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
Registration
Reconstruction
Single Shell: DTI, CSD, SDT, SFM, Q-Ball CSA...
Multi Shell: GQI, DTI, DKI, SHORE and MAPMRI...
Tractography
Tissue Classification
Many tracking algorithms
Apply different operations on streamlines
Simplify large datasets via streamlines clustering.
Calculate distances/correspondences between streamlines.
Visualization
Simple interactive visualization of ODFs
Streamlines interactive visualization
Advanced widget
Command Line Interfaces
Announcements
DIPY Workshop - Titanium Edition (March 16-20, 2020) is now open for registration:
DIPY 1.1.1 released January 10, 2020.
DIPY 1.0 released August 5, 2019.
DIPY 0.16 released March 10, 2019.
See some of our Past Announcements
Highlights
DIPY 1.1.1 is now available. New features include:
New module for deep learning
DIPY.NN(uses TensorFlow 2.0).Improved DKI performance and increased utilities.
Non-linear and RESTORE fits from DTI compatible now with DKI.
Numerical solutions for estimating axial, radial and mean kurtosis.
Added Kurtosis Fractional Anisotropy by Glenn et al. 2015.
Added Mean Kurtosis Tensor by Hansen et al. 2013.
Nibabel minimum version is 3.0.0.
Azure CI added and Appveyor CI removed.
New command line interfaces for LPCA, MPPCA and Gibbs Unringing.
New MTMS CSD tutorial added.
Horizon refactored and updated to support StatefulTractograms.
Speeded up all cython modules by using a smarter configuration setting.
All tutorials updated to API changes and 2 new tutorials added.
Large documentation update.
Closed 126 issues and merged 50 pull requests.
See Older Highlights.
Tweets
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… |
Fri May 22 |
RT @g_kiar: Happy to announce that my recent paper exploring the stability of pipelines has been officially published by the International… |
Fri May 22 |
RT @g_kiar: Pipeline of choice: @dipymri deterministic tractography + connectome estimation Perturbation methods: Operating system, 1-voxe… |
Thu May 07 |
RT @arokem: Slides for my talk @UWPsychology this afternoon are at: https://t.co/r9zBpvXFWw |
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
