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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Progressive Subsampling for Oversampled Data - Application to Quantitative MRI
Blumberg, S. B., Lin, H., Grussu, F., Zhou, Y., Figini, M., & Alexander, D. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_40 · Cited by 5
“As MUDI data provides combined diﬀusion and relaxometry information, to evaluate the practical impact of the diﬀerent reconstructions we estimated T1, T2* values and DTI parameters with a dictionary-based approach in [13].”
Fiblets for Real‐Time Rendering of Massive Brain Tractograms
Schertzer, J., Mercier, C., Rousseau, S., & Boubekeur, T. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14486 · Cited by 5
Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging
Shusharina, N., Liu, X., Coll‐Font, J., Foster, A. N., Fakhri, G. E., Woo, J., Bortfeld, T. R., & Nguyen, C. T. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac8045 · Cited by 5
Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on “Frontiers in Neuroinformatics”
Li, X., & Liang, H. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.902452 · Cited by 5
Does peripheral neuroinflammation predict chronicity following whiplash injury? Protocol for a prospective cohort study
Ridehalgh, C., Fundaun, J., Bremner, S. A., Cercignani, M., Young, R. C. D., Trivedy, C., Novak, A., Greening, J., Schmid, A. B., & Dilley, A. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-066021 · Cited by 5
Randomized iterative spherical‐deconvolution informed tractogram filtering
Hain, A., Jörgens, D., & Moreno, R. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120248 · Cited by 6
Total-Body Irradiation Alters White Matter Volume and Microstructural Integrity in Rhesus Macaques
Johnson, B. J., Barcus, R. A., Olson, J. D., Lipford, M. E., Andrews, R. N., Dugan, G. O., Tooze, J. A., Kim, J., Deycmar, S., Whitlow, C. T., et al. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.11.014 · Cited by 5
Multivariate investigation of aging in mouse models expressing the Alzheimer’s protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics
Moon, H. S., Mahzarnia, A., Stout, J. A., Anderson, R. J., Strain, M., Tremblay, J. T., Han, Z. Y., Niculescu, A., MacFarlane, A., King, J., et al. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02731-x · Cited by 4
“Diffusion parameters were reconstructed using MRtrix3 (Tournier et al. 2019) producing –2 million tracts and MAP MRI parameters were reconstructed using DIPY (Garyfallidis et al. 2014; Tournier et al. 2012).”
CCADD: An online webserver for Alzheimer's disease detection from brain MRI
Panigrahi, P., Das, S., & Chakrabarti, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108622 · Cited by 6
Diffusion tensor transformation for personalizing target volumes in radiation therapy
Buti, G., Ajdari, A., Bridge, C. P., Sharp, G. C., & Bortfeld, T. R. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103271 · Cited by 5
Chemical shift–encoded multishot EPI for navigator‐free prostate DWI
Dong, Y., Atkinson, D., Koolstra, K., van Osch, M. J. P., & Börnert, P. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30334 · Cited by 5
Sex Differences in the Brain’s White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models
Benavidez, S. M., Abaryan, Z., Kim, G. S., Laltoo, E., McCracken, J. T., Thompson, P. M., & Lawrence, K. E. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10781992 · Cited by 7
“Each subject’s dMRI scan was denoised using the Marcenko-Pastur principal component analysis (PCA) algorithm in DIPY to enhance the signal-to-noise ratio [26].”
Method
Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes
Manzano-Patron, J. P., Deistler, M., Schröder, C., Kypraios, T., Gonçalves, P. J., Macke, J. H., & Sotiropoulos, S. N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103580 · Cited by 4
“Both signal representations were obtained using Dipy implementations [70].”
Cognitive impairment and vulnerability of cholinergic brain network in the Alzheimer’s continuum: free-water imaging based on diffusion tensor imaging
Zhao, S., Nie, Y., Wen, L., Qin, X., Huang, L., Chu, C., & Qu, M. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1587702 · Cited by 5
“Utilizing the regularized two-tensor model and the Diffusion Imaging in Python (Dipy) software package, fiber water (FW) images were derived from the preprocessed diffusion-weighted imaging data 2 (Garyfallidis et al., 2014).”
Effective Brain Connectivity Through a Constrained Autoregressive Model
Crimi, A., Dodero, L., Murino, V., & Sona, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_17 · Cited by 5
Alternative diffusion anisotropy measures for the investigation of white matter alterations in 22q11.2 deletion syndrome
Villalón-Reina, J. E., Forsyth, J. K., Kushan, L., Vajdi, A., Jalbrzikowski, M., Antshel, K. M., Fremont, W. P., Campbell, L., McCabe, K., Durdle, C. A., et al. (2018) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2513788 · Cited by 4
“With the dMRI in the ENIGMA-DTI template space we used a nonlinear fitting and outlier detection for robust estimation of the diffusion tensor [21] by using the DIPY package [22].”
Method
Crystallinity characterization of white matter in the human brain
Teich, E., Cieslak, M., Giesbrecht, B., Vettel, J., Grafton, S. T., Satterthwaite, T., & Bassett, D. (2020) · New Journal of Physics · doi:10.1088/1367-2630/ac1286 · Cited by 5
Optimized Diffusion Imaging for Brain Structural Connectome Analysis
Consagra, W., Venkataraman, A., & Zhang, Z. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3156868 · Cited by 5
Accelerating Geometry-Based Spherical Harmonics Glyphs Rendering for dMRI Using Modern OpenGL
Poirier, C., Descoteaux, M., & Gilet, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87615-9_13 · Cited by 5
Self-citation
Structural Connectivity of Human Inferior Colliculus Subdivisions Using in vivo and post mortem Diffusion MRI Tractography
Sitek, K. R., Calabrese, E., Johnson, G. A., Ghosh, S., & Chandrasekaran, B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.751595 · Cited by 7
“Streamlines were generated using DIPY’s EuDX approach.”
Method
Non-invasive in vivo MRI detects long-term microstructural brain alterations related to learning and memory impairments in a model of inflammation-induced white matter injury
Pierre, W. C., Zhang, E., Londoño, I., Leener, B. D., Lesage, F., & Lodygensky, G. A. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2022.113884 · Cited by 5
Blood pressure changes impact corticospinal integrity and downstream gait and balance control
Coon, E. A., Castillo, A. M., Lesnick, T. G., Raghavan, S., Mielke, M. M., Reid, R. I., Windham, B. G., Petersen, R. C., Jack, C. R., Graff‐Radford, J., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.08.011 · Cited by 5
Deep constrained spherical deconvolution for robust harmonization
Yao, T., Rheault, F., Cai, L. Y., Nath, V., Asad, Z., Newlin, N. R., Cui, C., Deng, R., Ramadass, K., Schilling, K. G., et al. (2023) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2654398 · Cited by 5
“22 For the spherical harmonics coeifficients c mk , k is the order, m is the degree.”
BackgroundMethod
Dmipy, A Diffusion Microstructure Imaging Toolbox in Python to Improve Research Reproducibility
Alimi, A., Fick, R., Wassermann, D., & Rachid Deriche, B. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_5 · Cited by 5
“It is diﬀerent from dipy [33] because Dmipy is a “model generator”.”
BackgroundMethod
Automatic comprehensive aspects reports in clinical acute stroke MRIs
Liu, C.-F., Li, J., Kim, G., Miller, M. I., Hillis, A. E., & Faria, A. V. (2023) · Scientific Reports · doi:10.1038/s41598-023-30242-6 · Cited by 5
Increased BOLD activation in the left parahippocampal cortex after 1 year of medical school
Bernier, M., Gauvreau, C., Theriault, D., Madrolle, S., Lepage, J., & Whittingstall, K. (2015) · Neuroreport · doi:10.1097/wnr.0000000000000497 · Cited by 5
Semi-Supervised Brain Lesion Segmentation Using Training Images with and Without Lesions
Liu, C., Pang, F., Liu, Y., Liang, K., Li, X., Zeng, X., & Ye, C. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098565 · Cited by 5
“The DWI scans were skull-stripped using Dipy [12], and the intensities were normalized so that brain tissue had zero mean and unit variance.”
Method
The role of the structural connectome in literacy and numeracy development in childrenPreprint
Bathelt, J., Gathercole, S. E., Butterfield, S., & Astle, D. E. (2017) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/jk6yb · Cited by 8
Fitting IVIM with Variable Projection and Simplicial OptimizationPreprint
Fadnavis, S., Farooq, H., Afzali, M., Lenglet, C., Georgiou, T. T., Cheng, H., Newman, S. D., Ahmed, S., Henriques, R. N., Peterson, E., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00095 · Cited by 5
“…quality of estimates amd to ensure the stability and robustness of fitting, we conducted experiments with both simulated (with and without noise) and real data (Peterson, 2016) We simulated the signal from the IVIM model in DIPY (Garyfallidis et al., 2014) and fitted it with the proposed framework.”
MethodInfluentialSelf-citation
Bridging the Gap: From Neuroanatomical Knowledge to Tractography of Brain PathwaysPreprint
Gallardo, G., Wassermann, D., & Anwander, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.01.232116 · Cited by 5
“These masks can be readily used to inform any tractography algorithm (Jenkinson et al., 2012; Garyfallidis et al., 2014; Tournier et al., 2019).”
Background
Sparse multiway decomposition for analysis and modeling of diffusion imaging and tractographyPreprint
Caiafa, C. F., & Pestilli, F. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.07170 · Cited by 5
Advanced imaging reveals enhanced malignancy in glioblastomas involving the subventricular zone: evidence of increased infiltrative growth and perfusion
Griessmair, M., Schramm, S., Ziegenfeuter, J., Canisius, J., Jung, K., Delbridge, C., Schmidt‐Graf, F., Mitsdoerffer, M., Zimmer, C., Meyer, B., et al. (2024) · Journal of Neuro-Oncology · doi:10.1007/s11060-024-04849-2 · Cited by 4
“Resulting segmentations were quality-checked by M.G. ADC and FA maps were derived from DTI using the dipy library [ 25 ].”
Effects of Aging, Estimated Fitness, and Cerebrovascular Status on White Matter Microstructural Health
Clements, G. M., Camacho, P., Bowie, D. C., Low, K. A., Sutton, B. P., Gratton, G., & Fabiani, M. (2025) · Human Brain Mapping · doi:10.1002/hbm.70168 · Cited by 6
Characterization of brain microstructural changes in children with infantile vitamin B12 deficiency using diffusion tensor imaging
Vyas, S., Kumar, P., Salan, T., Sankhyan, N., Dash, S., Srikumar, S., Ahuja, C. K., Govind, V., & Singh, P. (2025) · Neuroradiology · doi:10.1007/s00234-025-03831-7 · Cited by 4
“The Dipy library [10]was then used for diffusion tensor fitting and reconstruction of DTI maps: frac - tional anisotropy (FA), mean diffusivity (MD), axial diffu - sivity (AD), and radial diffusivity (RD).”
Deterministic versus Probabilistic Tractography: Impact on White Matter Bundle Shape
Shuai, Y., Chandio, B. Q., Feng, Y., Villalón‐Reina, J. E., Nir, T. M., Ching, C. R. K., Gari, I. B., Thomopoulos, S. I., Alibrando, J. D., John, J. P., et al. (2025) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim67325.2025.11283349 · Cited by 4
Diffusion Tensor MRI and Spherical‐Deconvolution‐Based Tractography on an Ultra‐Low Field Portable MRI System
Gholam, J., Schmid, P., Ametepe, J. M., Plumley, A., Beltrachini, L., Padormo, F., Teixeira, R. P. A. G., O’Halloran, R., Petkov, K., Engel, K., et al. (2026) · Human Brain Mapping · doi:10.1002/hbm.70454 · Cited by 4
“Diffusion tensors were fitted to the observed data using the RESTORE method [ Chang2005 , Chung2006 , Yendiki2014 ] implemented in DIPY Version 1.11 [ Garyfallidis2014 ].”
Influential
Multi-Architecture Deep Learning for Early Alzheimer’s Detection in MRI: Slice- and Scan-Level Analysis
Bricaud, I., & Masala, G. L. (2026) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph23030322 · Cited by 5
“We performed tissue segmentation using the Hidden Markov Random Field (HMRF) algorithm implemented in the DIPY library [19,20].”
Choroid plexus–glymphatic axis disruption in Alzheimer's disease: Cerebrospinal fluid expansion as a mediator of metabolic dysfunction and cognitive decline
Lv, M., Wang, Q., Yu, G., Chen, J., Zhou, X., Chen, Z., Li, R., Huang, S., Liang, Y., Que, Y., et al. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261426578 · Cited by 4
Noninvasive- and invasive mapping reveals similar language network centralities – A function-based connectome analysis
Ille, S., Zhang, H., Stassen, N., Schwendner, M. J., Schröder, A., Wiestler, B., Meyer, B., & Krieg, S. M. (2024) · Cortex · doi:10.1016/j.cortex.2024.01.013 · Cited by 4
“Next, the rim of nTMS-and DCS-POS targets were enlarged to 5 mm and regarded as start regions for seed-based brain tractography through the Python library DIPY (Version 1.2.0, https://dipy.org) (Garyfallidis et al., 2014) (Fig.”
Influential
Comparison of structural connectomes for modeling deep brain stimulation pathway activation
Mehta, K., Noecker, A. M., & McIntyre, C. C. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121211 · Cited by 4
“Dice overlap was calculated by converting the streamlines into their voxel representation (i.e. a binar-ized mapping of all voxels that contain a streamline) which was done using ‘density_map() ’ function of the DIPY Python library (Garyfallidis et al., 2014).”
Exploring IVIM‐DKI and DKI for Assessing Microvascular and Microstructural Changes After Traumatic Brain Injury
Harritz, T., Hansen, B., Zhang, B., & Xue, R. (2025) · NMR in Biomedicine · doi:10.1002/nbm.70110 · Cited by 4
“The combined IVIM-DKI data was corrected for between-volumes motion in DiPy [33] by registering each volume to a reference b0-image from the corresponding animal [36].”
Influential
Mapping Alzheimer’s disease pathology using free water through integrated analysis of plasma biomarkers, microstructural DTI metrics, and macrostructural MRI measures
Kim, B. H., Shin, D., Kang, S. H., Jang, H., Yun, J., Park, Y., Zetterberg, H., Blennow, K., González‐Ortiz, F., Ashton, N. J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-14200-y · Cited by 4
“The FW and FW-corrected maps (FA t and MD t ) were then calculated using the open-source software package Diffusion Imaging in Python (DIPY, https://dipy.org/) 37 .”
A Generalized SMT-Based Framework for Diffusion MRI Microstructural Model Estimation
Zucchelli, M., Descoteaux, M., & Menegaz, G. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_4 · Cited by 4
Self-citation
Bayesian Tractography Using Geometric Shape Priors
Dong, X., Zhang, Z., & Srivastava, A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00483 · Cited by 4
“To estimate a diffusion tensor at each voxel, we use the open source software Dipy (Garyfallidis et al., 2014).”
Method
Mapping the impact of non-linear gradient fields on diffusion MRI tensor estimation
Kanakaraj, P., Hansen, C. B., Rheault, F., Cai, L. Y., Ramadass, K., Rogers, B. P., Schilling, K. G., & Landman, B. A. (2022) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2611900 · Cited by 4
“Tensors are simulated at different orientations that are uniformly spaced with one hundred 𝜃 ∈ [0, 𝜋] and hundred 𝜑 ∈ [0, 2𝜋] over a spere using dipy that implemented the electrostatic repulsion from Jones1999 [13].”
Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence
Bogusz, F., Pieciak, T., Afzali, M., & Pizzolato, M. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.05.007 · Cited by 4
“In this experiment, the ODFs were estimated for 11554 equally spaced points on a unit sphere generated with the repulsion algorithm using Diffusion Imaging in Python (DIPY) package (Jones et al., 1999; Garyfallidis et al., 2014).”
Microstructural and Microvascular Alterations in Psychotic Spectrum Disorders: A Three-Compartment Intravoxel Incoherent Imaging and Free Water Model
McKenna, F., Gupta, P. K., Sui, Y. V., Bertisch, H. C., Gonen, O., GOFF, D. C., & Lazar, M. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad019 · Cited by 4
Likelihood-free posterior estimation and uncertainty quantification for diffusion MRI models
Karimi, H. S., Pal, A., Ning, L., & Yogesh Rathi, A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00088 · Cited by 4
“For comparison with existing techniques, we calculate the number of peaks using the state- of- the-art method called Constrained Spherical Deconvolution (CSD) ( Tournier et al., 2007) as implemented in the DIPY package ( Garyfallidis et al., 2014).”
Method
Dynamic Perviousness Predicts Revascularization Success in Acute Ischemic Stroke
Bertalan, G., Duparc, R., Krepuska, M., Toth, D., Madjidyar, J., Thurner, P., Schubert, T. B., & Kulcsár, Z. (2024) · Diagnostics · doi:10.3390/diagnostics14050535 · Cited by 4
Complex relationships of socioeconomic status with vascular and Alzheimer’s pathways on cognition
Shir, D., Graff‐Radford, J., Fought, A. J., Lesnick, T. G., Przybelski, S. A., Vassilaki, M. E., Lowe, V. J., Knopman, D. S., Machulda, M. M., Petersen, R. C., et al. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103634 · Cited by 6
“Diffusion tensors were estimated using nonlinear least squares fitting and used to calculate Fractional Anisotropy (FA) and Mean Diffusivity (MD) images in dipy (Garyfallidis et al., 2014).”
Method
Optimal navigability of weighted human brain connectomes in physical space
Barjuan, L., Soriano, J., & Serrano, M. Á. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120703 · Cited by 4
“The already preprocessed (Glasser et al., 2013) DWI data of each subject was employed to fit a second-order tensor for each voxel and its different voxelwise scalar maps by using Dipy (Garyfallidis et al., 2014).”

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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