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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Single‐shell NODDI using dictionary‐learner‐estimated isotropic volume fraction
Faiyaz, A., Doyley, M. M., Schifitto, G., Zhong, J., & Uddin, M. N. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4628 · Cited by 19
In vivo restricted diffusion imaging (RDI) is sensitive to differences in axonal density in typical children and adults
Garic, D., Yeh, F.-C. F., Graziano, P. A., & Dick, A. S. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02364-y · Cited by 18
CTtrack: A CNN+Transformer-based framework for fiber orientation estimation & tractography
Hosseini, S. M., Hassanpour, M., Masoudnia, S., Iraji, S., Raminfard, S., & Nazem-Zadeh, M.-R. (2022) · Neuroscience Informatics · doi:10.1016/j.neuri.2022.100099 · Cited by 19
Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow‐compensated diffusion images in the liver—A quantitative evaluation of postprocessing algorithms
Führes, T., Saake, M., Lorenz, J., Seuß, H., Stemmer, A., Benkert, T., Uder, M., & Laun, F. B. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29427 · Cited by 17
Intense long‐term training impairs brain health compared with moderate exercise: Experimental evidence and mechanisms
Sangüesa, G., Batlle, M., Muñoz‐Moreno, E., Sòria, G., Alcarraz, A., Rubies, C., Sitjà‐Roqueta, L., Solana, E., Martínez‐Heras, E., Meza‐Ramos, A., et al. (2022) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14912 · Cited by 16
Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI
Pizzolato, M., Canales-Rodríguez, E. J., Andersson, M., & Dyrby, T. B. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102767 · Cited by 18
Multi-planar 2.5D U-Net for image quality enhancement of dental cone-beam CT
Ryu, K., Lee, C., Han, Y., Pang, S., Kim, Y. H., Choi, C., Jang, I., & Han, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285608 · Cited by 20
FD‐Net: An unsupervised deep forward‐distortion model for susceptibility artifact correction in EPI
Alkilani, A. Z., Çukur, T., & Sarıtaş, E. Ü. (2023) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29851 · Cited by 17
“55 For all methods, hyperparameters were chosen empirically to maximize PSNR and SSIM over the four subjects reserved as validation data.”
Method
Investigation of perfusion impairment in degenerative cervical myelopathy beyond the site of cord compression
Lebret, A., Lévy, S., Pfender, N., Farshad, M., Altorfer, F. C. S., Callot, V., Curt, A. E. P., Freund, P., & Seif, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-49896-3 · Cited by 17
Convolutional neural network-based classification of glaucoma using optic radiation tissue properties
Kruper, J., Richie-Halford, A., Benson, N. C., Caffarra, S., Owen, J. P., Wu, Y., Egan, C. A., Lee, A., Lee, C. S., Yeatman, J. D., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00496-w · Cited by 16
“Tissue properties were calculated using the diffusional kurtosis model implemented in DIPY 18,37 .”
Self-citation
Tutorial: a guide to diffusion MRI and structural connectomics
Shamir, I., & Assaf, Y. (2024) · Nature Protocols · doi:10.1038/s41596-024-01052-5 · Cited by 19
Merged Group Tractography Evaluation with Selective Automated Group Integrated Tractography
Chen, D. Q., zhong, J., Hayes, D. J., Behan, B., Walker, M., Hung, P. S.-P., & Hodaie, M. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00096 · Cited by 17
“We have previously studied fornix subregion anatomies extensively (Chen et al., 2015b), and it is a popular anatomy to showcase the ability of new tractography algorithms due to the curvature of the forniceal crura (Garyfallidis et al., 2014).”
Background
Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach
Gallardo, G., Wells, W. M., Rachid Deriche, B., & Wassermann, D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.070 · Cited by 17
“For each subject, we ﬁtted a CSD model (Tournier et al., 2004) to their diﬀusion data using Dipy (version 0.11) (Garyfallidis et al., 2014) and created 5000 streamlines per seed-voxel using the implementation of probabilistic tractography in Dipy.”
MethodInfluential
Multiuser virtual reality environment for visualising neuroimaging data
Shattuck, D. W. (2018) · Healthcare Technology Letters · doi:10.1049/htl.2018.5077 · Cited by 17
Effects of simvastatin on white matter integrity in healthy middle‐aged adults
Vogt, N. M., Hunt, J. F. V., Ma, Y., Hulle, C. A. V., Adluru, N., Chappell, R. J., Lazar, K. K., Jacobson, L. E., Austin, B. P., Asthana, S., et al. (2021) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51421 · Cited by 17
Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI
Ren, M., Kim, H., Dey, N., & Gerig, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_50 · Cited by 17
Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves
Renauld, E., Descoteaux, M., Bernier, M., Garyfallidis, E., & Whittingstall, K. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156436 · Cited by 15
“T1 images were denoised using non-local means (NLM) [60] (Dipy, [61]) and skull-stripped using FSL [62].”
MethodSelf-citation
The diffusion-simulated connectivity (DiSCo) dataset
Rafael‐Patiño, J., Girard, G., Truffet, R., Pizzolato, M., Caruyer, E., & Thiran, J. (2021) · Data in Brief · doi:10.1016/j.dib.2021.107429 · Cited by 17
Brain glymphatic fluid mapping in Alzheimer’s disease: a human MRI and PET study
Zhou, L., Nguyen, T. D., Chiang, G. C.-Y., Keil, S. A., Wang, X. H., Hu, T., Lan, H., Xi, K., Costa, A. P. F., Tanzi, E. B., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf200 · Cited by 17
“7,8,42 The DTI-FW was converted to FW mapping in the same way as described for pCSF mapping above and coregistered to FS T1w space using the transform between FA to FS T1w.”
Jointly estimating parametric maps of multiple diffusion models from undersampled q‐space data: A comparison of three deep learning approaches
HashemizadehKolowri, S., Chen, R., Adluru, G., & DiBella, E. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29162 · Cited by 16
Texture analysis in brain T2 and diffusion MRI differentiates histology-verified grey and white matter pathology types in multiple sclerosis
Hosseinpour, Z., Jonkman, L. E., Oladosu, O., Pridham, G., Pike, G. B., Inglese, M., Geurts, J. J. G., & Zhang, Y. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109671 · Cited by 17
Preoperative language tract integrity is a limiting factor in recovery from aphasia after glioma surgery
Prasse, G., Meyer, H., Scherlach, C., Maybaum, J., Hoffmann, A., Kasper, J., Fehrenbach, M. K., Wilhelmy, F., Meixensberger, J., Hoffmann, K., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103310 · Cited by 16
Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge
Girard, G., Rafael‐Patiño, J., Truffet, R., Aydogan, D. B., Adluru, N., Nair, V. A., Prabhakaran, V., Bendlin, B. B., Alexander, A. L., Bosticardo, S., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120231 · Cited by 19
Self-citation
Extracellular Free Water Elevations are Associated with Brain Volume and Maternal Cytokine Response in a Longitudinal Nonhuman Primate Maternal Immune Activation Model
Lesh, T., Iosif, A.-M., Tanase, C., Vlasova, R., Ryan, A. M., Bennett, J. L., Hogrefe, C. E., Maddock, R., Geschwind, D., van de Water, J. A., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02213-w · Cited by 17
“The Dipy diffusion imaging library [57] and included free water elimination model [58] were used to calculate all diffusion metrics.”
Longitudinal evolution of diffusion metrics after left hemisphere ischaemic stroke
Boucher, J., Marcotte, K., Bedetti, C., Houzé, B., Descoteaux, M., Brisebois, A., García, A. O., Rochon, E. A., Léonard, C., Désautels, A., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad313 · Cited by 21
“Second, diffusion weighted imaging (DWI)-weighted images were pre-processed through several processing steps, including noise correction with the Marchenko–Pastur principal component analysis method using Mrtrix3, 31 correction for subject movement, eddy-currents and susceptibility-induced distortions with AP-PA image…”
MethodSelf-citation
Sex-specific interactions between stress axis and redox balance are associated with internalizing symptoms and brain white matter microstructure in adolescents
Schilliger, Z., Alemán‐Gómez, Y., Smith, M. M., Celen, Z., Meuleman, B., Binz, P., Steullet, P., Kim, Q., Conus, P., Merglen, A., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-023-02728-4 · Cited by 20
“Dipy (v.1.5.0) [60] was applied over the corrected DWIs to ﬁ t both second order tensors and intravoxel orientation distribution functions (ODF) via the Simple Harmonic Oscillator-based Reconstruction and Estimation method (SHORE) [61].”
MethodInfluential
BundleSeg: A Versatile, Reliable and Reproducible Approach to White Matter Bundle Segmentation
St‐Onge, E., Schilling, K. G., & Rheault, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47292-3_5 · Cited by 17
Alzheimer’s disease diagnosis using deep learning techniques: datasets, challenges, research gaps and future directions
Nazir, A., Assad, A., Hussain, A. B., & Singh, M. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02441-5 · Cited by 20
A time‐efficient acquisition protocol for multipurpose diffusion‐weighted microstructural imaging at 7 Tesla
Sepehrband, F., O’Brien, K., & Barth, M. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26608 · Cited by 16
“Dipy software (http://nipy.org/dipy/) version 0.8.0 was used to perform LiFE (68).”
MethodInfluential
Model‐based denoising in diffusion‐weighted imaging using generalized spherical deconvolution
Sperl, J. I., Sprenger, T., Tan, E. T., Menzel, M. I., Hardy, C. J., & Marinelli, L. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26626 · Cited by 16
“TikReg was applied to SHORE using the Dipyimplementation (44), to SpherRidge using simple MoorePenrose inversion, and to genSD using quadratic programming to incorporate the nonnegativity constraints.”
Method
Stability metrics for optic radiation tractography: Towards damage prediction after resective surgery
Meesters, S., Ossenblok, P., Wagner, L., Schijns, O. E. M. G., Boon, P., Florack, L. M. J., Vilanova, A., & Duits, R. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.05.029 · Cited by 16
Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study
Timmermans, C., Smeets, D., Verheyden, J., Terzopoulos, V., Anania, V., Parizel, P. M., & Maas, A. I. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26333 · Cited by 16
Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and Results
Ning, L., Bonet-Carné, E., Grussu, F., Sepehrband, F., Kaden, E., Veraart, J., Blumberg, S. B., Khoo, C. S., Palombo, M., Coll‐Font, J., et al. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_18 · Cited by 16
White matter degeneration profile in the cognitive cortico‐subcortical tracts in Parkinson's disease
Hanganu, A., Houde, J., Fonov, V., Degroot, C., Mejia‐Constain, B., Lafontaine, A., Soland, V., Chouinard, S., Collins, D. L., Descoteaux, M., et al. (2018) · Movement Disorders · doi:10.1002/mds.27364 · Cited by 15
“Various statistics were computed along those bundles, including axial, radial, and mean diffusivity (AD, RD, MD), fractional anisotropy (FA), apparent fiber density (AFD), track volume (TV), and track count (number of reconstructed streamlines [RS]) using Dipy.60 AFD provides an estimate of the local voxel-wise fiber…”
MethodInfluentialSelf-citation
Progressive axonopathy when oligodendrocytes lack the myelin protein CMTM5
Buscham, T. J., Eichel-Vogel, M. A., Steyer, A. M., Jahn, O., Strenzke, N., Dardawal, R., Memhave, T. R., Siems, S. B., Müller, C., Meschkat, M., et al. (2022) · eLife · doi:10.7554/elife.75523 · Cited by 18
Glymphatic dysfunction associated with cortisol dysregulation in major depressive disorder
Chen, S., Xu, Z., Guo, Z., Lin, S., Zhang, H., Liang, Q., Chen, Q., Zhang, Y., Peng, B., Zhao, Y., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03486-1 · Cited by 15
Portable Learning Environments for Hands-On Computational Instruction
Holdgraf, C., Culich, A., Rokem, A., Deniz, F., Alegro, M., & Ushizima, D. (2017) · Practice and Experience in Advanced Research Computing · doi:10.1145/3093338.3093370 · Cited by 15
Self-citation
An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography
Jordan, K., Morin, O., Wahl, M. T., Amirbekian, B., Chapman, C. H., Owen, J. P., Mukherjee, P., Braunstein, S. E., & Henry, R. G. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00810 · Cited by 15
“The code to generate the WMPL maps is open-source and freely available on Github as part of the Diffusion Imaging in Python package (29).”
MethodInfluentialSelf-citation
Automatic Segmentation of White Matter Tracts Using Multiple Brain MRI Sequences
Nelkenbaum, I., Tsarfaty, G., Kiryati, N., Konen, E., & Mayer, A. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098454 · Cited by 15
“Raw DWI data was processed with Dipy [19] python module, and PDD [20], consisting of 3 channels per voxel, was generated.”
Method
White matter microstructure predicts individual differences in infant fear (But not anger and sadness)
Planalp, E. M., Dowe, K. N., Alexander, A. L., Goldsmith, H. H., Davidson, R. J., & Dean, D. (2022) · Developmental Science · doi:10.1111/desc.13340 · Cited by 16
“DTI Diffusion tensors were estimated at each voxel using the robust estimation of tensors by an outlier rejection (RESTORE; Chang et al., 2005) algorithm as part of the Diffusion Imaging in Python open source software package (DIPY; (Garyfallidis et al., 2014).”
Method
The importance of the dorsal branch of the arcuate fasciculus in phonological working memory
Barbeau, É. B., Kousaie, S., Brass, K., Descoteaux, M., Petrides, M., & Klein, D. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad226 · Cited by 15
Self-citation
Investigating female versus male differences in white matter neuroplasticity associated with complex visuo-motor learning
Kirby, E., Andrushko, J., Rinat, S., D'Arcy, R. C. N., & Boyd, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-56453-z · Cited by 15
Comparison of distances for supervised segmentation of white matter tractography
Olivetti, E., Bertò, G., Gori, P., Sharmin, N., & Avesani, P. (2017) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2017.7981502 · Cited by 15
“For each bundle and distance function, we observed a standard deviation 1 http://nipy.org/dipy, [22]. of DSC of approximately 0 .”
Method
Classifying Phenotypes Based on the Community Structure of Human Brain Networks
Kurmukov, A. G., Ananyeva, M., Dodonova, Y., Gutman, B. A., Faskowitz, J., Jahanshad, N., Thompson, P. M., & Евгеньевич, Ж. Л. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67675-3_1 · Cited by 14
q-Space Learning with Synthesized Training Data
Ye, C., Cui, Y., & Li, X. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_10 · Cited by 15
FURY: advanced scientific visualization
Garyfallidis, E., Koudoro, S., Guaje, J., Côté, M.-A., Biswas, S., Reagan, D., Anousheh, N., Silva, F. N., Fox, G., & Contributors, F. (2021) · The Journal of Open Source Software · doi:10.21105/joss.03384 · Cited by 15
“…and allows to reprogram the low-level shaders for the creation of stunning effects (see Figure 2) not available in VTK. Historically, FURY had also a different path than these libraries as it was originally created for heavy-duty medical visualization purposes for DIPY (Garyfallidis et al., 2014).”
BackgroundSelf-citation
Visualizing cellularity and angiogenesis in newly-diagnosed glioblastoma with diffusion and perfusion MRI and FET-PET imaging
Liesche-Starnecker, F., Prokop, G., Yakushev, I., Preibisch, C., Delbridge, C., Meyer, H. S., Aftahy, K., Barz, M., Meyer, B., Zimmer, C., et al. (2021) · EJNMMI Research · doi:10.1186/s13550-021-00817-3 · Cited by 15
“DTI data were analyzed using the open-source dipy framework [12].”
Method
Gadolinium‐free assessment of synovitis using diffusion tensor imaging
Sandford, H. J. C., Mackay, J., Watkins, L., Gold, G. E., Kogan, F., & Mazzoli, V. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4614 · Cited by 15
“Tensor fitting of the diffusion images was performed using the Dipy library.(30) Voxelwise maps of MD and FA were created (Figure 2C and 2D) by using the three eigen-values of the diffusion tensor (λ1, λ2, λ3) in the following formulas:”
Method
Elucidating the structural–functional connectome of language in glioma‐induced aphasia using nTMS and DTI
Zhang, H., Ille, S., Sogerer, L., Schwendner, M. J., Schröder, A., Meyer, B., Wiestler, B., & Krieg, S. M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25757 · Cited by 15
Brain‐age estimation accuracy is significantly increased using multishell free‐water reconstruction
Nemmi, F., Levardon, M., & Péran, P. (2022) · Human Brain Mapping · doi:10.1002/hbm.25792 · Cited by 15
Evaluating reproducibility and subject-specificity of microstructure-informed connectivity
Koch, P., Girard, G., Brügger, J., Cadic‐Melchior, A., Beanato, E., Park, C.-H., Morishita, T., Wessel, M. J., Pizzolato, M., Canales-Rodríguez, E. J., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119356 · Cited by 16
“For each dataset, 5 million streamlines were generted, from which all streamlines with both endpoints in the individual ortical or subcortical brain masks were selected using the DIPY software ackage ( Garyfallidis et al., 2014 ).”
Method
Identifying vulnerable brain networks associated with Alzheimer’s disease risk
Mahzarnia, A., Stout, J. A., Anderson, R. J., Moon, H. S., Han, Z. Y., Beck, K., Browndyke, J. N., Dunson, D. B., Johnson, K. G., O’Brien, R., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac419 · Cited by 14
Blockmodels for connectome analysis
Moyer, D., Gutman, B. A., Prasad, G., Faskowitz, J., Steeg, G. V., & Thompson, P. M. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2211519 · Cited by 15

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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