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 11-15, 2019) is now open for registration:
DIPY 1.0 released August 5, 2019.
DIPY 0.16 released March 10, 2019.
DIPY 0.15 released December 12, 2018.
See some of our Past Announcements
Highlights
DIPY 1.0.0 is now available. New features include:
Critical API changes
Large refactoring of tracking API.
New denoising algorithm: MP-PCA.
New Gibbs ringing removal.
New interpolation module:
dipy.core.interpolation.New reconstruction models: MTMS-CSD, Mean Signal DKI.
Increased coordinate systems consistency.
New object to manage safely tractography data: StatefulTractogram
New command line interface for downloading datasets: FetchFlow
Horizon updated, medical visualization interface powered by QuickBundlesX.
Removed all deprecated functions and parameters.
Removed compatibility with Python 2.7.
Updated minimum dependencies version (Numpy, Scipy).
All tutorials updated to API changes and 3 new added.
Large documentation update.
Closed 289 issues and merged 98 pull requests.
See Older Highlights.
Tweets
Wed Oct 23 |
RT @Ragini__Verma: Recruiting multiple data analysts for my lab @DiCIPHR for processing and analyzing large clinical datasets in tumor, tra… |
Tue Oct 22 |
Check out the tutorials on #DIPYreconstruction: Diffusion Kurtosis Imaging (DKI) and its variations!! Mean Signal a… https://t.co/YDH6WLkJjP |
Mon Oct 21 |
RT @dipymri: Registration for DIPY Workshop 2020 is now open. Visit https://t.co/9gh65ZngaR for information about dates, program and accomm… |
Fri Oct 11 |
RT @soffiafdz: @egarzav @mallarchkrvrty1 @sattertt @AlcauterSarael Shotout to @garyfallidis and @dipymri as well. Amazing talks in general. |
Wed Oct 09 |
RT @garyfallidis: Tomorrow @cimatoficial in Guanajuato, Leon, Mexico! Talking about new exiting brain mapping research and features of DIPY… |
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
