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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“[25], including affine registration of diffusion-weighted images to the b0 image and appropriate”
Background
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Method
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“QSIPrep is an integrative platform for pre-processing dMRI and structuralimaging data, aswellasreconstructing white matter structural connectome 75 by incorporating tools from FSL 76 , DSI Studio (https://dsi-studio.labsolver.org/), DIPY 77 , ANTs (https://stnava.github.io/ANTs/), and MRtrix3 78 .”
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Method
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Method
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Self-citation
Wallerian Degeneration Assessed by Multi-Modal Magnetic Resonance Imaging of Cervical Spinal Cord Is Associated With Neurological Impairment After Spinal Cord Injury
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“31 Next, the DTI maps were registered to T2*-weighted images following a non-linear transformation (BSplineSyn algorithm).”
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Fouto, A. R., Henriques, R. N., Golub, M., Freitas, A. C., Ruiz‐Tagle, A., Esteves, I., Gil‐Gouveia, R., da Silva, N. A., Vilela, P. F., Figueiredo, P., et al. (2024) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-024-01153-y · Cited by 7
“These simulations are implemented in Python using tools from diffusion imaging in Python (DIPY) package [30, 32].”
Method
Geometric deep learning for diffusion MRI signal reconstruction with continuous samplings (DISCUS)
Ewert, C., Kügler, D., Stirnberg, R., Koch, A., Yendiki, A., & Reuter, M. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00121 · Cited by 9
“We fit the DTI model with FSL dtifit ( Smith et al., 2004), the DKI and MAP-MRI models with Dipy ( Garyfallidis et al., 2014), and the NODDI model with the Microstructure Diffusion Toolbox ( Harms & Roebroeck, 2018; Harms et al., 2017).”
Method
Microstructural changes of the white matter in systemic lupus erythematosus patients without neuropsychiatric symptoms: a multi-shell diffusion imaging study
Hu, W., Qiu, Z., Huang, Q., Lin, Y., Mo, J., Wang, L., Wang, J., Deng, K., Feng, Y., Zhang, X., et al. (2024) · Arthritis Research & Therapy · doi:10.1186/s13075-024-03344-3 · Cited by 7
“Then, preprocessed multi-shell dMRI data were employed to assess DKI metrics (FA, MD, AD, RD, MK, AK and RK) with DIPY (https://dipy.org/) [18, 19] and NODDI metrics (NDI, ODI and VISO) with AMICO (https://github.com/ daducci/AMICO) [20].”
Method
Characterization of the orientation dependence of magnetization transfer measures in single and crossing‐fiber white matter
Karan, P., Edde, M., Gilbert, G., Baraković, M., Magon, S., & Descoteaux, M. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30195 · Cited by 7
“Tractoflow also generated the fODFs using constrained spherical deconvolution from the Python library DIPY, 32 and extracted the number of fiber orientation (NuFO), the fODF peaks and peak amplitudes (also called peak values) using the scil_compute_fodf_metrics.py script from the Python library Scilpy (https://github.…”
Self-citation
HAITCH: A framework for distortion and motion correction in fetal multi-shell diffusion-weighted MRI
Snoussi, H., Karimi, D., Afacan, O., Ütkür, M., & Gholipour, A. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00490 · Cited by 7
“It is important to note that this basis differs from the one available in the Dipy library [52], resulting in more number of basis functions (72 in Merlet & Deriche vs. 50 in Dipy for radial order 6).”
The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain
Mazur, W., & Krzyżak, A. T. (2024) · Scientific Reports · doi:10.1038/s41598-024-66076-z · Cited by 9
“Preprocessing was carried out in Python DIPY software 48 and encompassed denoising and next, Gibbs ringing removal 49,50 .”
Placental multimodal MRI prior to spontaneous preterm birth <32 weeks' gestation: An observational study
Hall, M., Suff, N., Slator, P. J., Rutherford, M., Shennan, A. H., Hutter, J. M., & Story, L. (2024) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.17901 · Cited by 7
“24,25 Two combined diffusion-relaxometry models were fit to the data: a simple T2*- ADC model (Equation 1) and a two-compartment T2*- IVIM model that differentiates fast and slow diffusion, taken to represent perfusing and diffusing placental blood respectively (Equation 2): Where TE = echo time;…”
Deep Medullary Vein Integrity and Relationships with Small Vessel Disease and Interstitial Diffusivity Measures in Patients with a Recent Small Subcortical Infarct
Brenlla, C., Sozzi, C., Girona, A., Muñoz‐Moreno, E., Laredo, C., Rodríguez-Vázquez, A., Amaro, S., Renú, A., Cubero, A. D.-M., Llull, L., et al. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8591 · Cited by 7
Rethinking MRI as a measurement device through modular and portable pipelines
Karakuzu, A., Blostein, N., Caron, A. V., Boré, A., Rheault, F., Descoteaux, M., & Stikov, N. (2025) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-025-01245-3 · Cited by 7
“The Tractoflow container is based on scilpy [109], dipy [110], mrtrix3 [111], ANTS [112], FSL [113] and freesurfer [114].”
Self-citation
Patch-Based DTI Grading: Application to Alzheimer’s Disease Classification
Initiative, A. D. N., Hett, K., Ta, V., Giraud, R., Mondino, M., Manjón, J. V., & Pierrick Coupé, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47118-1_10 · Cited by 7
A Multi-Tissue Global Estimation Framework for Asymmetric Fiber Orientation Distributions
Wu, Y., Feng, Y., Shen, D. G., & Yap, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_6 · Cited by 7
“Whole brain tractography was performed using deterministic and probabilistic algorithms [12] using the peak directions of the AFODFs and FODFs.”
Method
TractEM: Evaluation of protocols for deterministic tractography white matter atlas
Rheault, F., Bayrak, R. G., Wang, X., Schilling, K. G., Greer, J. M., Hansen, C. B., Kerley, C. I., Ramadass, K., Remedios, L. W., Blaber, J. A., et al. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.10.017 · Cited by 7
Volumetric segmentation of the corpus callosum: training a deep learning model on diffusion MRI
Rodrigues, J., Pinheiro, G. R., do Carmo, D. S., & Rittner, L. (2021) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2606233 · Cited by 7
Quantitative MR Markers in Non-Myelopathic Spinal Cord Compression: A Narrative Review
Valošek, J., Bednařík, P., Keřkovský, M., Hluštı́k, P., Bednařík, J., & Svátková, A. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11092301 · Cited by 6
Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor‐valued diffusion MRI
Karan, P., Reymbaut, A., Gilbert, G., & Descoteaux, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2022.102476 · Cited by 7
Self-citation
Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI
Hansen, C. B., Schilling, K. G., Rheault, F., Resnick, S. M., Shafer, A. T., Beason‐Held, L. L., & Landman, B. A. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.06.004 · Cited by 7
Artificial Intelligence–Assisted Evaluation of the Spatial Relationship between Brain Arteriovenous Malformations and the Corticospinal Tract to Predict Postsurgical Motor Defects
Jiao, Y., Zhang, J., Yang, X., Zhan, T., Wu, Z., Li, Y., Zhao, S., Li, H.-G., Weng, J., Huo, R., et al. (2022) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7735 · Cited by 7
Diffusivity Changes in Posterior Cortical Atrophy and Logopenic Progressive Aphasia: A Longitudinal Diffusion Tensor Imaging Study
Singh, N. A., Graff‐Radford, J., Machulda, M. M., Pham, N. T. T., Schwarz, C. G., Reid, R. I., Lowe, V. J., Petersen, R. C., Jack, C. R., Josephs, K. A., et al. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-221217 · Cited by 7
“Diffusion tensors were estimated using nonlinear least squares ﬁttingandusedtocalculatefractionalanisotropy(FA) and mean diffusivity (MD) images in dipy [30].”
Microstructural Abnormalities in the Contralateral Normal-appearing White Matter of Glioblastoma Patients Evaluated with Advanced Diffusion Imaging
Genç, B., Aslan, K., Özçağlayan, A., & İncesu, L. (2023) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2023-0054 · Cited by 7
Prediction of misfolded proteins spreading in Alzheimer’s disease using machine learning and spreading models
Gherardini, L., Zajdel, A., Pini, L., & Crimi, A. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad380 · Cited by 8
“For DWI, we performed brain extraction with BET2, proceeding with LPCA denoising and Gibbs correction from Dipy, then Eddy correction in FSL, and then co-registered to the previously registered T1-weighted image.”
Influential
Diffusion MRI is valuable in brainstem glioma genotyping with quantitative measurements of white matter tracts
Xiao, X., Yang, N., Gu, G., Wang, X., Jiang, Z., Li, T., Zhang, X., Ma, L., Zhang, P., Liao, H., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10377-w · Cited by 7
“…imaging was performed in the sagittal plane with the following parameters: repetition time (TR) = The FMRIB Software Library (FSL, version 6.0) [21, 22] was used to analyze DWI data, including (1) bet command: brain tissue extraction, (2) topup command: estimating and correcting susceptibility…”
Influential
Multi-Site Statistical Mapping of Along-Tract Microstructural Abnormalities in Bipolar Disorder with Diffusion MRI Tractometry
Nabulsi, L., Chandio, B. Q., McPhilemy, G., Martyn, F. M., Roberts, G. M. P., Hallahan, B. P., Dannlowski, U., Kircher, T. T. J., Haarman, B. C. M., Mitchell, P. B., et al. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim56729.2023.10373494 · Cited by 9
Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration
Stout, J. A., Mahzarnia, A., Dai, R., Anderson, R. J., Cousins, S. W., Zhuang, J., Lad, E. M., Whitaker, D., Madden, D. J., Potter, G. G., et al. (2024) · Biomedicines · doi:10.3390/biomedicines12010147 · Cited by 7
“The diffusion images were preprocessed using a pipeline [50], modified to incorporate Principal Component Analysis denoising [51,52] and to use BET [53] for brain masking.”
Method
A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI
Yao, T., Newlin, N. R., Kanakaraj, P., Nath, V., Cai, L. Y., Ramadass, K., Schilling, K. G., Landman, B. A., & Huo, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47292-3_2 · Cited by 7
“Ground-truth fODF maps were computed from MSMT-CSD using the DIPY library with the default settings [9].”
Method
Characterizing Peritumoral Tissue Using DTI-Based Free Water Elimination
Ismail, A. A. O., Parker, D., Hernández-Fernández, M., Brem, S. S., Alexander, S., Pasternak, O., Caruyer, E., & Verma, R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11723-8_12 · Cited by 7
SHORE‐based detection and imputation of dropout in diffusion MRI
Koch, A., Zhukov, A., Stöcker, T., Groeschel, S., & Schultz, T. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27893 · Cited by 7
Sleep spindles are resilient to extensive white matter deterioration
Sanchez, E., Arbour, C., El-Khatib, H., Marcotte, K., Blais, H., Baril, A., Bedetti, C., Descoteaux, M., Lina, J., Gilbert, D., et al. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa071 · Cited by 10
“For probabilistic tractography, DTI and high angular resolution diffusion imaging metrics were computed with the DIPY software (https://dipy.org/; last accessed 10 June 2020) (Garyfallidis et al., 2014).”
MethodSelf-citation
Increased Callosal Connectivity in Reeler Mice Revealed by Brain-Wide Input Mapping of VIP Neurons in Barrel Cortex
Hafner, G., Guy, J., Witte, M., Truschow, P., Rüppel, A., Sirmpilatze, N., Dadarwal, R., Boretius, S., & Staiger, J. F. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa280 · Cited by 10
“1994) was fitted to the preprocessed data and fractional anisotropy (FA) was derived (Garyfallidis et al. 2014).”
Method
Fully automated identification of brain abnormality from whole-body FDG-PET imaging using deep learning-based brain extraction and statistical parametric mapping
Whi, W., Choi, H., Paeng, J. C., Cheon, G. J., Kang, K. W., & Lee, D. S. (2021) · EJNMMI Physics · doi:10.1186/s40658-021-00424-0 · Cited by 7
“The spatial normalization was performed by symmetric normalization (SyN) with the cross-correlation loss function implanted in the DIPY package [34].”
Method
Assessing the feasibility of estimating axon diameter using diffusion models and machine learning
Fick, R., Sepasian, N., Pizzolato, M., Ianuș, A., & Rachid Deriche, B. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950631 · Cited by 7
“We estimate the AAD using Laplacian-regularized MAPMRI [9, 10] on the multi-shell acquisition, and 1D-SHORE [7] on the ∆ = 30ms segment of the AxCaliber one to have the same ∆ between the two.”
Method
Groupwise track filtering via iterative message passing and pruning
Xia, Y., & Shi, Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117147 · Cited by 7
MVC-Net: A Convolutional Neural Network Architecture for Manifold-Valued Images With ApplicationsPreprint
Bouza, J., Yang, C.-H., Vaillancourt, D. E., & Vemuri, B. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01234 · Cited by 7
“We estimate the diffusion tensor images from the segmented dMRIs of the sensorimotor tracts using the DiPy software (Garyfallidis et al., 2014).”
Method
Anatomical and functional organization of the interpeduncular nucleus in larval zebrafishPreprint
Wu, Y. K., Petrucco, L., & Portugues, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.09.617353 · Cited by 7
“These stacks were aligned using the dipy library [72], focusing on the channel displaying the Tg(16715:Gal4) signal.”
Method
A Python Toolbox for Representational Similarity AnalysisPreprint
van den Bosch, J. J. F., Golan, T., Peters, B., Taylor, J., Shahbazi, M., Lin, B., Charest, I., Diedrichsen, J., Kriegeskorte, N., Mur, M., et al. (2025) · eLife · doi:10.7554/elife.107828.1 · Cited by 8
Advanced mapping of the human white matter microstructure better separates elite sports participation
Caron, B., Bullock, D., Kitchell, L., McPherson, B., Kellar, D. A., Cheng, H., Newman, S. D., Port, N. L., & Pestilli, F. (2020) · doi:10.31234/osf.io/dxaqp · Cited by 9
Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles
Dong, X., Liu, B., Huang, W., Chen, H., Zhang, Y., Yao, Z., Shmuel, A., Yang, A., Dai, Z., Ma, G., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae238 · Cited by 6
Muscle diffusion tensor imaging in facioscapulohumeral muscular dystrophy
Barzaghi, L., Paoletti, M., Monforte, M., Bortolani, S., Bonizzoni, C., Feiweier, T., Bergsland, N., Santini, F., Deligianni, X., Tasca, G. A., et al. (2024) · Muscle & Nerve · doi:10.1002/mus.28179 · Cited by 6
“22 In more detail, the DWI dataset was de-noised using a Joint Rician Linear Minimum Mean Square Error (LMMSE) estimator, 23 registered to the b0 image and then corrected for eddy currents prior to the tensor calculation and estimation of fractional anisotropy (FA), mean diffusivity (MD), radial…”
Method
White matter microstructural integrity mediates associations between prenatal endocrine-disrupting chemicals exposure and intelligence in adolescents
Wang, S.-M., Wen, H., Huang, F., Sun, C.-W., Huang, C., & Wang, S. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103758 · Cited by 7
High resolution diffusion tensor imaging of the human cortex reveals non-linear trajectories over the healthy lifespan
Acosta‐Franco, J. A., Little, G. E., & Beaulieu, C. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.115 · Cited by 6
“7.0 for tensor model fitting and DTI map generation ( Garyfallidis et al., 2014).”
Influential
Optimizing cardiac diffusion tensor imaging in vivo: More directions or repetitions?
Coveney, S., Shelley, D., Foster, R. J., Afzali, M., Poenar, A., Sharrack, N., Plein, S., Dall’Armellina, E., Schneider, J. E., Nguyen, C. T., et al. (2025) · Journal of Cardiovascular Magnetic Resonance · doi:10.1016/j.jocmr.2025.101951 · Cited by 6
“The DTI signal representation was then fitted to the full dataset using robust weighted-least squares [41] implemented in DiPy [42], and the entire image series was predicted from this fit.”
Automatic determination of glymphatic flow with the DTI ‐ ALPS ‐index along the principal axis system in native imaging space corrects for head and fiber orientation
Ajouz, A., Jansen, O., Frohwein, L. J., Seehafer, S., Larsen, N., & Hövener, J. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70082 · Cited by 6
“The diffusion tensor and color-coded FA (RGB) maps (Dipy 26 ) were calculated on the masked diffusion data.”
Mapping along-tract commissural and association white matter microstructural differences in autistic children and young adults
Kim, G. S., Chandio, B. Q., Benavidez, S. M., Feng, Y., Thompson, P. M., & Lawrence, K. E. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf291 · Cited by 8
“Next, a step known as RecoBundles was used to extract 9 individual white matter tracts for each subjectusingthewholebraintractogramandmodelbundlesfrom the HCP-842 template included in BUAN (Garyfallidis et al. 2014; Garyfallidis et al.2018; Yeh et al.2018; Chandio et al.2020).”
Influential
Nigral volumetric and microstructural measures in individuals with scans without evidence of dopaminergic deficit
Langley, J. A., Hwang, K., Hu, X. P., & Huddleston, D. E. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1048945 · Cited by 6
Feasibility of in vivo multi-parametric quantitative magnetic resonance imaging of the healthy sciatic nerve with a unified signal readout protocol
Boonsuth, R., Battiston, M., Grussu, F., Samlidou, C. M., Calvi, A., Samson, R. S., Wheeler‐Kingshott, C. A. M. G., & Yiannakas, M. C. (2023) · Scientific Reports · doi:10.1038/s41598-023-33618-w · Cited by 6
Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus
Bhattacharya, D., Behrendt, F., Becker, B., Beyersdorff, D., Petersen, E. L., Petersen, M., Cheng, B., Eggert, D., Betz, C. S., Hoffmann, A. S., et al. (2023) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-023-02990-3 · Cited by 6
“To ensure consistency across all of the head and neck MRI scans in our study, we apply a process of rigid registration using Dipy library [20].”
Method
Disruption of corpus callosum microstructural integrity by diffusion MRI as a predictor of progression of cerebral microangiopathy
Добрынина, Л. А., Kremneva, E. I., Витальевна, Ш. К., Geints, A. A., Filatov, A. S., Trubitsyna, V. V., Bitsieva, E. T., Byrochkina, A. A., Akhmetshina, Y. I., Maximov, I. I., et al. (2023) · S S Korsakov Journal of Neurology and Psychiatry · doi:10.17116/jnevro202312311195 · Cited by 6
Multi-shell diffusion MRI of the fornix as a biomarker for cognition in Alzheimer's disease
Sakaie, K., Koenig, K. A., Lerner, A. J., Appleby, B. S., Ogrocki, P. K., Pillai, J. A., Rao, S. M., Leverenz, J. B., & Lowe, M. J. (2024) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.03.030 · Cited by 6
“…tool from FSL with the weighted least squares option [25], 2) a PVA-corrected diffusion tensor (DTIc), using an algorithm developed Hoy et al. [31] and implemented by Henriques et al. [32] with dipy [33] and 3) the NODDI model [22] implemented with the NODDI toolbox (http://mig.cs.ucl.ac.uk).”
Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring
Veldmann, M., Edwards, L., Pine, K., Ehses, P., Ferreira, M., Weiskopf, N., & Stoecker, T. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30180 · Cited by 6
“The axon radius was additionally estimated along the left corticospinal tract (left CST) by applying along-fibre quantification 42 using Dipy 43 and pyAFQ.”
NerveTracker: a Python-based software toolkit for visualizing and tracking groups of nerve fibers in serial block-face microscopy with ultraviolet surface excitation images
Kolluru, C., Joseph, N. M., Seckler, J. M., Fereidouni, F., Levenson, R. M., Shoffstall, A. J., Jenkins, M. W., & Wilson, D. L. (2024) · Journal of Biomedical Optics · doi:10.1117/1.jbo.29.7.076501 · Cited by 6
“36 However, these software tools are made to work with modality-specific datasets (e.g., diffusion-weighted images acquired at various gradient strengths).”
Method
Fully automated determination of robotic pedicle screw accuracy and precision utilizing computer vision algorithms
Groisser, B. N., Thakur, A., Hillstrom, H. J., Adhiyaman, A., Zucker, C. P., Du, J. Y., Cunningham, M. E., Hresko, M. T., Haddas, R., Blanco, J., et al. (2024) · Journal of Robotic Surgery · doi:10.1007/s11701-024-02001-w · Cited by 6
“The optimization step was performed using the diffusion imaging in Python package; segmentation labels were used as inclusive masks for the preoperative volumes while screws were excluded from the postoperative volumes by an intensity threshold [37].”
Method
Spherical Harmonics-Based Deep Learning Achieves Generalized and Accurate Diffusion Tensor Imaging
Chen, Y., Li, J., Lu, Q., Wu, Y., Liu, X., Gao, Y., Feng, Y., Zhang, Z., & Zhang, X. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3471769 · Cited by 6
“WLLS : Weighted linear least squares ﬁtting approach, a widely used DTI parameter estimation method, implemented with the DIPY [43] library.”
Susceptibility-induced internal gradients reveal axon morphology and cause anisotropic effects in the diffusion-weighted MRI signal
Winther, S., Lundell, H., Rafael‐Patiño, J., Andersson, M., Thiran, J., & Dyrby, T. B. (2024) · Scientific Reports · doi:10.1038/s41598-024-79043-5 · Cited by 6
“First, Gibbs ringing effects and noise were suppressed for all dMRI scans using gibbs_removal() and mppca()from the DIPY library 74 .”
Cardiac diffusion kurtosis imaging in the human heart in vivo using 300 mT/m gradients
Afzali, M., Coveney, S., Mueller, L., Jones, S. F., Fasano, F., Evans, J., Teh, I., Dall’Armellina, E., Szczepankiewicz, F., Jones, D. K., et al. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30626 · Cited by 6
“38,39 Real-valued diffusion-weighted images were first registered: For each slice, all low b-value images were registered to one user-specified low b-value image, and then all images were registered to the mean of the co-registered low b-value images.”
BundleWarp: Enhancing white matter tractometry and morphometry with precise neuronal mapping using streamline-based nonlinear registration
Chandio, B. Q., Olivetti, E., Romero-Bascones, D., Thomopoulos, S. I., Villalón‐Reina, J. E., Nir, T. M., Harezlak, J., Thompson, P. M., & Garyfallidis, E. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104114 · Cited by 7
Self-citation
Measures of Tractography Convergence
Moyer, D., Thompson, P. M., & Steeg, G. V. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_23 · Cited by 6
inCCsight: A software for exploration and visualization of DT-MRI data of the Corpus Callosum
Caldeira, T., Júlio, P. R., Appenzeller, S., & Rittner, L. (2021) · Computers & Graphics · doi:10.1016/j.cag.2021.07.012 · Cited by 7
Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord
Snoussi, H., Cohen‐Adad, J., Combès, B., Bannier, É., Tounekti, S., Kerbrat, A., Barillot, C., & Caruyer, E. (2023) · Brain and Behavior · doi:10.1002/brb3.3159 · Cited by 6
“These DTI metrics were computed using the DIPY library (Garyfallidis et al., 2014).”
Method
Structural connectivity and subcellular changes after antidepressant doses of ketamine and Ro 25-6981 in the rat: an MRI and immuno-labeling study
Pascual-Antón, R., Blasco‐Serra, A., Muñoz‐Moreno, E., Pilar-Cuéllar, F., Garro-Martínez, E., Florensa-Zanuy, E., López-Gil, X., Campa, V. M., Sòria, G., & Adell, A. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02354-0 · Cited by 6

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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