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.
Available on many operating systems
All Agorithms available via CLI
Create your Own
K-Fold cross validation
Single Shell: DTI, CSA, SFM, SDT, Q-Ball, CSD, ...
Multi-Shell: GQI, DTI, DKI, SHORE, MAPMRI, MSMT-CSD, ...
Diffeomorphic 2D/3D Registration
Streamlines based Registration
High resolution DWI
LPCA - MPPCA
Non Local Means
interactive tractogram visualization
Advanced UI and Shaders
DIPY 1.3.0 is now available. New features include:
Gibbs Ringing correction 10X faster.
Spherical harmonics basis definitions updated.
Added SMT2 metrics from mean signal diffusion kurtosis.
New interface functions added to the registration module.
New linear transform added to the registration module.
New tutorials for DIPY command line interfaces.
Fixed compatibility issues with different dependencies.
Tqdm (multiplatform progress bar for data downloading) dependency added.
Large documentation update.
Bundle section highlight from BUAN added in Horizon.
Closed 134 issues and merged 49 pull requests.
See Older Highlights.
Mon Jan 18
RT @msandstr: I just added another 10 projects to the @INCForg #GSoC Ideas list. Excited to have @dipymri, @hebbianloop, @R3RT0, @katjaQhe…
Mon Jan 04
RT @arokem: A milestone for @dipymri, as the paper describing the software reached 500 citations, according to Google Scholar https://t.co/…
Fri Dec 18
RT @arokem: New @dipymri with heavy lifting by our collaborators @nvidia: GPU-accelerated brain tractography, https://t.co/CqKJLgRpba. Trac…
Wed Dec 09
RT @dipymri: 🚨Discussion: Patch2Self for #DiffusionMRI data, being presented @NeurIPS_Conf #NeurIPS2020 as a Spotlight Presentation (Thu 10…
Wed Dec 09
Denoise your data in seconds using @dipymri implementation via CLI command: dipy_denoise_patch2self !! For #Python… https://t.co/98xtYkqtTj
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.