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Front. Neuroinform., 21 February 2014 | doi: 10.3389/fninf.2014.00008

Dipy, a library for the analysis of diffusion MRI data

  • 1Computer Science Department, University of Sherbrooke, Sherbrooke, QC, Canada
  • 2MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  • 3Henry H. Wheeler, Jr. Brain Imaging Center, University of California, Berkeley, CA, USA
  • 4Department of Neurology and Graduate Group in Bioengineering, University of California, San Francisco, CA, USA
  • 5Department of Psychology, Stanford University, Stanford, CA, USA
  • 6Department of Mathematical Sciences, Division of Applied Mathematics, Stellenbosch University, Stellenbosch, South Africa
  • 7http://dipy.org/developers.html

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, 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. 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.

Keywords: diffusion MRI, DTI, DSI, HARDI, dMRI, Python, free open source software, tractography

Citation: 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. Front. Neuroinform. 8:8. doi: 10.3389/fninf.2014.00008

Received: 10 October 2013; Accepted: 23 January 2014;
Published online: 21 February 2014.

Edited by:

Fernando Perez, University of California at Berkeley, USA

Reviewed by:

Krzysztof Gorgolewski, Max Planck Institute for Human Cognitive and Brain Sciences, Germany
Lester Melie-Garcia, Cuban Neuroscience Center, Cuba

Copyright © 2014 Garyfallidis, Brett, Amirbekian, Rokem, van der Walt, Descoteaux, Nimmo-Smith and Dipy Contributors. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Eleftherios Garyfallidis, Computer Science Department, University of Sherbrooke, 2500 University Boulevard, Sherbrooke, QC J1K 2R1, Canada e-mail: garyfallidis@gmail.com

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