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Dipy, a library for the analysis of diffusion MRI data
Open accessAbstract
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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“In contrast to projects such as DiPy (Garyfallidis et al., 2014) , this library does not provide functionality to manipulate raw image data within Python itself, but provides functionality to invoke and manage external high-performance executables to perform the actual computations.”
From MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation (2019) · cites this paper as Garyfallidis et al., 2014
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Citations
Cites
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“…median_otsu (see Figure 3 ). from dipy.segment.mask import median_otsu mask, S0_mask = median_otsu(data [:, :, :, 0]) median_otsu uses first a median filter to smooth the S0 and then Otsu’s method, an automated histogram method (Otsu, 1979) to separate the brain (foreground) from its background.”
MethodAdvances in functional and structural MR image analysis and implementation as FSL
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MethodInfluentialThe NumPy Array: A Structure for Efficient Numerical Computation
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“There has been large growth in the number of Python users, there are many Python tools for scientific computing (Pérez and Granger, 2007; Pérez et al., 2011; McKinney, 2012), and there are complementary neuroimaging packages in Python1.”
Background
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Cited by
Sorted by citations
Items 201–300 of 1,000
Ren, H., Zhu, J., Su, X., Chen, S., Zeng, S., Lan, X., Zou, L., Sughrue, M. E., & Guo, Y. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.584430 · Cited by 31
“Diffusion Tractography Preprocessing The diffusion tractography (DT) images were processed using the Omniscient software, which employs a standard processing steps in the Python language (28): (1) the diffusion image was resliced to ensure isotropic voxels; (2) motion correction was performed using a rigid body alignm…”
MethodMultidimensional analysis and detection of informative features in human brain white matter
Richie-Halford, A., Yeatman, J. D., Simon, N., & Rokem, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009136 · Cited by 29
Self-citationTractography of supplementary motor area projections in progressive speech apraxia and aphasia
Carbó, A. V., Reid, R. I., Tosakulwong, N., Weigand, S. D., Duffy, J. R., Clark, H. M., Utianski, R. L., Botha, H., Machulda, M. M., Strand, E. A., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.102999 · Cited by 32
“Diffusion tensors were estimated using nonlinear least squares fitting and used to calculate Fractional Anisotropy (FA) and Mean Diffusivity (MD) images in dipy (Garyfallidis et al., 2014).”
MethodMulti-modal and multi-subject modular organization of human brain networks
Puxeddu, M. G., Faskowitz, J., Sporns, O., Astolfi, L., & Betzel, R. F. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119673 · Cited by 31
Children's academic attainment is linked to the global organization of the white matter connectome
Bathelt, J., Gathercole, S. E., Butterfield, S., the CALM team, & Astle, D. E. (2018) · Developmental Science · doi:10.1111/desc.12662 · Cited by 35
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
Lu, Y., Aslani, S., Zhao, A., Shahin, A. H., Barber, D., Emberton, M., Alexander, D. C., & Jacob, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18695 · Cited by 30
White matter and literacy: A dynamic system in flux
Roy, E. A., Richie-Halford, A., Kruper, J., Narayan, M., Bloom, D., Nedelec, P., Rauschecker, A. M., Sugrue, L. P., Brown, T. T., Jernigan, T. L., et al. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101341 · Cited by 28
“First, we generated a linear-mixed effects model predicting mean-centered TOWRE reading scores from time point, initial age, mean-centered FA at each time point, and overall mean FA as fixed effects, and participants as a random effect.”
MethodSelf-citationBrun, L., Pron, A., Sein, J., Deruelle, C., & Coulon, O. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00536 · Cited by 31
Correlation Tensor MRI deciphers underlying kurtosis sources in stroke
Alves, R., Henriques, R. N., Kerkelä, L., Chavarrías, C., Jespersen, S. N., & Shemesh, N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118833 · Cited by 28
Rozanski, V. E., da Silva, N. M., Ahmadi, S., Mehrkens, J. H., Cunha, J. D. S., Houde, J., Vollmar, C., Bötzel, K., & Descoteaux, M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23450 · Cited by 30
“In parallel, diffusion MRI processing was done as in [Girard et al., 2014], based on the Diffusion in Python (Dipy—www.dipy.org) [Garyfallidis et al., 2014].”
MethodSelf-citationChamberland, M., Girard, G., Bernier, M., Fortin, D., Descoteaux, M., & Whittingstall, K. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0539 · Cited by 30
“SC matrices (M n · n ) (Hagmann et al., 2007) were defined as the number of streamlines connecting pairs of cortical regions and computed by using Dipy (Garyfallidis et al., 2014) The M matrices were then normalized to account for various total streamline counts across different subjects, such that:…”
Self-citationInnocenti, G. M., Caminiti, R., Rouiller, E. M., Knott, G. W., Dyrby, T. B., Descoteaux, M., & Thiran, J. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx363 · Cited by 30
Self-citationNoise reduction in diffusion MRI using non-local self-similar information in joint x−q space
Chen, G., Wu, Y., Shen, D. G., & Yap, P. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.006 · Cited by 30
“GFA: We computed the GFA ( Tuch, 2004 ) based on Aganj et al. (2010) , as implemented in Dipy ( Garyfallidis et al., 2014 ), for evaluation.”
MethodMarawi, T., Zhukovsky, P., Rashidi‐Ranjbar, N., Bowie, C. R., Brooks, H., Fischer, C. E., Flint, A. J., Herrmann, N., Mah, L., Pollock, B. G., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.05.018 · Cited by 30
Medical image registration via neural fields
Sun, S., Han, K., You, C., Tang, H., Kong, D., Naushad, J., Yan, X., Ma, H., Khosravi, P., Duncan, J. S., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103249 · Cited by 50
Rheault, F., Houde, J., & Descoteaux, M. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00042 · Cited by 30
“The basic point-based technique used the implementation available in Dipy (Garyfallidis et al., 2014).”
BackgroundMethodInfluentialSelf-citationTractography of the external capsule and cognition: A diffusion MRI study of cholinergic fibers
Nolze-Charron, G., Rouleau, R. D., Houde, J., Dumont, M., Castellano, C., Cunnane, S. C., Lorrain, D., Fülöp, T., Descoteaux, M., & Bocti, C. (2019) · Experimental Gerontology · doi:10.1016/j.exger.2019.110792 · Cited by 29
Self-citationChen, H., Zhan, C., Cai, L., Lin, J., Zhou, M., Zou, Z., Yao, X., & Lin, Y.-J. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102863 · Cited by 29
Comparison of Biomarkers in Transgenic Alzheimer Rats Using Multi-Shell Diffusion MRI
Fick, R., Daianu, M., Pizzolato, M., Wassermann, D., Jacobs, R. E., Thompson, P. M., Town, T., & Rachid Deriche, B. (2017) · Mathematics and visualization · doi:10.1007/978-3-319-54130-3_16 · Cited by 30
“We estimated the DTI and MAP-MRI metrics using the diffusion imaging in python (dipy) open source software [12] and the NODDI metrics using the NODDI toolbox [3].”
MethodDenoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space
Chen, G., Dong, B., Zhang, Y., Lin, W., Shen, D. G., & Yap, P. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2915629 · Cited by 29
“[46] E. Garyfallidis et al., “Dipy, a library for the analysis of diffusion MRI data,” Frontiers Neuroinform., vol. 8, p. 8, Feb. 2014.”
Method[Re] Optimization of a free water elimination two-compartment model for diffusion tensor imagingPreprint
Henriques, R. N., Rokem, A., Garyfallidis, E., St‐Jean, S., Peterson, E., & Correia, M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/108795 · Cited by 29
“All implementations are made in Python, and are designed to be compatible with previously optimized functions that are freely available as part of the open-source software package Diffusion Imaging in Python (Dipy, [4]).”
MethodInfluentialSelf-citationScience in the cloud (SIC): A use case in MRI connectomics
Kiar, G., Gorgolewski, K. J., Kleissas, D. M., Roncal, W. G., Litt, B., Wandell, B. A., Poldrack, R. A., Wiener, M., Vogelstein, R. J., Burns, R. C., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix013 · Cited by 25
“The tensor calculation and tractography steps are performed with the DiPy package [30].”
MethodTracking and validation techniques for topographically organized tractography
Aydogan, D. B., & Shi, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.071 · Cited by 27
Segato, A., Pieri, V., Favaro, A., Riva, M., Falini, A., Momi, E. D., & Castellano, A. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00070 · Cited by 28
“Diffusion tensor and fractional anisotropy (FA) maps were estimated using Diffusion imaging in Python (Dipy) software (Garyfallidis et al., 2014).”
MethodHansen, C. B., Yang, Q., Lyu, I., Rheault, F., Kerley, C. I., Chandio, B. Q., Fadnavis, S., Williams, O. A., Shafer, A. T., Resnick, S. M., et al. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09497-1 · Cited by 29
Self-citationDiffusion tensor estimation with transformer neural networks
Karimi, D., & Gholipour, A. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102330 · Cited by 28
Segregated circuits for phonemic and semantic fluency: A novel patient-tailored disconnection study
Zigiotto, L., Vavassori, L., Annicchiarico, L., Corsini, F., Avesani, P., Rozzanigo, U., Sarubbo, S., & Papagno, C. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103149 · Cited by 25
Impact of asthma on the brain: evidence from diffusion MRI, CSF biomarkers and cognitive decline
Nair, A. K., Hulle, C. A. V., Bendlin, B. B., Zetterberg, H., Blennow, K., Wild, N., Kollmorgen, G., Suridjan, I., Busse, W. W., Dean, D., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad180 · Cited by 29
“43 Diffusion tensors were fit at each voxel within the brain mask using the Diffusion Imaging in Python (DIPY) software package 44 using weighted least-squares optimization.”
Age-related white-matter correlates of motor sequence learning and consolidation
Vien, C., Boré, A., Lungu, O., Benali, H., Carrier, J., Fogel, S. M., & Doyon, J. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.08.006 · Cited by 34
Diffusion MRI monitoring of specific structures in the irradiated rat brain
Constanzo, J., Dumont, M., Lebel, R., Tremblay, L., Whittingstall, K., Masson‐Côté, L., Geha, S., Sarret, P., Lepage, M., Paquette, B., et al. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27112 · Cited by 28
“From our isotropic diffusion-weighted data set, in-house DTI reconstruction(24) were performed using the Dipy library.(25) First, a non-negative least square method was used to compute the diffusion tensors and local DTI measures such as FA between 0 and 1 and diffusivities AD, RD, MD (axial, radial, mean diffusivitie…”
MethodSelf-citationCortical microstructural associations with CSF amyloid and pTau
Nir, T. M., Villalón‐Reina, J. E., Salminen, L. E., Haddad, E., Zheng, H., Thomopoulos, S. I., Jack, C. R., Weiner, M. W., Thompson, P. M., Jahanshad, N., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02321-7 · Cited by 27
“Raw DWI were denoised using the DIPY LPCA fi lter [20, 21] and corrected for Gibbs ringing artifacts with MRtrix [22].”
MethodAging and Positive Mood: Longitudinal Neurobiological and Cognitive Correlates
Cotter, D. L., Walters, S. M., Fonseca, C. S., Wolf, A. A., Cobigo, Y., Fox, E. C., You, M. Y., Altendahl, M., Djukic, N. A., Staffaroni, A. M., et al. (2020) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2020.05.002 · Cited by 27
Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction
Huber, E., Mezer, A., & Yeatman, J. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118453 · Cited by 28
GA3C Reinforcement Learning for Surgical Steerable Catheter Path Planning
Segato, A., Sestini, L., Castellano, A., & Momi, E. D. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196954 · Cited by 27
“From HARDI images, MR Tractography reconstruction of the corticospinal tracts (CST) based on a q-ball residual bootstrap algorithm were obtained using Diffusion imaging in Python (Dipy) software ([27]; [28]).”
MethodAmrani, F. A., Kwan, S., Gilbert, G., Saint‐Martin, C., Shevell, M. I., & Wintermark, P. (2017) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2017.04.025 · Cited by 26
“Then, apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps were generated for the whole brain using the tools of the Diffusion Imaging in Python (DIPY) package (Sherbrooke Connectivity Imaging Lab, Sherbrooke University, Quebec, Canada) [17].”
MethodShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning
Chatterjee, S., Sciarra, A., Dünnwald, M., Mushunuri, R. V., Podishetti, R., Rao, R. N., Gopinath, G. D., Oeltze‐Jafra, S., Speck, O., & Nürnberger, A. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9615963 · Cited by 26
Impact of Locus Coeruleus and Its Projections on Memory and Aging
Langley, J. A., Hussain, S., Huddleston, D. E., Bennett, I. J., & Hu, X. P. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0947 · Cited by 23
“The free water elimination al-gorithm was implemented in DiPy (Garyfallidis et al., 2014).”
Diffusion tensor imaging analysis in three progressive supranuclear palsy variants
Whitwell, J. L., Tosakulwong, N., Clark, H. M., Ali, F., Botha, H., Weigand, S. D., Sintini, I., Machulda, M. M., Schwarz, C. G., Reid, R. I., et al. (2021) · Journal of Neurology · doi:10.1007/s00415-020-10360-1 · Cited by 27
“Diffusion tensors were estimated using nonlinear least squares fitting, and used to calculate Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD), and Axial Diffusivity (AxD) images in dipy [39].”
MethodDissociation of structural and functional connectomic coherence in glioma patients
Jütten, K., Weninger, L., Mainz, V., Gauggel, S., Binkofski, F., Wiesmann, M., Merhof, D., Clusmann, H., & Na, C.-H. (2021) · Scientific Reports · doi:10.1038/s41598-021-95932-5 · Cited by 27
Moody, J. F., Dean, D., Kecskemeti, S. R., Blennow, K., Zetterberg, H., Kollmorgen, G., Suridjan, I., Wild, N., Carlsson, C. M., Johnson, S. C., et al. (2022) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12381 · Cited by 24
“26 Immunoassays were performed on either a cobas e 601 analyzer (Elecsys β-Amyloid (1-42) CSF, β-Amyloid (1-40) CSF, Elecsys Phospho-Tau (181P) CSF) or a cobas e 411 analyzer (NfL, neurogranin, α-synuclein, GFAP, sTREM2, and YKL-40).”
MethodBernier, M., Chamberland, M., Houde, J., Descoteaux, M., & Whittingstall, K. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00715 · Cited by 26
Self-citationBathelt, J., Astle, D. E., Barnes, J., Raymond, F. L., & Baker, K. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.07.016 · Cited by 25
“Whole brain coverage was obtained with 60 contiguous axial slices and isometric image resolution of 2 mm. Echo time was 90 ms and repetition time was 8400 ms.”
MethodInfluentialStaffaroni, A. M., Tosun, D., Lin, J., Elahi, F. M., Casaletto, K. B., Wynn, M. J., Patel, N., Neuhaus, J. M., Walters, S. M., Epel, E. S., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.04.021 · Cited by 28
Lapa, P., Castelli, M., Gonçalves, I., Sala, E., & Rundo, L. (2020) · Applied Sciences · doi:10.3390/app10010338 · Cited by 26
A multi-scale probabilistic atlas of the human connectome
Alemán‐Gómez, Y., Griffa, A., Houde, J., Najdenovska, E., Magon, S., Cuadra, M. B., Descoteaux, M., & Hagmann, P. (2022) · Scientific Data · doi:10.1038/s41597-022-01624-8 · Cited by 28
“The corrected DWIs were also used to estimate the intravoxel fiber orientation distribution function ( fODF ) by using the Constrained Spherical Deconvolution ( CSD ) 42 approach implemented in Dipy.”
MethodSelf-citationTesting for Equivalence of Network Distribution Using Subgraph Counts
Maugis, P.-A., Olhede, S. C., Priebe, C. E., & Wolfe, P. J. (2020) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1736085 · Cited by 24
“A deterministic tractography algorithm is applied using DiPy’s EuDX (Garyfallidis et al. 2012) to obtain streamlines, which indicate the voxels connected by an axonal fiber tract.”
MethodÖrzsik, B., Palombo, M., Asllani, I., Dijk, D., Harrison, N. A., & Cercignani, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120124 · Cited by 26
An analysis-ready and quality controlled resource for pediatric brain white-matter research
Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S. C., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M. P., Avelar‐Pereira, B., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01695-7 · Cited by 32
Vijayakumari, A. A., Parker, D., Osmanlıoğlu, Y., Alappatt, J. A., Whyte, J., Diaz-Arrastia, R. R., Kim, J., & Verma, R. (2021) · Journal of Neurotrauma · doi:10.1089/neu.2021.0057 · Cited by 24
“A tensor model was fit to the data 38 in DIPY, 39 and standard fractional anisotropy (FA) and mean diffusivity (MD) were derived.”
Fascicle- and Glucose-Specific Deterioration in White Matter Energy Supply in Alzheimer’s Disease
Roy, M., Rheault, F., Croteau, É., Castellano, C., Fortier, M., St‐Pierre, V., Houde, J., Turcotte, É. E., Bocti, C., Fülöp, T., et al. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200213 · Cited by 23
Self-citationInterpretable deep learning as a means for decrypting disease signature in multiple sclerosis
Cruciani, F., Brusini, L., Zucchelli, M., Pinheiro, G. R., Setti, F., Galazzo, I. B., Rachid Deriche, B., Rittner, L., Calabrese, M., & Menegaz, G. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac0f4b · Cited by 27
Bazydlo, A., Zammit, M. D., Wu, M., Dean, D., Johnson, S. C., Tudorascu, D., Cohen, A. D., Cody, K. A., Ances, B. M., Laymon, C. M., et al. (2021) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-021-09366-1 · Cited by 23
“Data were processed using an in-house processing pipeline utilizing tools from FSL [40], Mrtrix [41], and the DiPy toolbox [42].”
MethodGardier, R. M. M., Haro, J. L. V., Canales-Rodríguez, E. J., Jelescu, I., Girard, G., Rafael‐Patiño, J., & Thiran, J. (2023) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29720 · Cited by 24
Jordan, K., Amirbekian, B., Keshavan, A., & Henry, R. G. (2017) · Journal of Neuroimaging · doi:10.1111/jon.12467 · Cited by 24
Self-citationMasjoodi, S., Hashemi, H., Oghabian, M. A., & Sharifi, G. (2018) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.874 · Cited by 24
“DTI Data Analysis Applying Dipy software, the diffusion tensor model was fitted to the data for each voxel of the data using nonlinear least square fitting [24].”
MethodFar and wide: Associations between childhood socio-economic status and brain connectomics
Johnson, A., Bathelt, J., Akarca, D., Crickmore, G., & Astle, D. E. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100888 · Cited by 29
Proceedings of the 17th Python in Science Conference
(2018) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-4af1f417-018 · Cited by 24
Chen, J., Ades‐Aron, B., Lee, H., Mehrin, S., Pang, M., Novikov, D. S., Veraart, J., & Fieremans, E. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00125 · Cited by 27
“MRtrix3 ( Tournier et al., 2019), TOR-TOISE ( Irfanoglu et al., 2017), and DIPY ( Garyfallidis et al., 2014) are among many other software tools that make a range of previously proposed artifact correction methods available to the community and allow users to build a customized pipeline specific to…”
Falcó-Roget, J., Cacciola, A., Sambataro, F., & Crimi, A. (2024) · Communications Biology · doi:10.1038/s42003-024-06119-3 · Cited by 27
Differences in educational opportunity predict white matter development
Roy, E. A., Rinsveld, A. M. V., Nedelec, P., Richie-Halford, A., Rauschecker, A. M., Sugrue, L. P., Rokem, A., McCandliss, B. D., & Yeatman, J. D. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101386 · Cited by 22
“Briefly, fiber orientation distributions were estimated in each voxel using constrained spherical deconvolution (Tournier et al., 2007) implemented in DIPY (Garyfallidis et al., 2014) before probabilistic tractography was used to generate streamlines throughout the white matter.”
MethodSelf-citationBautin, P., Fortier, M.-A., Sean, M., Little, G. E., Martel, M., Descoteaux, M., Léonard, G., & Tétreault, P. (2024) · Pain · doi:10.1097/j.pain.0000000000003345 · Cited by 31
Self-citationDiffusion MRI with machine learning
Karimi, D., & Warfield, S. K. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00353 · Cited by 23
“Several computational pipelines and software projects have aimed at standardizing and streamlining some of the more routine dMRI computations [11], [12], [13].”
Tahedl, M., Tournier, J., & Smith, R. E. (2025) · Nature Protocols · doi:10.1038/s41596-024-01129-1 · Cited by 28
Continuous representations of brain connectivity using spatial point processes
Moyer, D., Gutman, B. A., Faskowitz, J., Jahanshad, N., & Thompson, P. M. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.04.013 · Cited by 22
Casaletto, K. B., Elahi, F. M., Fitch, R., Walters, S. M., Fox, E. P., Staffaroni, A. M., Bettcher, B. M., Zetterberg, H., Karydas, A., Rojas, J. C., et al. (2018) · Cytokine · doi:10.1016/j.cyto.2018.05.032 · Cited by 23
Edde, M., Dilharreguy, B., Theaud, G., Chanraud, S., Helmer, C., Dartigues, J., Amieva, H., Michèle Allard, A., Descoteaux, M., & Catheline, G. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02121-7 · Cited by 20
Self-citationLohrberg, M., Winkler, A., Franz, J., van der Meer, F., Ruhwedel, T., Sirmpilatze, N., Dadarwal, R., Löber-Handwerker, R., Esser, D. S., Wiegand, K., et al. (2020) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-01105-2 · Cited by 25
Enhanced Fiber Tractography Using Edema Correction: Application and Evaluation in High-Grade Gliomas
Henderson, F. C., Parker, D., Vijayakumari, A. A., Elliott, M., Lucas, T. H., McGarvey, M. L., Karpf, L., Desiderio, L. M., Harsch, J., Levy, S. C., et al. (2021) · Neurosurgery · doi:10.1093/neuros/nyab129 · Cited by 23
Shen, X., Raghavan, S., Przybelski, S. A., Lesnick, T. G., Ma, S., Reid, R. I., Graff‐Radford, J., Mielke, M. M., Knopman, D. S., Petersen, R. C., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103077 · Cited by 23
Schilling, K. G., Fadnavis, S., Batson, J., Visagie, M., Combes, A. J. E., By, S., McKnight, C. D., Bagnato, F. R., Garyfallidis, E., Landman, B. A., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119826 · Cited by 22
Self-citationCaffarra, S., Kanopka, K., Kruper, J., Richie-Halford, A., Roy, E. A., Rokem, A., & Yeatman, J. D. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0684-23.2023 · Cited by 23
Self-citationLocus coeruleus features are linked to vagus nerve stimulation response in drug-resistant epilepsy
Berger, A., Beckers, E., Joris, V., Duchêne, G., Danthine, V., Delinte, N., Cakiroglu, I., Sherif, S., Germany, E., Torres, A., et al. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1296161 · Cited by 27
A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity
Tanner, J., Faskowitz, J., Teixeira, A. S., Seguin, C., Coletta, L., Gozzi, A., Mišić, B., & Betzel, R. F. (2024) · Nature Communications · doi:10.1038/s41467-024-50248-6 · Cited by 23
“Replace lower triangle weights with weights from upper triangle: ofls* t(cid:176) fi oflt* s(cid:176) .”
Estimation of fiber orientations using neighborhood information
Ye, C., Zhuo, J., Gullapalli, R. P., & Prince, J. L. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.05.008 · Cited by 22
“SHORE and CSD are implemented using the Dipy software (Garyfallidis et al., 2014) (http://nipy.org/dipy/documentation.html); CFARI and CFARI-s are implemented in the JIST software framework (Lucas et al., 2010); GQI is implemented in the DSI Studio software (http://dsistudio.labsolver.org/Manual/…”
MethodMasson, H. L., Wallraven, C., & Petit, L. (2016) · Human Brain Mapping · doi:10.1002/hbm.23422 · Cited by 26
“Preprocessing of the diffusion data used a combination of tools from the FMRIB software library (FSL [Jenkinson et al., 2012], MRtrix [Tournier et al., 2012], www.nitrc.org/projects/ mrtrix) and Dipy ([Garyfallidis et al., 2014], http://nipy. org/dipy) software packages.”
MethodInvestigation of white matter PiB uptake as a marker of white matter integrity
Zeydan, B., Schwarz, C. G., Lowe, V. J., Reid, R. I., Przybelski, S. A., Lesnick, T. G., Kremers, W. K., Senjem, M. L., Gunter, J. L., Min, H.-K. P., et al. (2019) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.741 · Cited by 23
“After correcting for subject motion and residual eddy current distortion, diffusion tensors were fit on extracted brain voxels using weighted least squares optimization and FA and MD images were calculated from eigenvalues of the tensors using Dipy.(23) DTI FA and MD images were registered to the T1-weighted image usi…”
Griffiths, J. D., McIntosh, A. R., & Lefebvre, J. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.575143 · Cited by 24
Tractome: a visual data mining tool for brain connectivity analysis
Porro, D., Olivetti, E., Sharmin, N., Nguyen, B. T., Garyfallidis, E., & Avesani, P. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-015-0408-z · Cited by 22
Self-citationBridgeford, E., Wang, S., Yang, Z., Wang, Z., Xu, T., Craddock, C., Dey, J. K., Kiar, G., Gray-Roncal, W., Colantuoni, C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/802629 · Cited by 22
In vivo diffusion MRI of the human heart using a 300 mT/m gradient system
Afzali, M., Mueller, L., Coveney, S., Fasano, F., Evans, C. J., Engel, M., Szczepankiewicz, F., Teh, I., Dall’Armellina, E., Jones, D. K., et al. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30118 · Cited by 22
“32-34 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.”
Morys, F., Kanyamibwa, A., Fängström, D., Tweedale, M., Pastor‐Bernier, A., Azizi, H., Liu, L., Horstmann, A., & Dagher, A. (2025) · npj Metabolic Health and Disease · doi:10.1038/s44324-025-00056-3 · Cited by 25
Neural function in DCC mutation carriers with and without mirror movements
Vosberg, D. E., Beaulé, V., Torres‐Berrío, A., Cooke, D., Chalupa, A., Jaworska, N., Cox, S. M. L., Larcher, K. M.-H., Zhang, Y., Allard, D., et al. (2019) · Annals of Neurology · doi:10.1002/ana.25418 · Cited by 22
“As for the rs-fcMRI data, a full factorial using the same design described below in the Statistical Analyses section was run in SPM12 for the fMRI 2ndlevel analyses, and family was entered as a fixed factor, because GEEs are not possible with SPM. Diffusion-Weighted Imaging of CC Images were denoised using NLMEANS den…”
MethodComparing perturbation models for evaluating stability of neuroimaging pipelines
Kiar, G., Castro, P. D. O., Rioux, P., Petit, E., Brown, S. T., Evans, A. C., & Tristan Glatard, B. (2020) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342020926237 · Cited by 22
“After pre-processing the raw diffusion data using FSL, structural connectomes were generated for an 83-region cortical and subcortical parcellation (Cammoun et al., 2012) using Dipy (Garyfallidis et al., 2014).”
MethodPieri, V., Sanvito, F., Riva, M., Petrini, A., Rancoita, P. M. V., Cirillo, S., Iadanza, A., Bello, L., Castellano, A., & Falini, A. (2020) · Human Brain Mapping · doi:10.1002/hbm.25291 · Cited by 21
“Diffusion imaging in Python (Dipy) software (Garyfallidis & Brett, 2014; Soares, Marques, Alves, & Sousa, 2013) was employed to perform tractography that was based on a q-ball residual bootstrap algorithm (Berman et al., 2008; Caverzasi F IGURE 1 Working pipeline.”
MethodColl‐Font, J., Chen, S., Eder, R. A., Fang, Y., Han, Q., van den Boomen, M., Sosnovik, D. E., Mekkaoui, C., & Nguyen, C. T. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28972 · Cited by 22
Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches
Thomopoulos, S. I., Nir, T. M., Villalón‐Reina, J. E., Zavaliangos‐Petropulu, A., Maiti, P., Zheng, H., Nourollahimoghadam, E., Jahanshad, N., Thompson, P. M., & Initiative, A. D. N. (2021) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2606337 · Cited by 22
Liu, Y., Wang, P., Wang, S., Zhang, H., Song, Y. S., Yan, X., & Gao, Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1202170 · Cited by 22
Early detection of white matter hyperintensities using SHIVA‐WMH detector
Tsuchida, A., Boutinaud, P., Verrecchia, V., Tzourio, C., Debette, S., & Joliot, M. (2023) · Human Brain Mapping · doi:10.1002/hbm.26548 · Cited by 23
“…Library (FSL, version 5.0.10: https://fsl.fmrib.ox.ac.uk/fsl) to correct for susceptibility and eddy-current distortion, then denoised using the nonlocal means filter (Coupe et al., 2008; Coupe et al., 2011) using nlmeans tool implemented in the Dipy package (0.12.0: Garyfallidis et al., 2014).”
MethodInfluentialRaj, A. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0905 · Cited by 21
Fixel-based analysis reveals tau-related white matter changes in early stages of Alzheimer's disease
Ahmadi, K., Pereira, J. B., Westen, D. V., Pasternak, O., Zhang, F., Nilsson, M., Stomrud, E., Spotorno, N., & Hansson, O. (2024) · The Journal of Neuroscience · doi:10.1523/jneurosci.0538-23.2024 · Cited by 25
Murray, K. D., Singh, M. V., Zhuang, Y., Uddin, M. N., Qiu, X., Weber, M. T., Tivarus, M. E., Wang, H. Z., Sahin, B., Zhong, J., et al. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.595463 · Cited by 19
Kern, K. C., Wright, C. B., & Leigh, R. J. (2022) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x221101644 · Cited by 21
“org).(39) DTI data was corrected for eddy current distortions and motion using FSL’s Eddy(40) and a tensor was fit at each voxel.”
MethodImaging biomarkers for early multiple system atrophy
Vemuri, P., Castillo, A. M., Thostenson, K. B., Ward, C. P., Raghavan, S., Reid, R. I., Lesnick, T. G., Reddy, A. L., Gehrking, T. L., Gehrking, J. A., et al. (2022) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2022.08.028 · Cited by 21
Increased myelination plays a central role in white matter neuroplasticity
Kirby, E. D., Frizzell, T. O., Grajauskas, L. A., Song, X., Gawryluk, J. R., Lakhani, B., Boyd, L. A., & D’Arcy, R. C. N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119644 · Cited by 22
Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions
Chen, G., Hong, Y., Huynh, K. M., & Yap, P. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102742 · Cited by 23
“GFA and DKI scalars were computed using Dipy (Garyfallidis et al., 2014).”
MethodRadiomic tractometry reveals tract-specific imaging biomarkers in white matter
Neher, P., Hirjak, D., & Maier-Hein, K. H. (2024) · Nature Communications · doi:10.1038/s41467-023-44591-3 · Cited by 23
“2.0) 47,77 – 82 .”
InfluentialMulti-stage Prediction Networks for Data Harmonization
Blumberg, S. B., Palombo, M., Khoo, C. S., Tax, C. M. W., Tanno, R., & Alexander, D. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_45 · Cited by 21
Gu, X., & Eklund, A. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00076 · Cited by 21
“The representation of diffusion weighting was achieved by a spherical harmonic fit of order n = 8 to the b = 1000 s/mm2 shell of the diffusion data, using theDipy (Garyfallidis et al., 2014) module reconst.”
MethodBergé, D., Mané, A., Lesh, T. A., Bioque, M., Barcones, F., González-Pinto, A. M., Parellada, M., Vieta, E., Castro‐Fornieles, J., Rodríguez–Jiménez, R., et al. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz132 · Cited by 20
Motovylyak, A., Vogt, N. M., Adluru, N., Ma, Y., Wang, R., Oh, J. M., Kecskemeti, S., Alexander, A. L., Dean, D., Gallagher, C. L., et al. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100030 · Cited by 23
“Fractional Anisotropy (FA) and Mean Diffusivity (MD) maps were derived by fitting a tensor at each voxel using Diffusion Imaging in Python [25].”
Method
Metadata
- 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
- Venue
- Frontiers in Neuroinformatics
- Publisher
- Frontiers Media SA
- Published
- 2014-02-21
- Volume
- 8
- Pages
- 8-8
- ISSN
- 1662-5196 (ISSN-L)
- 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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