


DIPY 1.0.0 is now available. New features include:

  • Critical API changes

  • Large refactoring of tracking API.

  • New denoising algorithm: MP-PCA.

  • New Gibbs ringing removal.

  • New interpolation module: dipy.core.interpolation.

  • New reconstruction models: MTMS-CSD, Mean Signal DKI.

  • Increased coordinate systems consistency.

  • New object to manage safely tractography data: StatefulTractogram

  • New command line interface for downloading datasets: FetchFlow

  • Horizon updated, medical visualization interface powered by QuickBundlesX.

  • Removed all deprecated functions and parameters.

  • Removed compatibility with Python 2.7.

  • Updated minimum dependencies version (Numpy, Scipy).

  • All tutorials updated to API changes and 3 new added.

  • Large documentation update.

  • Closed 289 issues and merged 98 pull requests.

See Older Highlights.

Tweets

Wed Oct 23

RT @Ragini__Verma: Recruiting multiple data analysts for my lab @DiCIPHR for processing and analyzing large clinical datasets in tumor, tra…

Tue Oct 22

Check out the tutorials on #DIPYreconstruction: Diffusion Kurtosis Imaging (DKI) and its variations!! Mean Signal a… https://t.co/YDH6WLkJjP

Mon Oct 21

RT @dipymri: Registration for DIPY Workshop 2020 is now open. Visit https://t.co/9gh65ZngaR for information about dates, program and accomm…

Fri Oct 11

RT @soffiafdz: @egarzav @mallarchkrvrty1 @sattertt @AlcauterSarael Shotout to @garyfallidis and @dipymri as well. Amazing talks in general.

Wed Oct 09

RT @garyfallidis: Tomorrow @cimatoficial in Guanajuato, Leon, Mexico! Talking about new exiting brain mapping research and features of DIPY…

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