A software for computational neuroanatomy with focus on diffusion magnetic resonance imaging (dMRI) analysis.


Eleftherios Garyfallidis1, Ariel Rokem2, Matthew Brett3, Bago Amirbekian4, Omar Ocegueda5, Marc-Alexandre Côté6, Serge Koudoro7, Gabriel Girard8, Mauro Zucchelli9, Rafael Neto Henriques10, Matthieu Dumont11, Ranveer Aggarwal12 and more Dipy Contributors13

Estimated cost



Beta v0.14, license BSD

DIPY implements a broad range of algorithms for denoising, registration, reconstruction, tracking, clustering, visualization, and statistical analysis of MRI data.

DIPY is a software for computational neuroanatomy, focusing mainly on diffusion magnetic resonance imaging (dMRI) analysis. dMRI is an application of MRI that can be used to measure structural features of brain white matter. The purpose of DIPY is to make it easier to do better diffusion MR imaging research. Following up with the nipy mission statement the aim is to build software that is

  • clearly written
  • clearly explained
  • a good fit for the underlying ideas
  • a natural home for collaboration

Numerous classical signal reconstruction techniques have been implemented in DIPY, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. Here are just a few of the state-of-the-art technologies and algorithms which are provided in DIPY:

  • Reconstruction algorithms: CSD, DSI, GQI, DTI, DKI, QBI, SHORE and MAPMRI.
  • Fiber tracking algorithms: deterministic and probabilistic.
  • Simple interactive visualization of ODFs and streamlines.
  • Apply different operations on streamlines (selection, resampling, registration).
  • Simplify large datasets of streamlines using QuickBundles clustering.
  • Reslice datasets with anisotropic voxels to isotropic.
  • Calculate distances/correspondences between streamlines.
  • Deal with huge streamline datasets without memory restrictions (using the .dpy file format).
  • Visualize streamlines in the same space as anatomical images.

With the help of some external tools you can also:

  • Read many different file formats e.g. Trackvis or Nifti (with nibabel).
  • Examine your datasets interactively (with ipython).

In contrast to many other scientific software projects, DIPY is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, DIPY today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.



1Indiana University, IN, USA
2University of Washington, WA, USA
3Birmingham University, Birmingham, UK
4Databricks, San Francisco, CA, USA
5Google, San Francisco, CA
6Microsoft Research, Montreal, QC, CA
7Indiana University, IN, USA
8Swiss Federal Institute of Technology (EPFL), Lausanne, CH
9INRIA, Sophia-Antipolis, France
10Cambridge University, UK
11Imeka, Sherbrooke, QC, CA
12Microsoft, Hyderabad, Telangana, India