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

Command-Line Interfaces

All Algorithms available via CLI

Create your Own

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Statistical Analysis

BUAN

AFQ

K-Fold cross validation

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Reconstruction

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

Multi-Shell: GQI, DTI, DKI, SHORE, MAPMRI, MSMT-CSD, ...

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Registration

Affine Registration

Diffeomorphic 2D/3D Registration

Streamlines based Registration

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Tractography

Probabilistic Tracking

Deterministic Tracking

PFT Tracking

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Datasets

HCP Tractogram

CFIN Datasets

High resolution DWI

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Denoising

Patch2self

Gibbs Unringing

LPCA - MPPCA

Non Local Means

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Visualization

ODFs visualization

interactive tractogram visualization

Advanced UI and Shaders

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Pre-Processing

Brain extraction

SNR estimation

Reslice Datasets

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Announcements


See some of our Past Announcements

Highlights


DIPY 1.5.0 is now available. New features include:

  • New reconstruction model added: Q-space Trajectory Imaging (QTI).

  • New reconstruction model added: Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD).

  • New reconstruction model added: Residual block Deep Neural Network (ResDNN).

  • Masking management in Affine Registration added.

  • Multiple Workflows updated (DTIFlow, DKIFlow, ImageRegistrationFlow) and added (MotionCorrectionFlow).

  • Compatibility with Python 3.10 added.

  • Migrations from Azure Pipeline to Github Actions.

  • Large codebase cleaning.

  • New parallelisation module added.

  • dipy.io.bvectxt module deprecated.

  • New DIPY Horizon features (ROI Visualizer, random colors flag).

  • Large documentation update.

  • Closed 129 issues and merged 72 pull requests.

See Older Highlights.

Tweets

Fri Jun 24

RT @furranko: take a look at @FrancoisRheault 's work. We are design a standard file type for #tractography. We would like your engagement!…

Wed Apr 20

📢Dr. Rachel Barrett (@RachelLCBarrett) from @KingsCollegeLon will be presenting on the topic of 🚨Diffusion MRI vs P… https://t.co/xEDJpFgupa

Wed Apr 13

RT @JuanHig27819468: @pythoneuro @dipymri @garyfallidis I love Dipy 🥰

Wed Apr 13

RT @pythoneuro: DIPY @dipymri is a python library for the analysis of MR diffusion imaging. @garyfallidis #Python #neuroscience #OpenScienc…

Fri Mar 25

RT @CieslakMatt: Anyone interested in hearing about QSIPrep, there will be a talk today and a Q&A/demo this afternoon! Stop by with questio…

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