Dipy, a library for the analysis of diffusion MRI data

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 t...

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Main Authors:	Garyfallidis, Eleftherios, Brett, Matthew, Amirbekian, Bagrat, Rokem, Ariel, van der Walt, Stefan, Descoteaux, Maxime, Nimmo-Smith, Ian
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3931231/

id	pubmed-3931231
recordtype	oai_dc
spelling	pubmed-39312312014-03-05 Dipy, a library for the analysis of diffusion MRI data Garyfallidis, Eleftherios Brett, Matthew Amirbekian, Bagrat Rokem, Ariel van der Walt, Stefan Descoteaux, Maxime Nimmo-Smith, Ian Neuroscience 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. Frontiers Media S.A. 2014-02-21 /pmc/articles/PMC3931231/ /pubmed/24600385 http://dx.doi.org/10.3389/fninf.2014.00008 Text en Copyright © 2014 Garyfallidis, Brett, Amirbekian, Rokem, van der Walt, Descoteaux, Nimmo-Smith and Dipy Contributors. http://creativecommons.org/licenses/by/3.0/ 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Garyfallidis, Eleftherios Brett, Matthew Amirbekian, Bagrat Rokem, Ariel van der Walt, Stefan Descoteaux, Maxime Nimmo-Smith, Ian
spellingShingle	Garyfallidis, Eleftherios Brett, Matthew Amirbekian, Bagrat Rokem, Ariel van der Walt, Stefan Descoteaux, Maxime Nimmo-Smith, Ian Dipy, a library for the analysis of diffusion MRI data
author_facet	Garyfallidis, Eleftherios Brett, Matthew Amirbekian, Bagrat Rokem, Ariel van der Walt, Stefan Descoteaux, Maxime Nimmo-Smith, Ian
author_sort	Garyfallidis, Eleftherios
title	Dipy, a library for the analysis of diffusion MRI data
title_short	Dipy, a library for the analysis of diffusion MRI data
title_full	Dipy, a library for the analysis of diffusion MRI data
title_fullStr	Dipy, a library for the analysis of diffusion MRI data
title_full_unstemmed	Dipy, a library for the analysis of diffusion MRI data
title_sort	dipy, a library for the analysis of diffusion mri data
description	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.
publisher	Frontiers Media S.A.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3931231/
_version_	1612060681819914240

Dipy, a library for the analysis of diffusion MRI data

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