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

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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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“To generate consistent brain masks that were inclusive of all brain tissue voxels, initial tensor models were first fit on unprocessed diffusion data for each subject (Garyfallidis et al., 2014) (DIPY v1.”
Cross-hemicord spinal fiber reorganization associates with cortical sensory and motor network expansion in the rat model of hemicontusion cervical spinal cord injury
Mihailović, J. M., Sanganahalli, B. G., Hyder, F., Chitturi, J., Elkabes, S., Heary, R. F., & Kannurpatti, S. S. (2023) · Neuroscience Letters · doi:10.1016/j.neulet.2023.137607 · Cited by 2
BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data
Feng, Y., Chandio, B. Q., Villalón‐Reina, J. E., Thomopoulos, S. I., Joshi, H., Nair, G., Joshi, A. A., Venkatasubramanian, G., John, J. P., & Thompson, P. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47292-3_14 · Cited by 2
A framework for optimizing the acquisition protocol multishell diffusion‐weighted imaging for multimodel assessment
Ciceri, T., Luca, A. D., Agarwal, N., Arrigoni, F., & Peruzzo, D. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5141 · Cited by 2
“28,29 The mathematical formulation of the framework is included in Appendix A, the acquired data are freely available (10.5281/zenodo.10801619), and the Python code is available upon request.”
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Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks
Moon, H. S., Mahzarnia, A., Stout, J. A., Anderson, R. J., Han, Z. Y., Badea, C. T., & Badea, A. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3005695 · Cited by 2
The added value of diffusion tensor imaging with systematic bias correction for the assessment of liver morphology and physiology
Mazur, W., Spees, W. M., & Krzyżak, A. T. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5259 · Cited by 2
“18 Secondly, denoised data were corrected for B1 oscillatory field inhomogeneity in MRTrix3 software 34 using the N4 algorithm as provided in ANTs.”
Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains
Lin, R., Kebiri, H., Gholipour, A., Chen, Y., Thiran, J., Karimi, D., & Cuadra, M. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-86920-4_3 · Cited by 2
“…validation was performed based on the agreement rate (AR) [17] between the number of fibers estimated by the number of peaks extracted using Dipy [10] (with a mean separation angle of 45°, a maximum number of 3 peaks and relative peak threshold of 0.5), the angular error (AE) between those peaks…”
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Bertalan, G., Krepuska, M., Toth, D., Madjidyar, J., Thurner, P., Schubert, T. B., & Kulcsár, Z. (2024) · Sci · doi:10.3390/sci6040064 · Cited by 2
Spectral graph model for fMRI: A biophysical, connectivity-based generative model for the analysis of frequency-resolved resting-state fMRI
Raj, A., Sipes, B. S., Verma, P., Mathalon, D. H., Biswal, B. B., & Nagarajan, S. S. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00381 · Cited by 2
A Comparative Study of Rigid Three-Dimensional Image Registration Methods for Powder Bed Fusion with Laser Beam of Metals Using a Gold Standard Approach
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“9usingtheDIPY3Dimageregistrationlibrary[38].”
Replace2Self: Self-Supervised Denoising Based on Voxel Replacing and Image Mixing for Diffusion MRI
Wu, L., Wang, L., Deng, Z., Zhu, Y., & Wei, H. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3552611 · Cited by 2
Experimentally induced colitis impacts myelin development and home-cage behavior in young pigs regardless of supplementation with oral gamma-cyclodextrin-encapsulated tributyrin
Sutkus, L. T., Sommer, K. M., Li, Z., Sutton, B. P., Donovan, S. M., & Dilger, R. N. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1484497 · Cited by 2
“Raw DWI images were first denoised using the Marchenko-Pastur Principal Component Analysis (MPPCA) method (Manjón et al., 2013; Neto Henriques, 2018; Veraart et al., 2016a; Veraart et al., 2016b) within the DIPY toolbox (Garyfallidis et al., 2014).”
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Lan, Z., Chen, Y., Rushmore, J., Zekelman, L., Makris, N., Yogesh Rathi, A., Golby, A. J., Zhang, F., & O’Donnell, L. J. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00569 · Cited by 2
“The AFQ method ( Yeatman et al., 2012) implemented in the dipy software ( Garyfallidis et al., 2014) automatically computes the profile, which consists of mean FA values at sequential locations along a fiber tract.”
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Zhang, H., Zhou, R., Lu, W., Su, Y., Cai, Y., Huang, J., Shen, H., Ding, L., Wang, Y., Zhang, M., et al. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.04.064 · Cited by 2
HyTract: Advancing tractography for neurosurgical planning with a hybrid method integrating neural networks and a path search algorithm
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Reduction of systematic errors in diffusion tensor imaging of the human brain as a prospect for increasing the precision of planning neurosurgical operations with particular emphasis on fiber tracking
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Evaluating the Effect of Post-processing Steps When Analyzing Cardiac Diffusion Tensor Data
Cork, T. E., Hannum, A. J., Loecher, M., Perotti, L. E., & Ennis, D. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-94562-5_13 · Cited by 2
Evaluation of Interstitial Fluid Volume and Diffusivity in Patients With Idiopathic Normal Pressure Hydrocephalus Using Spectral Diffusion Analysis
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“The entire cDTI image processing pipeline was integrated using the Dipy Framework [39] with the exception of the shot-rejection module and the respiratory sorting module.”
AGFS-tractometry: A novel atlas-guided fine-scale tractometry approach for enhanced along-tract group statistical comparison using diffusion MRI tractography
Zheng, R., Zhang, W., Li, Y., Zhu, X., Lan, Z., Rushmore, J., Yogesh Rathi, A., Makris, N., O’Donnell, L. J., & Zhang, F. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103892 · Cited by 2
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“We used the peak-ﬁnding function from the dipy Python library [18].”
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“Fibre orientation distribution functions (fODFs) were computed for every voxel using constrained spherical deconvolution [17] with order 8 using the DiPy [18] package.”
Method
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Hierarchical Geodesic Modeling on the Diffusion Orientation Distribution Function for Longitudinal DW-MRI Analysis
Kim, H., Hong, S., Styner, M. A., Piven, J., Botteron, K. N., & Gerig, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_31 · Cited by 2
Cortically constrained shape recognition: Automated white matter tract segmentation validated in the pediatric brain
Jordan, K., Lauricella, M., Licata, A. E., Sacco, S., Asteggiano, C., Wang, C., Sudarsan, S. P., Watson, C., Scheffler, A. W., Battistella, G., et al. (2021) · Journal of Neuroimaging · doi:10.1111/jon.12854 · Cited by 2
Denoising Diffusion MRI via Graph Total Variance in Spatioangular Domain
Wu, H., & Yan, S. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/4645544 · Cited by 2
“Both ANLM and MP-PCA are implemented by DIPY [33] (3) XQ nonlocal means (XQ-NLM): XQ-NLM [17] denoises the signal via weighted averaging of selfsimilar information, which is defined in the spatialangular domain.”
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Kai, J., & KHAN, A. R. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.777853 · Cited by 2
“, 2012) clustering, as implemented within the DIPY library (Garyfallidis et al., 2014), was also used as a secondary clustering technique.”
Method
Role of insular cortex lesions in determining the pathogenetic subtype of ischemic stroke
Kulesh, A. A., Kulikova, S. P., Евгеньевич, Д. В., Александрович, М. С., Bartuli, E., Владимирович, Б. А., Syromyatnikova, L. I., Sobyanin, K., & Karakulova, Y. V. (2022) · Neurology neuropsychiatry Psychosomatics · doi:10.14412/2074-2711-2022-2-11-17 · Cited by 2
High Resolution Ex Vivo Diffusion Tensor Distribution MRI of Neural Tissue
Magdoom, K. N., Komlosh, M. E., Saleem, K. S., Gasbarra, D., & Basser, P. J. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.807000 · Cited by 2
“The thermal noise in the images was filtered using a Marchenko-Pastur principal component analysis (PCA) algorithm [25] implemented in DIPY software [26], and the effect of eddy current induced image translation and shearing were reduced by registering the individual DWI volumes with b = 0 s/mm(2) acquisition using 3D…”
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Neural network algorithms predict new diffusion MRI data for multi-compartmental analysis of brain microstructure in a clinical setting
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A taxonomic guide to diffusion MRI tractography visualization tools
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Multi-Shell Diffusion MRI Measures of Brain Aging: A Preliminary Comparison From ADNI3
Nir, T. M., Thomopoulos, S. I., Villalón‐Reina, J. E., Zavaliangos‐Petropulu, A., Dennis, E. L., Reid, R. I., Bernstein, M. A., Borowski, B., Jack, C. R., Weiner, M. W., et al. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759486 · Cited by 2
“To compare performance with a measure derived from a HARDI technique non-parametric q-ball imaging (QBI) we also fit constant solid angle orientation distribution functions (CSA-ODFs) [18, 19] using the DiPy package [20] to generate generalized FA (GFA1000) maps.”
Method
Tractography in Curvilinear Coordinates
Hussain, U., Baron, C. A., & KHAN, A. R. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.716538 · Cited by 2
“Both of these algorithms are implemented in the dipy library (Garyfallidis et al., 2014).”
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Investigating The Functional Consequence Of White Matter Damage: An Automatic Pipeline To Create Longitudinal Disconnection TractogramsPreprint
Jordan, K., Keshavan, A., Caverzasi, E., Osorio, J. A., Papinutto, N., Amirbekian, B., Berger, M. S., & Henry, R. G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/140137 · Cited by 2
“A tensor model was fit to the corrected HARDI data using the open-source package Diffusion Imaging in Python (Dipy) (Garyfallidis et al. 2014) and the resulting parameters used to calculate tensor metrics.”
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“8.0 implementation (Garyfallidis et al. 2014) of a non-local means de-noising algorithm (Coupe et al. 2008) to boost signal-to-noise ratio.”
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Pre-operarative Multivariate Connectome Analysis for Glioma PatientsPreprint
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“Initially tractographies for all subjects have been generated by processing DTI data with the Python library Dipy [9], stemming from 2,000,000 seed-points and stopping when the fractional anisotropy was smaller than < 0.”
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“The dMRI ﬁle is then resliced to the desired voxel size (speciﬁed by the user, with a reasonable default of 2mm 3 if not speciﬁed) using Dipy’s reslice function with trilinear interpolation[2].”
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Calibrated Diffusion Tensor EstimationPreprint
Karimi, D., Warfield, S. K., & Gholipour, A. (2021) · arXiv (Cornell University) · Cited by 2
Mindfulness Promotes Control of Brain Network Dynamics for Self-Regulation and Discontinues the Past from the PresentPreprint
Zhou, D., Kang, Y., Cosme, D., Jovanova, M., He, X., Mahadevan, A. S., Stanoi, O. A., Brynildsen, J. K., Cooper, N., Cornblath, E. J., et al. (2021) · doi:10.31234/osf.io/u83my · Cited by 2
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Badea, A., Stout, J. A., Anderson, R. J., Cofer, G. P., Duan, L. L., & Vogelstein, J. T. (2022) · Wiley eBooks · doi:10.1002/9783527827244.ch13 · Cited by 2
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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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