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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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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Track-to-Learn: A general framework for tractography with deep reinforcement learning
Théberge, A., Desrosiers, C., Descoteaux, M., & Jodoin, P. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102093 · Cited by 44
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Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks
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Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI
Karimi, D., Vasung, L., Jaimes, C. E., Machado‐Rivas, F., Warfield, S. K., & Gholipour, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118316 · Cited by 42
Network-level causal analysis of set-shifting during trail making test part B: A multimodal analysis of a glioma surgery case
Mandonnet, E., Vincent, M., Valero‐Cabré, A., Facque, V., Barberis, M., Bonnetblanc, F., Rheault, F., Emmanuelle Volle, C., Descoteaux, M., & Margulies, D. S. (2020) · Cortex · doi:10.1016/j.cortex.2020.08.021 · Cited by 42
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Interaction of amyloid and tau on cortical microstructure in cognitively unimpaired adults
Vogt, N. M., Hunt, J. F. V., Adluru, N., Ma, Y., Hulle, C. A. V., Dean, D., Kecskemeti, S., Chin, N. A., Carlsson, C. M., Asthana, S., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12364 · Cited by 42
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
Portegies, J. M., Fick, R., Sanguinetti, G., Meesters, S., Girard, G., & Duits, R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138122 · Cited by 39
The cingulum as a marker of individual differences in neurocognitive development
Bathelt, J., Johnson, A., Zhang, M., the CALM team, & Astle, D. E. (2019) · Scientific Reports · doi:10.1038/s41598-019-38894-z · Cited by 43
Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines
Bain, J. S., Yeatman, J. D., Schurr, R., Rokem, A., & Mezer, A. (2019) · Human Brain Mapping · doi:10.1002/hbm.24626 · Cited by 43
“We used the SFM implemented in Dipy (Garyfallidis et al., 2014; http://nipy.org/dipy), and also available as a Docker image (https://hub.docker.com/r/arokem/dipy-sfm/), to create a brain map of the dispersion index (DI), a metric that quantifies the amount of fascicle crossing per voxel.”
MethodSelf-citation
Physical activity and cognitive and imaging biomarkers of Alzheimer's disease in down syndrome
Fleming, V., Piro‐Gambetti, B., Patrick, A., Zammit, M. D., Alexander, A. L., Christian, B. T., Handen, B. L., Cohen, A. D., Klunk, W. E., Laymon, C. M., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.07.016 · Cited by 41
Arachidonic and docosahexaenoic acid supplementation and brain maturation in preterm infants; a double blind RCT
Moltu, S. J., Nordvik, T., Rossholt, M. E., Wendel, K., Chawla, M. S., Server, A., Gunnarsdottir, G., Pripp, A. H., Domellöf, M., Bratlie, M., et al. (2023) · Clinical Nutrition · doi:10.1016/j.clnu.2023.11.037 · Cited by 42
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Koppers, S., Haarburger, C., & Merhof, D. (2017) · Mathematics and visualization · doi:10.1007/978-3-319-54130-3_5 · Cited by 41
Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer’s Disease
Elsheikh, S. S. M., Chimusa, E. R., Mulder, N., & Crimi, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-58291-1 · Cited by 38
“In particular, the constant solid angle model was used 50 , and a deterministic algorithm called Euler Delta Crossings 49 was used stemming from 2,000,000 seed-points and stopping when the fractional anisotropy was smaller than 0 .”
Regionconnect: Rapidly extracting standardized brain connectivity information in voxel-wise neuroimaging studies
Qi, X., & Arfanakis, K. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117462 · Cited by 40
“…et al., 2015), d) major white matter bundles (Table 1) were reconstructed from the streamlines of the tractogram using the RecoBundle approach (Garyfallidis et al., 2014; E. 2018) based on the Yeh et al., 2018 bundle models (and were made available at www.nitrc.org/projects/iit), and all…”
Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study
Karayumak, S. C., Zhang, F., Zurrin, R., Billah, T., Zekelman, L., Makris, N., Pieper, S., O’Donnell, L. J., & Yogesh Rathi, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03058-w · Cited by 49
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Schwarz, C. G. (2021) · Neurotherapeutics · doi:10.1007/s13311-021-01030-9 · Cited by 40
“Some of the most popular include DTI-TK [61], DSI Studio [62], and multiple tools within the DIPY [63] and FSL packages [16, 64].”
Method
The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood
Tremblay, P., Perron, M., Deschamps, I., Kennedy‐Higgins, D., Houde, J., Dick, A. S., & Descoteaux, M. (2018) · Human Brain Mapping · doi:10.1002/hbm.24367 · Cited by 40
Self-citation
Free water in gray matter linked to gut microbiota changes with decreased butyrate producers in Alzheimer's disease and mild cognitive impairment
Yamashiro, K., Takabayashi, K., Kamagata, K., Nishimoto, Y., Togashi, Y., Yamauchi, Y., Ogaki, K., Li, Y., Hatano, T., Motoi, Y., et al. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106464 · Cited by 39
“FW maps of all study participants were calculated from multi-shell DWI data using the open-source software package Diffusion Imaging in Python (also known as Dipy) algorithm (dipy.org/) (Garyfallidis et al., 2014), based on a regularized bi-tensor model (Pasternak et al., 2009).”
Method
Mean Apparent Propagator MRI Is Better Than Conventional Diffusion Tensor Imaging for the Evaluation of Parkinson’s Disease: A Prospective Pilot Study
Le, H., Zeng, W., Zhang, H., Li, J., Wu, X., Xie, M., Yan, X., Zhou, M., Zhang, H., Wang, M., et al. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.563595 · Cited by 38
A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome VariabilityPreprint
Kiar, G., Bridgeford, E., Roncal, W. G., Chandrashekhar, V., Mhembere, D., Ryman, S. G., Zuo, X., Margulies, D. S., Craddock, R. C., Priebe, C. E., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/188706 · Cited by 38
The Mexican magnetic resonance imaging dataset of patients with cocaine use disorder: SUDMEX CONN
Ángeles-Valdéz, D., Rasgado‐Toledo, J., Issa‐Garcia, V., Balducci, T., Villicaña, V., Valencia, A., Gonzalez-Olvera, J. J., Reyes-Zamorano, E., & Garza‐Villarreal, E. A. (2022) · Scientific Data · doi:10.1038/s41597-022-01251-3 · Cited by 43
ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI
Boone, L., Biparva, M., Forooshani, P. M., Ramirez, J., Masellis, M., Bartha, R., Symons, S. P., Strother, S. C., Black, S. E., Heyn, C., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120289 · Cited by 42
3D interactive tractography-informed resting-state fMRI connectivity
Chamberland, M., Bernier, M., Fortin, D., Whittingstall, K., & Descoteaux, M. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00275 · Cited by 37
“Next, the data were spatially smoothed using NLM denoising (Coupe et al., 2008; Bernier et al., 2014) implemented in Dipy (Garyfallidis et al., 2014) using default parameters, and band-pass ﬁltered (0.008–0.08 Hz).”
MethodInfluentialSelf-citation
REM sleep is associated with white matter integrity in cognitively healthy, older adults
Altendahl, M., Cotter, D. L., Staffaroni, A. M., Wolf, A. A., Mumford, P., Cobigo, Y., Casaletto, K. B., Elahi, F. M., Yack, L. M., Javed, S., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235395 · Cited by 38
“Diffusion tensors were calculated using a non-linear least-squares algorithm in Dipy [35].”
Method
Mapping individual differences across brain network structure to function and behavior with connectome embedding
Levakov, G., Faskowitz, J., Avidan, G., & Sporns, O. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118469 · Cited by 41
Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer’s Disease
Badea, A., Wu, W., Shuff, J., Wang, M. S., Anderson, R. J., Qi, Y. P., Johnson, G. A., Wilson, J. G., Koudoro, S., Garyfallidis, E., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00072 · Cited by 31
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fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients
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Denoising diffusion MRI: Considerations and implications for analysis
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“We used the default implementation in DIPY (v.1.7.0) ( Garyfallidis et al., 2014), where each dMRI volume is denoised independently. ii) The Marchenko-Pastur PCA (MPPCA) (denoted as | MPPCA | throughout the text) ( Cordero-Grande et al., 2019; Veraart, Novikov, et al., 2016), reflecting a commonly…”
Influential
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“…currently available are integrated brain network tools that are designed for processing a single imaging modality Such packages include Camino [24], MNE [25, 26], dipy [27], Brainstorm [28], the Connectome Mapper [29], the MR Connectome Analysis Pipeline [30] and many other unpublished packages.”
BackgroundMethod
White matter information flow mapping from diffusion MRI and EEG
Deslauriers‐Gauthier, S., Lina, J., Butler, R., Whittingstall, K., Gilbert, G., Bernier, P., Rachid Deriche, B., & Descoteaux, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116017 · Cited by 39
Self-citation
Get Moving! Increases in Physical Activity Are Associated With Increasing Functional Connectivity Trajectories in Typically Aging Adults
Dorsman, K. A., Weiner‐Light, S., Staffaroni, A. M., Brown, J. A., Wolf, A. A., Cobigo, Y., Walters, S. M., Kramer, J. H., & Casaletto, K. B. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00104 · Cited by 39
The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students
Tsuchida, A., Laurent, A., Crivello, F., Petit, L., Joliot, M., Pepe, A., Beguedou, N., Gueye, M.-F., Verrecchia, V., Nozais, V., et al. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02334-4 · Cited by 40
Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping
Tourbier, S., Rué‐Queralt, J., Glomb, K., Alemán‐Gómez, Y., Mullier, E., Griffa, A., Schöttner, M., Wirsich, J., Tuncel, A., Jancovic, J., et al. (2022) · The Journal of Open Source Software · doi:10.21105/joss.04248 · Cited by 39
Neurofilament light chain is increased in the parahippocampal cortex and associates with pathological hallmarks in Parkinson’s disease dementia
Frigerio, I., Laansma, M. A., Lin, C., Hermans, E. J. M., Bouwman, M. M. A., Bol, J. G. J. M., Graaf, Y. G.-D., Hepp, D. H., Rozemüller, A. J. M., Barkhof, F., et al. (2023) · Translational Neurodegeneration · doi:10.1186/s40035-022-00328-8 · Cited by 35
“Freesurfer [41] and free‐water corrected cortical mean diffusivity (FWC‐MD) was derived with FSL [44], DIPY [47, 48] and Freesurfer [41] (purple”
Method
Predicting motor outcomes in stroke patients using diffusion Spectrum mri microstructural measures
Hodgson, K., Adluru, G., Richards, L. G., Majersik, J. J., Stoddard, G., Adluru, N., & DiBella, E. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00072 · Cited by 36
“The orientation distribution functions were estimated using DIPY (17, 29) for calculating GFA.”
Method
Efficient PCA denoising of spatially correlated redundant MRI data
Henriques, R. N., Ianuș, A., Novello, L., Jovicich, J., Jespersen, S. N., & Shemesh, N. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00049 · Cited by 38
“…was sub-divided into 9 portions (4 × 4 voxel regions each), and diffusion-weighted signals were generated using the Diffusion in Python (DIPY ( Garyfallidis et al., 2014; Henriques et al., 2021)) package according to a forward model containing two compartment types with different axially…”
Method
Whole mouse brain imaging using optical coherence tomography: reconstruction, normalization, segmentation, and comparison with diffusion MRI
Lefebvre, J., Castonguay, A., Pouliot, P., Descoteaux, M., & Lesage, F. (2017) · Neurophotonics · doi:10.1117/1.nph.4.4.041501 · Cited by 37
Self-citation
Applying microstructural models to understand the role of white matter in cognitive development
Huber, E., Henriques, R. N., Owen, J. P., Rokem, A., & Yeatman, J. D. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100624 · Cited by 34
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Plasma inflammatory biomarkers link to diffusion tensor imaging metrics in virally suppressed HIV-infected individuals
Chang, K., Premeaux, T. A., Cobigo, Y., Milanini, B., Hellmuth, J. M., Rubin, L. H., Javandel, S., Allen, I. E., Ndhlovu, L. C., Paul, R. H., et al. (2019) · AIDS · doi:10.1097/qad.0000000000002404 · Cited by 34
“The susceptibility distortion corrected diffusion images were then fitted using Dipy with a nonlinear least-squares approach to create the DTI tensor maps [34].”
Method
Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data
Chakraborty, R., Zhen, X., Vogt, N., Bendlin, B. B., & Singh, V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01072 · Cited by 35
“Diffusion tensor imaging (DTI) and the orientation distribution functions (ODF), which were used as the representative of the DWI, were performed using the Diffusion Imaging in Python (DIPY) toolbox[18].”
Method
The frontotemporal organization of the arcuate fasciculus and its relationship with speech perception in young and older amateur singers and non‐singers
Perron, M., Theaud, G., Descoteaux, M., & Tremblay, P. (2021) · Human Brain Mapping · doi:10.1002/hbm.25416 · Cited by 33
“…were normalized using a N4 correction procedure (Tustison et al., 2010) from the advanced normalization tools (ANTs) (Avants et al., 2011) and the resulting DW images were resampled to 1 mm isotropic as implemented in Diffusion Imaging in Python (DIPY) toolbox (Garyfallidis et al., 2014).”
MethodSelf-citation
Structurally constrained effective brain connectivity
Crimi, A., Dodero, L., Sambataro, F., Murino, V., & Sona, D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118288 · Cited by 33
A large-scale investigation of white matter microstructural associations with reading ability
Meisler, S. L., & Gabrieli, J. D. E. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118909 · Cited by 38
Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI
Karat, B. G., DeKraker, J., Hussain, U., Köhler, S., & KHAN, A. R. (2023) · Human Brain Mapping · doi:10.1002/hbm.26461 · Cited by 33
Dopaminergic denervation and associated MRI microstructural changes in the nigrostriatal projection in early Parkinson’s disease patients
López‐Aguirre, M., Matarazzo, M., Blesa, J., Monje, M. H. G., Rodríguez‐Rojas, R., Sánchez‐Ferro, Á., Obeso, J. A., & Pineda‐Pardo, J. A. (2023) · npj Parkinson s Disease · doi:10.1038/s41531-023-00586-x · Cited by 36
“FW maps were estimated with the Dipy library 82 by ﬁ tting the bi-tensor model with the regularized gradient descent method and applying the hybrid initialization strategy described by Parker et al. ( PLoS One , 2020) 83 .”
mpdcm: A toolbox for massively parallel dynamic causal modeling
Aponte, E. A., Raman, S., Sengupta, B., Penny, W. D., Stephan, K. E., & Heinzle, J. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.09.009 · Cited by 33
A finite-element reciprocity solution for EEG forward modeling with realistic individual head models
Ziegler, E., Chellappa, S. L., Gaggioni, G., Ly, J. Q. M., Vandewalle, G., André, E., Geuzaine, C., & Phillips, C. L. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.08.056 · Cited by 33
“Diffusion tensor eigenvalues were remapped to produce conductivity tensors [38] using functions from Dipy [39].”
Method
Connectomic consistency: a systematic stability analysis of structural and functional connectivity
Osmanlıoğlu, Y., Alappatt, J. A., Parker, D., & Verma, R. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab947b · Cited by 33
Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi‐group comparison
Ferizi, U., Scherrer, B., Schneider, T., Alipoor, M., Eufracio, O., Fick, R., Rachid Deriche, B., Nilsson, M., Loya-Olivas, A. K., Rivera, M., et al. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3734 · Cited by 31
Self-citation
Advances in computational and statistical diffusion MRI
O’Donnell, L. J., Daducci, A., Wassermann, D., & Lenglet, C. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3805 · Cited by 32
Micro-Networks for Robust MR-Guided Low Count PET Imaging
da Costa-Luis, C. O., & Reader, A. J. (2020) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2020.2986414 · Cited by 33
“Corresponding MPRAGE T1 reconstructions were scaled and registered to the full count PET reconstructions using dipy [41] before performing NLM ﬁltering on the PET reconstructions (3).”
Method
Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases
Samani, Z. R., Parker, D., Wolf, R. L., Hodges, W., Brem, S. S., & Verma, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-93804-6 · Cited by 32
MRI biomarkers for Alzheimer's disease: the impact of functional connectivity in the default mode network and structural connectivity between lobes on diagnostic accuracy
Mohtasib, R., Alghamdi, J., Jobeir, A., Masawi, A., de Barros, N. D., Billiet, T., Struyfs, H., Phan, T. V., van Hecke, W., & Ribbens, A. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e08901 · Cited by 32
Injury to the Cerebellum in Term Asphyxiated Newborns Treated with Hypothermia
Kwan, S., Boudes, E., Gilbert, G., Saint‐Martin, C., Albrecht, S., Shevell, M. I., & Wintermark, P. (2015) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4326 · Cited by 32
“Then, ADC and FA maps were generated for the whole brain by using the tools of the Diffusion Imaging in Python (DIPY) package (Sherbrooke Connectivity Imaging Lab, Sherbrooke University, Sherbrooke, Quebec, Canada).(20) To assess whether subtle changes in the cerebellum were not recognized on conventional MR imaging o…”
Method
A “glympse” into neurodegeneration: Diffusion MRI and cerebrospinal fluid aquaporin‐4 for the assessment of glymphatic system in Alzheimer's disease and other dementias
Sacchi, L., D’Agata, F., Campisi, C., Arcaro, M., Carandini, T., Örzsik, B., Maschio, V. P. D., Fenoglio, C., Pietroboni, A. M., Ghezzi, L., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26805 · Cited by 33
“Maps of the fractional volume of FW and of the MK were constructed from preprocessed DW images fitting a regularized bitensor model and the DKI, respectively, with the open-source software package Diffusion Imaging in Python (Dipy) algorithm (dipy.org/) (Garyfallidis et al., 2014).”
Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging
Tobisch, A., Stirnberg, R., Harms, R., Schultz, T., Roebroeck, A. F., Breteler, M. M. B., & Stöcker, T. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00650 · Cited by 31
Neurite Orientation Dispersion and Density Imaging for Assessing Acute Inflammation and Lesion Evolution in MS
Sacco, S., Caverzasi, E., Papinutto, N., Cordano, C., Bischof, A., Gundel, T. J., Cheng, S., Asteggiano, C., Kirkish, G., Mallott, J. M., et al. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6862 · Cited by 33

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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