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

Tue Aug 02

RT @BramshQ: First in-person conference after God knows how long! #AAIC2022. Come by my poster (P3-220) if you are at #AAIC22 to chat about…

Mon Aug 01

RT @krsitek: Loved flipping the page on my BRAIN Initiative calendar and seeing @BramshQ’s beautiful #sciart! Makes it a little less painfu…

Fri Jul 15

@njahanshad Thank you for taking interest in the challenge! We'll soon send you an email about the details.

Fri Jul 15

Great work 🤩 FiberNeat: Unsupervised White Matter Tract Filtering. No training or atlas required!! 🧠 https://t.co/xWsiooG0wh

Fri Jul 15

RT @PTenigma: If you're at #EMBC22 come see us @ 10:45am in the Forth Room (SEC Armadillo) to talk about manifold embedding (t-SNE, UMAP) t…

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