Cited by 1,371Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

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Dipy, a library for the analysis of diffusion MRI data

Open access

Garyfallidis, Eleftherios, Brett, Matthew, Amirbekian, Bagrat, Rokem, Ariel, van der Walt, Stefan, Descoteaux, Maxime, Nimmo-Smith, Ian, Dipy Contributors·2014·Frontiers in Neuroinformatics

DOI 10.3389/fninf.2014.00008PMC PMC3931231

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Abstract

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.

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Figure 1 The fornix is a C-shaped bundle connecting the hippocampus with the hypothalamus. The body of the fornix divides into two legs knows as the crus of the fornix. We can see here a detailed recreation of the fornix using dMRI data processed with Dipy.CC BY · PubMed Central

Figure 2 Dipy is one of the main projects of the Nipy community and depends strongly on Numpy, Scipy, and Cython. This diagram further shows the major Dipy sub-modules.CC BY · PubMed Central

Figure 3 Showing an axial slice in the center of S0 before (left) and after brain extraction (right) using median_otsu.CC BY · PubMed Central

Figure 4 Diffusion Tensor based scalar maps created with Dipy.CC BY · PubMed Central

Figure 5 Volumetric rendering of the 3D diffusion propagator of a 60° crossing with standard DSI (top-left) and DSI with deconvolution (top-right) with their corresponding ODFs (bottom).CC BY · PubMed Central

Figure 6 (A) Tensor ellipsoids color-coded with a DEC map, (B) tensor ODFs, (C) constant solid angle ODFs, and (D) constrained spherical deconvolution ODFs in a region in the centrum semiovale showing crossings between the corpus callosum, corticospinal tract and the superior longitudinal fasciculus.CC BY · PubMed Central

Figure 7 Left: An example of EuDX streamline tracking applied on a real human brain dataset and color-coded with a standard orientation colormap. Middle: Showing the QuickBundles centroids with random colors. Right: Showing the clusters color-coded with their corresponding centroid color.CC BY · PubMed Central

Figure 8 Left: Streamlines intersecting a mask in the corpus callosum (CC). Middle: Showing the streamlines with semi-transparency to make it easy to see the mask (yellow color). Right: Connectivity matrix of the CC streamlines that connect 89 cortical regions. A detailed tutorial which explains how it is possible to create similar connectivity matrices with your data is available at Dipy's website.CC BY · PubMed Central

Figure 9 Participation in the Dipy GitHub repository. The cumulative number of unique contributors has been extracted using the git log command and tallied. Many new contributors have joined the project after the 0.6 release.CC BY · PubMed Central

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Provencher, D., Hennebelle, M., Cunnane, S. C., Bérubé-Lauzière, Y., & Whittingstall, K. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00063 · Cited by 16
Gentoo Linux for Neuroscience - a replicable, flexible, scalable, rolling-release environment that provides direct access to development software
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“Neuroscientific software now usable under Gentoo includes but is not limited to: • Dipy (Garyfallidis et al. 2014) • FSL (Jenkinson et al. 2012) • Nipype (Gorgolewski et al. 2016) • Nilearn (Abraham et al. 2014) • PsychoPy (Peirce 2008) Herein we describe the implementation and current capabilities…”
Method
Cloudknot: A Python Library to Run your Existing Code on AWS Batch
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“First, inhouse implementations(38,39) of diffusion tensor imaging and HARDI reconstructions were performed using the Dipy library.(40) Fractional anisotropy (FA) was computed from the local diffusion tensors with a non-negative least square method.”
MethodSelf-citation
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Gray and White Matter Correlates of Dysphagia in Progressive Supranuclear Palsy
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“Diffusion tensors were estimated using nonlinear least squares fitting and used to calculate fractional anisotropy (FA) and mean diffusivity (MD) images in dipy.(29) The Johns Hopkins University “Eve” atlas was used to generate ROI-level data.”
Method
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Petrov, D., Ivanov, A., Faskowitz, J., Gutman, B. A., Moyer, D., Villalon, J. E., Jahanshad, N., & Thompson, P. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_59 · Cited by 12
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Mark, I. T., Wren-Jarvis, J., Xiao, J., Cai, L. T., Parekh, S. A., Bourla, I., Lazerwitz, M. C., Rowe, M. A., Marco, E. J., & Mukherjee, P. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1136424 · Cited by 12
“, 2016)] through the Dipy library (Garyfallidis et al., 2014).”
White Matter Microstructure Analysis in Subjective Memory Complaints and Cognitive Impairment: Insights from Diffusion Kurtosis Imaging and Free-Water DTI
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“15 Because the generation of ADC maps is computationally intensive and the skull produces a strong,unwanted signal in ADC maps,a brain mask is needed.”
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Moon, H. S., Mahzarnia, A., Stout, J. A., Anderson, R. J., Han, Z. Y., Tremblay, J. T., Badea, C. T., & Badea, A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00245 · Cited by 14
“We calculated the diffusion-based microstructural parameters using MRtrix3 ( Tournier et al., 2019), and RTOP, RTAP, and RTPP using Dipy ( Garyfallidis et al., 2014).”
Individual Variability in the Structural Connectivity Architecture of the Human Brain
Huang, W., Chen, H., Liu, Z., Dong, X., Feng, G., Liu, G., Yang, A., Zhang, Z., Shmuel, A., Su, L., et al. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.2139-23.2024 · Cited by 16
Lifespan normative modeling of brain microstructure
Villalón‐Reina, J. E., Zhu, A. H., Nabulsi, L., Thomopoulos, S. I., Moreau, C. A., Feng, Y., Chattopadhyay, T., Benavidez, S. M., Kushan, L., John, J. P., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72875-x · Cited by 15
“DTI maps (FA, MD, RD, and AD) were computed with DiPy ’ s tensor ﬁ tting function 72 for the following data-sets: AOMIC, CAMCAN, CHBMP, CHCP, HCP_A, HCP_D, HCP_YA, NIM-HANS, PNC, QTAB and UCLA.”
Training shortest-path tractography: Automatic learning of spatial priors
Kasenburg, N., Liptrot, M. G., Reislev, N. L., Ørting, S. N., Nielsen, M., Garde, E., & Feragen, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.031 · Cited by 12
“, 2007) with order 8 was computed with the DiPy package (Garyfallidis et al., 2014); on the HCP DWIs using all three shells, and on the preprocessed DWIs from the standard dataset.”
Method
Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI
Cieslak, M., Brennan, T., Meiring, W., Volz, L. J., Greene, C. A., Asturias, A., Suri, S., & Grafton, S. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.039 · Cited by 12
“Effort is currently underway to also load results from reconstructing in DiPy (Garyfallidis et al., 2014) and MRTRIX (Tournier et al., 2010).”
Background
Retrospective Distortion and Motion Correction for Free‐Breathing DW‐MRI of the Kidneys Using Dual‐Echo EPI and Slice‐to‐Volume Registration
Coll‐Font, J., Afacan, O., Hoge, W. S., Garg, H. K., Shashi, K., Marami, B., Gholipour, A., Chow, J. S., Warfield, S. K., & Kurugol, S. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27473 · Cited by 12
“The latter was done using in-house software for the IVIM fit with spatial regularization (C++) and the open-source software in DIPY for DTI.(30,31) The analysis of the results was done in Python and the statistical tests performed with the Wilcoxon test implementation in the SCIPY package (https://www.”
Method
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“Images were then corrected for eddy currents and fit to tensor eigenvalues using the Diffusion Imaging in Python Package [20].”
Method
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White matter tract strength correlates with therapy outcome in persistent developmental stuttering
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“Wilcoxen test significant at ***p .001, **p = .01 diffusion kurtosis model, implemented in DIPY (https://dipy.org/; Garyfallidis et al., 2014), to the diffusion data separately for each session and projected the FA, MD, RD, and AD onto the segmented fiber tracts generated by pyAFQ.”
Method
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“Details of the processing pipeline are described below and many internal operations of QSIPrep use Nilearn 0.6.2 (50) (RRID:SCR_001362) and Dipy (51).”
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“In the tensor estimation stage, the 6-component tensor model of the DIPY package (Garyfallidis et al., 2014) is then applied to get fractional anisotropy maps, which can reflect the structure and arrangement of the tensors.”
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“Another powerful Pyhton package, with a focus on neuroimaing, is DIPY (Garyfallidis et al., 2014), offering a multitude of ways to evaluated diffusion and perfusion MRI data.”
Background
Fiberweb: Diffusion Visualization and Processing in the Browser
Ledoux, L.-P., Morency, F. C., Cousineau, M., Houde, J., Whittingstall, K., & Descoteaux, M. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00054 · Cited by 12
“fODFs were estimated with Dipy (Garyfallidis et al., 2014) using a maximal spherical harmonics order of 8.”
BackgroundMethodInfluentialSelf-citation
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“The algorithm used for this purpose is a modified implementation of the median_otsu segmentation method from Dipy [19] • Computations are performed in parallel exploiting multicore processing, if available • 50 voxels are sent at a time to the SDP solver • All inserted diffusion volumes are used to…”
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“Tissue properties of the different tracts were characterized using the Diffusional Kurtosis Imaging model implemented in DIPY [15,32].”
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“Many internal operations of QSIPrep use Nilearn 0.10.2 (RRID: SCR_001362) 87,88 and DIPY 89 .”
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“The processing for the T1-weighted images in the pipeline consists of 8 steps, including denoising (Garyfallidis et al. 2014), N4 bias correction, brain mask extraction (Avants et al.”
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“The preprocessing stepsincludedskullstripping(usingtheDiffusionImaginginPythonlibrary – DiPy, https://dipy.org/) [14], Rician denoising (Marchenko-Pastur Principal Component Analysis [15]), Eddy currents correction, susceptibility distortion correction and motion correction [16].”
Intravoxel incoherent motion imaging in stroke infarct core and penumbra is related to long-term clinical outcome
Zimmermann, J., Reolon, B., Michels, L., Nemeth, B., Gorup, D., Barbagallo, M., Bellomo, J., van Niftrik, C. H. B., Sebök, M., Stumpo, V., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-81280-7 · Cited by 11
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Karimi, F., Steiner, M., Newton, T., Lloyd, B., Cassara, A. M., de Fontenay, P., Farcito, S., Triebkorn, P., Beanato, E., Wang, H., et al. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adb88f · Cited by 13
“…inhomogeneous maps of anisotropic WM conductivity, which our pipeline generates in the following steps: First, diffusion tensor images (DTI) are reconstructed in the Python package DIPY [75, 76] using the pre-processed and registered DWI data, and saved in a Sim4Life-compatible format.”
Relation of Alzheimer's disease-related TDP-43 proteinopathy to metrics from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI)
Lavrova, A., Pham, N. T. T., Reid, R. I., Boeve, B. F., Knopman, D. S., Petersen, R. C., Nguyen, A. T., Reichard, R. R., Dickson, D. W., Jack, C. R., et al. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.03.001 · Cited by 11
“Diffusion tensors were fit, and Fractional Anisotropy (FA) and Mean Diffusivity (MD) images were calculated using Dipy (Garyfallidis et al., 2014).”
Reconstruction of Diffusion Anisotropies Using 3D Deep Convolutional Neural Networks in Diffusion Imaging
Koppers, S., Friedrichs, M., & Merhof, D. (2017) · Mathematics and visualization · doi:10.1007/978-3-319-61358-1_17 · Cited by 11
Peripheral Innate Immune Activation Correlates With Disease Severity in GRN Haploinsufficiency
Ljubenkov, P. A., Miller, Z., Mumford, P., Zhang, J., Allen, I. E., Mitic, L. L., Staffaroni, A. M., Heuer, H. W., Rojas, J. C., Cobigo, Y., et al. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01004 · Cited by 10
“The Dipy nonlinear least-squares algorithm (22) was used to calculate diffusion tensors (DT), and a study specific template was created through iterative linear and non-linear registration of diffusion tensor images.”
Method
Imaging correlates of visual function in multiple sclerosis
Caverzasi, E., Cordano, C., Zhu, A. H., Zhao, C., Bischof, A., Kirkish, G., Bennett, D. J., Devereux, M., Baker, N., Inman, J., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235615 · Cited by 11
“After correcting for distortions due to eddy current and head motion, maps of mean diffusivity (MD) were calculated by fitting the diffusion tensor model within each voxel, using dipy fit tensor [19].”
Method
Towards linking diffusion MRI based macro- and microstructure measures with cortico-cortical transmission in brain tumor patients
Filipiak, P., Almairac, F., Papadopoulo, T., Fontaine, D., Mondot, L., Chanalet, S., Rachid Deriche, B., Clerc, M., & Wassermann, D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117567 · Cited by 11

Authors: Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Contributors, D.
Affiliations: Université de Sherbrooke (CA)
University of California, Berkeley (US)
University of California, San Francisco (US)
Stanford University (US)
Corresponding author: Garyfallidis, Eleftherios
ORCID: Garyfallidis, Eleftherios
Brett, Matthew
Rokem, Ariel
Descoteaux, Maxime
Venue: Frontiers in Neuroinformatics
Publisher: Frontiers Media SA
Published: 2014-02-21
Volume: 8
Pages: 8-8
ISSN: 1662-5196 (ISSN-L)
DOI: 10.3389/fninf.2014.00008
PMC: PMC3931231
OpenAlex: W2062791478
Cited by: 1,371
References: 98
Citations by year: 2026: 147 · 2025: 192 · 2024: 177 · 2023: 162 · 2022: 151 · 2021: 147 · 2020: 100 · 2019: 89 · 2018: 54 · 2017: 79 · 2016: 34 · 2015: 26 · 2014: 12
FWCI: 39.38
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Frontiers in Neuroinformatics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
Funders: Fonds Québécois de la Recherche sur la Nature et les Technologies
Natural Sciences and Engineering Research Council of Canada
National Eye Institute (F32EY022294)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: diffusion MRI, DIPY, white matter, fiber tractography, diffusion tensor model, constrained spherical deconvolution, diffusion spectrum imaging, probabilistic tracking, open source software, Python library

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Dipy, a library for the analysis of diffusion MRI data

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