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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Poliva, O., Poliva, O., Herrera, C., Sugai, K., Whittle, N., Leek, M. R., Yi, A. C., Barnes, S., Holshouser, B., & Venezia, J. H. (2024) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlae069 · Cited by 3
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Grouza, V., Bagheri, H., Liu, H., Tuznik, M., Wu, Z., Robinson, N., Siminovitch, K. A., Peterson, A. C., & Rudko, D. A. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120850 · Cited by 3
“Raw diffusion-weighted GRASE images were corrected for Gibbs ringing and denoised using the Marcenko-Pastur PCA algorithm (Veraart et al., 2016), implemented in Dipy.”
Streamline tractography of the fetal brain in utero with machine learning
Liu, W., Calixto, C. A., Warfield, S. K., & Karimi, D. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00537 · Cited by 3
“For analyzing adult brain dMRI data, there exists a repertoire of reliable computational tools ( Cai et al., 2021; Cieslak et al., 2021; Dubos et al., 2023; Garyfallidis et al., 2014; Joseph et al., 2021; Theaud et al., 2020; J.- D. Tournier et al., 2019).”
Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging
Li, Y., Zhuo, Z., Liu, C., Duan, Y., Shi, Y., Wang, T., Li, R., Wang, Y., Jiang, J., Xu, J., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120858 · Cited by 4
“The interpolation was performed with the SHORE basis (Merlet and Deriche, 2013) using the Dipy software (Garyfallidis et al., 2014).”
Gray matter based spatial statistics framework in the 1-month brain: insights into gray matter microstructure in infancy
DiPiero, M. A., Rodrigues, P. G., Justman, M., Roche, S., Bond, E., Guerrero-Gonzalez, J. M., Davidson, R. J., Planalp, E. M., & Dean, D. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02853-w · Cited by 3
End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI
Koska, İ. Ö., Selver, M. A., Gelal, F. M., Uluç, M. E., Çetinoğlu, Y. K., Yurttutan, N., Serındere, M., & Dıcle, O. (2024) · Neuroradiology · doi:10.1007/s00234-024-03520-x · Cited by 3
Multi-parametric diffusion spectrum imaging in tuberous sclerosis complex: Identifying cortical tubers and predicting genotypes
Sun, H., Yan, Z., Gao, J., Zheng, Y., Zheng, Y., Song, Y., Liu, Y., Lin, Z., Shen, W., Jin, F., et al. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.111963 · Cited by 3
The multiscale self-similarity of the weighted human brain connectome
Barjuan, L., Zheng, M., & Serrano, M. Á. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012848 · Cited by 3
“Employing Dipy [41], the corrected DWI of each subject was used to fit a second-order tensor for each voxel and its various voxelwise scalar maps.”
3D Anatomical Structure-Guided Deep Learning for Accurate Diffusion Microstructure Imaging
Ma, X., Cheng, J., Fan, W., Wu, R., Ye, Y., & Wang, S. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980927 · Cited by 3
“Specifically, we extract T1w images using the hidden Markov random field model available in the 3D/4D+ imaging library in Python (DIPY)[21], categorizing voxels into three types of tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF).”
Executive dysfunction relates to salience network desegregation in behavioural variant frontotemporal dementia
Matyi, M. A., Radhakrishnan, H., Olm, C. A., Phillips, J. S., Cook, P. A., Rhodes, E., Gee, J. C., Irwin, D. J., McMillan, C. T., & Massimo, L. M. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103853 · Cited by 6
“Many internal operations of QSIPrep use Nilearn 0.10.1 (Abraham et al., 2014) (RRID:SCR_001362) and Dipy (Garyfallidis et al., 2014).”
Influential
Towards an Informed Choice of Diffusion MRI Image Contrasts for Cerebellar Segmentation
Legarreta, J. H., Zhou, L., Chen, Y., Zhang, F., Yeterian, E. H., Makris, N., Rushmore, J., Yogesh Rathi, A., & O’Donnell, L. J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70317 · Cited by 2
“In particular, the DIPY library is harnessed through SCILPY scripts to compute the DTI-and DKI-model features, and to extract dMRI shell data.”
Free water elimination tractometry for aging brains
Chang, K., Burke, L., LaPiana, N., Howlett, B., Hunt, D., Dezelar, M., Andre, J. B., Curl, P. K., Ralston, J. D., Rokem, A., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.991 · Cited by 3
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Transcriptional upregulation of myelin components in spontaneous myelin basic protein-deficient mice
Staats, K. A., Pombal, D., Schönefeldt, S., Helleputte, L. V., Maurin, H., Dresselaers, T., Govaerts, K., Himmelreich, U., LEUVEN, F. V., Bosch, L. V. D., et al. (2015) · Brain Research · doi:10.1016/j.brainres.2015.02.021 · Cited by 3
3D-SSF: A bio-inspired approach for dynamic multi-subject clustering of white matter tracts
Chekir, A., Hassas, S., Descoteaux, M., Côté, M.-A., Garyfallidis, E., & Oulebsir-Boumghar, F. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.01.016 · Cited by 3
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Diffuse Low Grade Glioma NMR Assessment for Better Intra-operative Targeting Using Fuzzy Logic
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Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline
Jordan, K., Keshavan, A., Caverzasi, E., Osorio, J. A., Papinutto, N., Amirbekian, B., Berger, M. S., & Henry, R. G. (2020) · Journal of Neuroimaging · doi:10.1111/jon.12713 · Cited by 4
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Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
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Characterization of diffusion MRI using the mean apparent propagator model in hemodialysis patients: A pilot study
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Data-driven physical actigraphy patterns relate to cognitive and vascular health in older adults
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Unraveling multi-fixel microstructure with tractography and angular weighting
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“In our experiments, tractography of the WM was performed using a probabilistic algorithm based on the Constrained Spherical Deconvolution (CSD) model (Tournier et al., 2007) to produce a whole-brain tractogram (Garyfallidis et al., 2014).”
BackgroundMethod
versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database
Caron, A. V., Shmuel, A., Hao, Z., & Descoteaux, M. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1191200 · Cited by 3
“Upsampling Once denoised, both the T1w image and the diffusion volumes are upsampled to a finer resolution using Dipy (Garyfallidis et al., 2014).”
BackgroundMethodInfluentialSelf-citation
Consistency of muscle fibers directionality in human thigh derived from diffusion-weighted MRI
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Aortic valve Replacement compared to Transcatheter Implant and its relationship with COgnitive Impairment (ARTICO) evaluated with neuropsychological and advanced neuroimaging: a longitudinal cohort study
Gomis, M., Fernández, C., Dacosta‐Aguayo, R., Carrillo, X., Martínez, S., Muñoz-Guijosa, C., Berastegui, E., Valentín, A. G., Puig, J., Bernal, E., et al. (2023) · BMC Neurology · doi:10.1186/s12883-023-03362-9 · Cited by 3
“All subjects’ FA data will be aligned into a common space using the nonlinear registration tool FNIRT [49, 50], which uses a b-spline representation of the registration warp field [51, 52], resulting in all images transformed into 1 mm isotropic, MNI152 standard space.”
Method
Overview of methods and available tools used in complex brain disorders
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“) is an open-source Python library used for the analysis of diffusion magnetic resonance imaging (dMRI) data 81 .”
Method
Improved reconstruction of crossing fibers in the mouse optic pathways with orientation distribution function fingerprinting
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“In both cases, we used our in-house Python code available as an extension of DIPY 34 at: https://github.com/ filipp02/dipy_odffp Note that ODF-FP was not intended for use with any particular reconstruction algorithm.”
Method
Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study
Nabulsi, L., Chandio, B. Q., Dhinagar, N. J., Laltoo, E., McPhilemy, G., Martyn, F. M., Hallahan, B. P., McDonald, C., Thompson, P. M., & Cannon, D. M. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10339964 · Cited by 3
Large-scale Normative Modeling of Brain Microstructure
Villalón‐Reina, J. E., Zhu, A. H., Nir, T. M., Thomopoulos, S. I., Laltoo, E., Kushan, L., Bearden, C. E., Jahanshad, N., & Thompson, P. M. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim56729.2023.10373451 · Cited by 5
“For the datasets without precomputed DTI maps, FA and MD were computed with DIPY [28] or FSL [29] on the single shell or on the lowest shell available if multi-shell data were provided.”
Method
Automatic Fast and Reliable Recognition of a Small Brain White Matter Bundle
Kruper, J., & Rokem, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47292-3_7 · Cited by 3
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Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes
Patel, J. H., Schöttner, M., Tarun, A., Tourbier, S., Alemán‐Gómez, Y., Hagmann, P., & Bolton, T. A. W. (2024) · Network Neuroscience · doi:10.1162/netn_a_00368 · Cited by 3
“Also, the fiber orientation distribution functions (ODFs) were estimated using MRtrix CSD of order 6 for the DTI data and Dipy (Garyfallidis et al., 2014) simple”
Method
Improvement of diffusion tensor imaging–based tractography by free-water correction in nonedematous gliomas: assessment with brain mapping
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TSAD: Two-Stage Separable Adversarial Distortion-Based Robust Watermarking Framework for Diffusion Tensor Imaging
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Phase Stabilization with Motion Compensated Diffusion Weighted Imaging
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“45 The registration transformation was then applied to both the real and imaginary components of the complex image data.”
Deep learning‐based reconstruction on intensity‐inhomogeneous diffusion magnetic resonance imaging
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BundleAGE: Predicting White Matter Age using Along-Tract Microstructural Profiles from Diffusion MRI
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Microstructural abnormalities of the dentato-rubro-thalamo-cortical tract in tremor dominant Parkinson’s disease and essential tremor plus syndrome
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Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace
Qian, C., Han, M., Zhu, L., Wang, Z., Guan, F., Guo, Y., Ruan, D., Guo, Y., Kang, T., Lin, J., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103546 · Cited by 3
Spatial and signal features of white matter integrity and associations with clinical factors: A CARDIA brain MRI study
Vedaei, F., Srinivasan, D., Parker, D., Erus, G., Dolui, S., Sorond, F. A., Jacobs, D. R., Launer, L. J., Lackland, D. T., Davatzikos, C. A., et al. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103768 · Cited by 3
“A brain mask was fit to the average B0 image with BET2, and a tensor model was applied to the data in DIPY, from which the standard FA and MD measures were computed.”
Development of the arcuate fasciculus is linked to learning gains in reading
Roy, E. A., Harriott, E. M., Nguyen, T. Q., Richie-Halford, A., Rokem, A., Cutting, L. E., & Yeatman, J. D. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00542 · Cited by 3
“Fiber orientation distributions (FOD) were estimated in each voxel using constrained spherical deconvolution implemented in DIPY ( Garyfallidis et al., 2014; Tournier et al., 2007).”
InfluentialSelf-citation
Variation in high-amplitude events across the human lifespan
Jo, Y., Tanner, J., Seguin, C., Faskowitz, J., & Betzel, R. F. (2025) · Network Neuroscience · doi:10.1162/netn.a.515 · Cited by 3
“Tractography was performed using Dipy ’ s Local Tracking module (Garyfallidis et al., 2014).”
The Stein effect for Fréchet means
McCormack, A., & Hoff, P. D. (2022) · The Annals of Statistics · doi:10.1214/22-aos2245 · Cited by 3
“We now apply the estimator (23) to the ‘Sherbrooke 3-shell’ dataset that is publicly available from the DIPY Python package [31].”
Method
A Signal Peak Separation Index for Axisymmetric B-Tensor Encoding
Rensonnet, G., Rafael‐Patiño, J., Macq, B. M. M., Thiran, J., Girard, G., & Pizzolato, M. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-73018-5_3 · Cited by 3
“The estimation of fascicle orientation was performed with DIPY [10] routines by first reconstructing the ODF from the noisy signal and then extracting the maxima from the ODF.”
Method
The Microstructural Features of the Diffusion-Simulated Connectivity (DiSCo) Dataset
Rafael‐Patiño, J., Girard, G., Truffet, R., Pizzolato, M., Thiran, J., & Caruyer, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87615-9_14 · Cited by 4
“Peaks were extracted from the FODs for each voxel size (25 μm, 50 μm and 100 μm) using Dipy [12].”
Method
Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms
Kuehn, T. K., Mushtaha, F. N., KHAN, A. R., & Baron, C. A. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.833209 · Cited by 2
“DIPY (Garyfallidis et al., 2014) was used to perform weighted ordinary least squares fits of Diffusion Tensor Imaging (DTI) (Basser et al.”
Method
On the computation of integrals over fixed-size rectangles of arbitrary dimension
Ocegueda, O., Dalmau, O., Garyfallidis, E., Descoteaux, M., & Rivera, M. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.05.008 · Cited by 3
Self-citation
A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
Moyer, D., Gutman, B. A., Jahanshad, N., & Thompson, P. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59050-9_27 · Cited by 3
“Probabilistic streamline tractography was conducted using the DWI in 2mm isotropic MNI 152 space, using Dipy’s [7] implementation of constrained spherical deconvolution (CSD) [21] with a harmonic order of 6.”
Method
Repeated Tractography of a Single Subject: How High Is the Variance?
Gu, X., Eklund, A., & Knutsson, H. E. (2017) · Mathematics and visualization · doi:10.1007/978-3-319-61358-1_14 · Cited by 3
“To further extend this study, we performed deterministic tractography on the same datasets using Dipy (Garyfallidis et al., 2014).”
MethodInfluential
Applying Python in Brain Science Education
Zhang, X., Huang, J., Yang, Y., He, X., Liu, R., & Zhong, N. (2019) · International Joint Conference on Information, Media and Engineering (IJCIME) · doi:10.1109/ijcime49369.2019.00086 · Cited by 3
“…to interact with existing neuroimaging software, including ANTS, SPM, FSL, FreeSurfer, Camino, MRtrix, MNE, AFNI, Slicer et al. In addition, Dipy [6] is used for dMRI analysis, Mindboggle [7] is used to improve prediction accuracy and consistency of brain image data annotation and morphology,…”
Method
Diffusion propagator metrics are biased when simultaneous multi-slice acceleration is used
Muftuler, L. T., Nencka, A. S., & Koch, K. M. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.11.003 · Cited by 4

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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