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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Lebret, A., Frese, S., Lévy, S., Curt, A. E. P., Callot, V., Freund, P., & Seif, M. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2024.0267 · Cited by 9
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Chandio, B. Q., Villalón‐Reina, J. E., Nir, T. M., Thomopoulos, S. I., Feng, Y., Benavidez, S. M., Jahanshad, N., Harezlak, J., Garyfallidis, E., & Thompson, P. M. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782419 · Cited by 9
Self-citation
Microglia positron emission tomography and progression in multiple sclerosis: thalamus on fire
Zeydan, B., Neyal, N., Son, J., Schwarz, C. G., Thomas, J. C. K., Morrison, H. A., Bush, M. L., Reid, R. I., Przybelski, S. A., Fought, A. J., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf141 · Cited by 10
Expansion of white matter hyperintensities is associated with the glymphatic system dysfunction in cerebral small vessel disease
Zeng, B., Zeng, P., Xiang, Y., Peng, Y., Luo, D., Li, B., Chai, Y., Wang, D., Nie, L., & Li, Y. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111391 · Cited by 9
Mapping the microstructure of human cerebral cortex in vivo with diffusion MRI
Sadikov, A., Choi, H. L., Xiao, J., Cai, L. T., & Mukherjee, P. (2025) · Communications Biology · doi:10.1038/s42003-025-08523-9 · Cited by 12
“The DTI, DKI, and MAP-MRI ﬁ tting was performed via dipy 68 ; the NODDI ﬁ tting was performed via AMICO 69 .”
Using 3D-SHORE and MAP-MRI to obtain both tractography and microstructural constrast from a clinical DMRI acquisition
Fick, R., Zucchelli, M., Girard, G., Descoteaux, M., Menegaz, G., & Rachid Deriche, B. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163905 · Cited by 9
“In experiment (I) we generate fiber crossings with angles ranging from 0 0 to 90 0 using the multi-tensor model in Dipy [8].”
InfluentialSelf-citation
A Continuous Model of Cortical Connectivity
Moyer, D., Gutman, B. A., Faskowitz, J., Jahanshad, N., & Thompson, P. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_19 · Cited by 9
“Probabilistic streamline tractography was conducted using the DWI in 2mm isotropic MNI 152 space, using Dipy’s [7] implementation of constrained spherical deconvolution (CSD) [16] with a harmonic order of 6.”
Method
Dense and structured representations of U-shape fiber connectivity in the central sulcus
Pron, A., Brun, L., Deruelle, C., & Coulon, O. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363670 · Cited by 9
“Diffuse toolbox(3), an in-house Brainvisa toolbox [7] wrapping FSL(4) and Dipy [8] functions.”
Background
Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy
Ge, Y., Zheng, W., Li, Y., Dou, W., Ren, S., Chen, Z., & Wang, Z. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.799251 · Cited by 9
Absolute Winding Number Differentiates Mouse Spatial Navigation Strategies With Genetic Risk for Alzheimer’s Disease
Badea, A., Li, D., Niculescu, A. R., Anderson, R. J., Stout, J. A., Williams, C. L., Colton, C. A., Maeda, N., & Dunson, D. B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.848654 · Cited by 9
On the performance of multi-compartment relaxometry for myelin water imaging (MCR-MWI) – test-retest repeatability and inter-protocol reproducibility
Chan, K., Chamberland, M., & Marques, J. P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119824 · Cited by 9
“DWI data was pre-processed using an in-house pipeline consisting of the following procedures: (1) data denoising with Patch2self (Fadnavis et al., 2020; Garyfallidis et al., 2014), (2) Gibbs ringing removal (Kellner et al.”
Method
Design and validation of a navigation system of multimodal medical images for neurosurgery based on mixed reality
Zhou, Z., Yang, Z., Jiang, S., Zhu, Τ., Ma, S., Li, Y., & Zhuo, J. (2023) · Visual Informatics · doi:10.1016/j.visinf.2023.05.003 · Cited by 9
PPA: Principal parcellation analysis for brain connectomes and multiple traits
Liu, R., Li, M., & Dunson, D. B. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120214 · Cited by 10
Tract- and gray matter- based spatial statistics show white matter and gray matter microstructural differences in autistic males
DiPiero, M. A., Cordash, H., Prigge, M. B. D., King, C. K., Morgan, J., Guerrero-Gonzalez, J. M., Adluru, N., King, J. B., LANGE, N. D., Bigler, E. D., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1231719 · Cited by 9
“Diffusion tensors were estimated at each voxel using a weightedleast squares algorithm as part of the diffusion imaging in python (DIPY) open-source software package (Garyfallidis et al., 2014).”
Method
Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging
Goeckner, B. D., Brett, B. L., Mayer, A. R., España, L. Y., Banerjee, A., Muftuler, L. T., & Meier, T. B. (2023) · Human Brain Mapping · doi:10.1002/hbm.26556 · Cited by 8
“A positivity constraint was imposed to ensure a physically meaningful propagator estimate, and Laplacian regularization with a weighting of 0.05 was used to reduce spurious oscillations in the reconstruction (Fick et al., 2015; Garyfallidis et al., 2014).”
MethodInfluential
Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging
Yao, T., Rheault, F., Nath, V., Asad, Z., Newlin, N. R., Cui, C., Deng, R., Ramadass, K., Shafer, A., Resnick, S., et al. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.1.014005 · Cited by 9
“34 All diffusion signals are transformed to SH basis signal ODF as a unified input for DL models, using SHs with the “ tour-nier07 ” basis.”
Method
Privacy preserving image registration
Taiello, R., Önen, M., Capano, F., Humbert, O., & Lorenzi, M. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103129 · Cited by 15
E(3) × SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI.
Elaldi, A., Gerig, G., & Dey, N. (2024) · PubMed · Cited by 9
DIMOND: DIffusion Model OptimizatioN with Deep Learning
Li, Z., Li, Z., Bilgiç, B., Lee, H., Ying, K., Huang, S. Y., Liao, H., & Tian, Q. (2024) · Advanced Science · doi:10.1002/advs.202307965 · Cited by 9
Non-parametric graphnet-regularized representation of dMRI in space and time
Fick, R., Petiet, A., Santin, M. D., Philippe, A.-C., Lehéricy, S., Rachid Deriche, B., & Wassermann, D. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.09.002 · Cited by 9
“We find optimal vales for regularization weights α and λ using cross-validation and mplemented the surrounding code infrastructure inside the DiPy ramework ( Garyfallidis et al., 2014 ).”
MethodInfluential
Comparison of basis functions and q‐space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging
Tobisch, A., Schultz, T., Stirnberg, R., Varela-Mattatall, G. E., Knutsson, H. E., Pablo Irarrázaval, M., & Stöcker, T. (2019) · NMR in Biomedicine · doi:10.1002/nbm.4055 · Cited by 10
Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions
Zhang, Z., Descoteaux, M., & Dunson, D. B. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1574582 · Cited by 9
“First, high angular resolution diffusion imaging (HARDI) techniques are used to estimate the fiber orientation distribution function (ODF) at each location (Descoteaux et al. 2009) (implemented in dipy (Garyfallidis et al. 2014)).”
MethodSelf-citation
Knowledge Transfer Between Datasets for Learning-Based Tissue Microstructure Estimation
Yu, Q., Li, Y., Liu, Z., & Ye, C. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098410 · Cited by 9
“The default parameters given by Dipy [10] are used for the SHORE representation and solving Eq.”
MethodInfluential
Spatiotemporal dynamics of nonhuman primate white matter development during the first year of life
Aggarwal, N., Moody, J. F., Dean, D., Tromp, D., Kecskemeti, S. R., Oler, J. A., Alexander, A. L., & Kalin, N. H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117825 · Cited by 9
Segmented simultaneous multi‐slice diffusion‐weighted imaging with navigated 3D rigid motion correction
Riedel, M., Setsompop, K., Mertins, A., & Börnert, P. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28813 · Cited by 9
White matter microstructural variability linked to differential attentional skills and impulsive behavior in a pediatric population
Gagnon, A., Grenier, G., Bocti, C., Gillet, V., Lepage, J., Baccarelli, A., Posner, J. E., Descoteaux, M., & Takser, L. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac180 · Cited by 10
Self-citation
Impact of chronic exposure to legacy environmental contaminants on the corpus callosum microstructure: A diffusion MRI study of Inuit adolescents
Migneron-Foisy, V., Muckle, G., Jacobson, J. L., Ayotte, P., Jacobson, S. W., & Saint‐Amour, D. (2022) · NeuroToxicology · doi:10.1016/j.neuro.2022.08.010 · Cited by 10
Asymmetry Spectrum Imaging for Baby Diffusion Tractography
and the UNC/UMN Baby Connectome Project Consortium, Wu, Y., Lin, W., Shen, D. G., & Yap, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_24 · Cited by 9
“We extended the direction getter mechanism described in [15], which originally caters to symmetric FODFs, to work with asymmetric FODFs and extracted five tracts-of-interest using RecoBundles [15] (see Figs.”
Method
Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflowPreprint
Schilling, K. G., Tax, C. M. W., Rheault, F., Hansen, C. B., Yang, Q., Yeh, F.-C. F., Cai, L. Y., Anderson, A. W., & Landman, B. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.17.435872 · Cited by 10
A hybrid approach for optimal automatic segmentation of White Matter tracts in HARDI
Chekir, A., Descoteaux, M., Garyfallidis, E., Cote, M.-A., & Boumghar, F. O. (2014) · 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES) · doi:10.1109/iecbes.2014.7047481 · Cited by 12
Self-citation
Effect of age on lateralized auditory processing
Stadler, J., Brechmann, A., & Angenstein, N. (2023) · Hearing Research · doi:10.1016/j.heares.2023.108791 · Cited by 8
Neurite-based white matter alterations in MAPT mutation carriers: A multi-shell diffusion MRI study in the ALLFTD consortium
Corriveau‐Lecavalier, N., Tosakulwong, N., Lesnick, T. G., Fought, A. J., Reid, R. I., Schwarz, C. G., Senjem, M. L., Jack, C. R., Jones, D. T., Vemuri, P., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.12.001 · Cited by 9
Dissection of the Temporofrontal Extreme Capsule Fasciculus Using Diffusion MRI Tractography and Association with Lexical Retrieval
Barbeau, É. B., Badhwar, A., Kousaie, S., Bellec, P., Descoteaux, M., Klein, D., & Petrides, M. (2023) · eNeuro · doi:10.1523/eneuro.0363-23.2023 · Cited by 8
“Standard DTI metrics and ﬁ ber orientation density functions of spherical harmonics order 8 were reconstructed with Dipy (Garyfallidis et al., 2014).”
Self-citation
DCP : A pipeline toolbox for diffusion connectome
Huang, W., Dong, X., Zhao, T., Kucikova, L., Fu, A., & Shu, N. (2024) · Human Brain Mapping · doi:10.1002/hbm.26626 · Cited by 7
“The first set of tools, such as Camino (Cook et al., 2006), MRtrix3 (Tournier et al., 2019), Dipy (Garyfallidis et al., 2014), and QSIPrep (Cieslak et al., 2021), do not have GUI and require users to customize their pipelines by programming, which is challenging for scientific researchers without programming skills.”
Method
Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer’s disease
Phillips, J. S., Adluru, N., Chung, M. K., Radhakrishnan, H., Olm, C. A., Cook, P. A., Gee, J. C., Cousins, K. A. Q., Arezoumandan, S., Wolk, D. A., et al. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1353306 · Cited by 9
Histologic tau lesions and magnetic resonance imaging biomarkers differ across two progressive supranuclear palsy variants
Orlandi, F., Carlos, A. F., Ali, F., Clark, H. M., Duffy, J. R., Utianski, R. L., Botha, H., Machulda, M. M., Stephens, Y. C., Schwarz, C. G., et al. (2024) · Brain Communications · doi:10.1093/braincomms/fcae113 · Cited by 8
Prescription Opioids and Brain Structure in Community-Dwelling Older Adults
Warner, N. S., Hanson, A. C., Schulte, P. J., Kara, F., Reid, R. I., Schwarz, C. G., Benarroch, E. E., Graff‐Radford, J., Vemuri, P., Jack, C. R., et al. (2024) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2024.01.018 · Cited by 8
A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI
Consagra, W., Ning, L., & Yogesh Rathi, A. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103537 · Cited by 8
“We use the implementations in the python library dipy (Garyfallidis et al., 2014) under the default suggested parameterizations.”
Cognitive Impairment in Cerebral Small Vessel Disease Is Associated with Corpus Callosum Microstructure Changes Based on Diffusion MRI
Добрынина, Л. А., Kremneva, E. I., Витальевна, Ш. К., Geints, A. A., Filatov, A. S., Gadzhieva, Z. S., Gnedovskaya, E. V., Кротенкова, М. В., & Maximov, I. I. (2024) · Diagnostics · doi:10.3390/diagnostics14161838 · Cited by 8
Characterizing the spatial patterns and determinants of cerebrospinal fluid pseudorandom flow in the human brain with low b-value diffusion MRI
Nazeri, A., Hosseini, H., Dehkharghanian, T., Lindsay, K. E., LaMontagne, P., Shimony, J. S., Benzinger, T. L. S., & Sotiras, A. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00473 · Cited by 8
“5.0 (Garyfallidis et al., 2014).”
Influential
Cerebral hypoperfusion, brain structural integrity, and cognitive impairment in older APOE4 carriers
Pappas, I., Lohman, T., Dutt, S., Kapoor, A., Engstrom, A. C., Alitin, J. P. M., Barnes, S. R., Chakhoyan, A., Saca, L., Gaggar, R., et al. (2025) · GeroScience · doi:10.1007/s11357-025-01642-5 · Cited by 9
“White matter bundles were extracted using the RecoBundles framework as implemented in DIPY [45].”
Spatial Mapping of Glioblastoma Infiltration: Diffusion Tensor Imaging-Based Radiomics and Connectomics in Recurrence Prediction
Jang, K., & Back, M. (2025) · Brain Sciences · doi:10.3390/brainsci15060576 · Cited by 8
Hamstring muscle architecture and microstructure changes following Nordic hamstring exercise training and detraining
Anoosha Pai, S., Andrews, M. H., Gurchiek, R. D., Pincheira, P. A., Barbieri, M., Friedrich, T., Kogan, F., Gold, G. E., Mazzoli, V., Lichtwark, G. A., et al. (2025) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2025.101070 · Cited by 8
“21 DTI data preprocessing comprised 3 steps: (a) denoising using non-local means, 22 (b) eddy current deformation and motion correction using afﬁne registration of the diffusion-weighted images to the mean of non-weighted images, and (c) suscepti-bility-induced echo planer imaging (EPI) distortion…”
Glymphatic dysfunction links vascular pathology to Alzheimer’s biomarkers and cognitive decline
Kang, S. H., Kim, S., Kim, Y. J., Yoo, H., Lee, E. H., Jang, H. J., Shin, D., Yun, J., Kim, J. P., Kim, H., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-01964-2 · Cited by 8
“DTI model was fitted to the preprocessed data using python library for the analysis of diffusion MRI (DIPY, h t t p s : / / d i p y . o r g / ) [31], and maps for fractional anisotropy (FA), mean diffusivity (MD), axial and radial diffusivity (AD and RD) were generated from the eigenvalues of the…”
A Unifying Framework for Spatial and Temporal Diffusion in Diffusion MRI
Fick, R., Wassermann, D., Pizzolato, M., & Rachid Deriche, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_13 · Cited by 8
Longitudinal Assessment of Intravoxel Incoherent Motion Diffusion‐Weighted MRI Metrics in Cognitive Decline
Bergamino, M., Burke, A. D., Baxter, L., Caselli, R., Sabbagh, M., Talboom, J., Huentelman, M., & Stokes, A. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28172 · Cited by 8
Placental vascular alterations are associated with early neurodevelopmental and pulmonary impairment in the rabbit fetal growth restriction model
Valenzuela, I. J., Basurto, D., Regin, Y., Gie, A. G., der Veeken, L. V., Vergote, S., Muñoz‐Moreno, E., Leszczyński, B., Tielemans, B., Velde, G. V., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-22895-6 · Cited by 7
A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI)
Gast, H., Horowitz, A., Krupnik, R., Barazany, D., Lifshits, S., Ben-Amitay, S., & Assaf, Y. (2023) · Neuroinformatics · doi:10.1007/s12021-023-09630-w · Cited by 8
“Tractography was done using the DiPy Library (Garyfallidis et al., 2014).”
Method
Connectomic insight into unique stroke patient recovery after rTMS treatment
Chen, R., Dadario, N. B., Cook, B., Sun, L., Wang, X., Li, Y., Hu, X., Zhang, X., & Sughrue, M. E. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1063408 · Cited by 8
“Diffusion tractography preprocessing includes standard processing steps (36), which include motion correction, elimination of excess movement, gradient distortion correction, eddy correction, and constrained spherical deconvolution-based deterministic tractography.”
Method
A case of pure apraxia of speech after left hemisphere stroke: behavioral findings and neural correlates
Pracar, A. L., Ivanova, M. V., Richardson, A., & Dronkers, N. F. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1187399 · Cited by 9
“Further tractography data processing was done using algorithms from DIPY (45) as implemented in TractoFlow (46).”
Method
ReeBundle: A Method for Topological Modeling of White Matter Pathways Using Diffusion MRI
Shailja, S., Bhagavatula, V., Cieslak, M., Vettel, J. M., Grafton, S. T., & Manjunath, B. S. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3306049 · Cited by 10
“In contrast, tract profiling techniques like DIPY [6], BUAN [7], and PyAFQ [8] quantify diffusion measures Fig.”
Method
Diffusion Tensor and Kurtosis MRI ‐Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes
Yang, D., Tian, C., Liu, J., Peng, Y., Xiong, Z., Da, J., Yang, Y., Zha, Y., & Zeng, X. (2024) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29263 · Cited by 9
“14 All the b -values were utilized for ﬁ tting both the DTI and DKI models.”
Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning
Planchuelo‐Gómez, Á., Descoteaux, M., Larochelle, H., Hutter, J. M., Jones, D. K., & Tax, C. M. W. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103134 · Cited by 8
Self-citation
Exploring molecular glioblastoma: Insights from advanced imaging for a nuanced understanding of the molecularly defined malignant biology
Griessmair, M., Delbridge, C., Ziegenfeuter, J., Jung, K., Mueller, T., Schramm, S., Bernhardt, D., Schmidt‐Graf, F., Kertels, O., Thomas, M., et al. (2024) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdae106 · Cited by 8
“15 To calculate leakage-corrected CBV, the algorithm of Arzanforoosh et al. 16 was used, which also requires a mask of normal-appearing white matter to normalize the resulting CBV. ANT’s Atropos was used for this purpose with the exclusion of tumor areas.”
Method
Sequential Hierarchical Agglomerative Clustering of White Matter Fiber Pathways
Demir, A., & Çetingül, H. E. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2391913 · Cited by 8
Diffantom: Whole-Brain Diffusion MRI Phantoms Derived from Real Datasets of the Human Connectome Project
Estéban, O., Caruyer, E., Daducci, A., Cuadra, M. B., Ledesma‐Carbayo, M. J., & Santos, A. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00004 · Cited by 8
“The software workflow released with this data report (the diffantomizer) ensures the reproducibility of Diffantom and enables the generation of custom diffantoms.”
BackgroundMethodInfluential

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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