Diffusion Imaging in Python

An open-source, user-friendly and growing imaging library for 3D/4D+ imaging.

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Command-Line Interfaces

Command-Line Interfaces

All the algorithms are available using CLI. You can also create your own algorithms.

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Statistical Analysis

Statistical Analysis

DIPY allows you to use different methods like BUAN, AFQ, K-Fold cross validation.

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Reconstruction

Reconstruction

Single shell: DTI, CSA, SFM, SDT, Q-Ball, CSD, and many more.
Multi shell: GQI, DTI, DKI, SHORE, MAPMRI, MSMT-CSD, and many more.

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Registration

Registration

Affine Registration, Diffeomorphic 2D/3D Registration, Streamlines based Registration, and much more.

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Tractography

Tractography

Probabilistic Tracking, Deterministic Tracking, PFT Tracking, and much more.

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Denoising

Denoising

Patch2Self, Non Local Means, Gibbs Unringing, MP-PCA, L-PCA and much more.

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Visualization

Visualization

ODFs visualization, interactive tractogram visualization, Advanced UI and Shaders, and much more.

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Pre-Processing

Pre-Processing

Brain extraction, SNR estimation, Reslice Datasets, and much more.

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Sponsors

NIH NIBIB Luddy IU Google Summer of Code Indiana University UW eScience Institute Chan Zuckerberg Initiative

Cite Us!

DIPY, a library for the analysis of diffusion MRI data.

Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I and Dipy Contributors (2014).

Frontiers in Neuroinformatics, vol.8, no.8.