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.
Available on many operating systems
Denoising : NLmeans, Local PCA
SNR estimation / Reslice Datasets
Streamlines based Registration
Diffeomorphic 2D/3D Registration
Single Shell: DTI, CSD, SDT, SFM, Q-Ball CSA...
Multi Shell: GQI, DTI, DKI, SHORE and MAPMRI...
Many tracking algorithms
Apply different operations on streamlines
Simplify large datasets via streamlines clustering.
Calculate distances/correspondences between streamlines.
Simple interactive visualization of ODFs
Streamlines interactive visualization
Many algorithms available via command line
Create your owns command line!
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.
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
Fri Mar 20
RT @keshjordan: I have been so lucky in the python open source communities I have had the privilege of being a part of. Here are my two cen…
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.