Data

Contents

Datasets

Details about datasets available in DIPY are described in the table below:

Name Synthetic/Phantom/Human/Animal Data features (structural; diffusion; label information) Scanner DIPY name Citations
Tractogram file formats examples Synthetic Tractogram file formats (`.dpy`, `.fib`, `.tck`, `.trk`) bundle_file_formats_example Rheault, F. (2019). Bundles for tractography file format testing and example (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3352379
CENIR HCP-like dataset Multi-shell data: b-vals: [200, 400, 1000, 2000, 3000] (s/mm^2); [20, 20, 202, 204, 206] gradient directions; Corrected for Eddy currents cenir_multib
CFIN dataset T1; Multi-shell data: b-vals: [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000] (s/mm^2); 496 gradient directions cfin_multib Hansen, B., Jespersen, S.. Data for evaluation of fast kurtosis strategies, b-value optimization and exploration of diffusion MRI contrast. Sci Data 3, 160072 (2016). doi:10.1038/sdata.2016.72
Gold standard streamlines IO testing Synthetic Tractogram file formats (`.dpy`, `.fib`, `.tck`, `.trk`) gold_standard_io Rheault, F. (2019). Gold standard for tractogram io testing (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2651349
HCP842 bundle atlas Human Whole brain/bundle-wise tractograms in MNI space; 80 bundles Human Connectome Project (HCP) scanner bundle_atlas_hcp842 Garyfallidis, E., et al. Recognition of white matter bundles using local and global streamline-based registration and clustering. NeuroImage 170 (2017): 283-297; Yeh, F.-C., et al. Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage 178 (2018): 57-68. figshare.com/articles/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375883
HCP bundle FA Human Fractional Anisotropy (FA); 2 bundles bundle_fa_hcp
HCP tractogram Human/td> Whole brain tractogram Human Connectome Project (HCP) scanner target_tractogram_hcp
ISBI 2013 Phantom Multi shell data: b-vals: [0, 1500, 2500] (s/mm^2); 64 gradient directions isbi2013_2shell Daducci, A., et al. Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI. IEEE Transactions on Medical Imaging, vol. 33, no. 2, pp. 384-399, Feb. 2014. HARDI reconstruction challenge 2013
IVIM dataset Human Multi shell data: b-vals: [0, 10, 20, 30, 40, 60, 80, 100, 120, 140, 160, 180, 200, 300, 400, 500, 600, 700, 800, 900, 1000] (s/mm^2); 21 gradient directions fetch_ivim
MNI template Human MNI 2009a T1, T2; 2009c T1, T1 mask mni_template Fonov, V.S., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L., BDCG. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, Volume 54, Issue 1, January 2011, ISSN 1053–8119, doi:10.1016/j.neuroimage.2010.07.033; Fonov, V.S., Evans, A.C., McKinstry, R.C., Almli, C.R., Collins, D.L. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood, NeuroImage, Volume 47, Supplement 1, July 2009, Page S102 Organization for Human Brain Mapping 2009 Annual Meeting, doi:10.1016/S1053-8119(09)70884-5 ICBM 152 Nonlinear atlases version 2009
qt-dMRI C57Bl6 mice dataset Animal 2 C57Bl6 mice test-retest qt-dMRI; Corpus callosum (CC) bundle masks qtdMRI_test_retest_2subjects Wassermann, D., Santin, M., Philippe, A.-C., Fick, R., Deriche, R., Lehericy, S., Petiet, A. (2017). Test-Retest qt-dMRI datasets for "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.996889
SCIL b0 b0 GE (1.5, 3 T), Philips (3 T); Siemens (1.5, 3 T) scil_b0 Sherbrooke Connectivity Imaging Lab (SCIL)
Sherbrooke 3 shells Human Multi shell data: b-vals: [0, 1000, 2000; 3500] (s/mm^2); 193 gradient directions sherbrooke_3shell Sherbrooke Connectivity Imaging Lab (SCIL)
SNAIL dataset 2 subjects: T1; Fractional Anisotropy (FA); 27 bundles bundles_2_subjects
Stanford HARDI Human HARDI-like multi-shell data: b-vals: [0, 2000] (s/mm^2); 160 gradient directions GE Discovery MR750 stanford_hardi Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths. Rokem, A., Yeatman, J.D., Pestilli, F., Kay, K.N., Mezer A., van der Walt, S., and Wandell, B.A. (2015) Evaluating the Accuracy of Diffusion MRI Models in White Matter. PLoS ONE 10(4): e0123272. doi:10.1371/journal.pone.0123272
Stanford labels Human Gray matter region labels GE Discovery MR750 stanford_labels Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths. Rokem, A., Yeatman, J.D., Pestilli, F., Kay, K.N., Mezer A., van der Walt, S., and Wandell, B.A. (2015) Evaluating the Accuracy of Diffusion MRI Models in White Matter. PLoS ONE 10(4): e0123272. doi:10.1371/journal.pone.0123272
Stanford PVE maps Human Partial Volume Effects (PVE) maps: Gray matter (GM), White matter (WM); Cerebrospinal Fluid (CSF) GE Discovery MR750 fetch_stanford_pve_maps Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths. Rokem, A., Yeatman, J.D., Pestilli, F., Kay, K.N., Mezer A., van der Walt, S., and Wandell, B.A. (2015) Evaluating the Accuracy of Diffusion MRI Models in White Matter. PLoS ONE 10(4): e0123272. doi:10.1371/journal.pone.0123272
Stanford T1 Human T1 GE Discovery MR750 stanford_t1 Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths. Rokem, A., Yeatman, J.D., Pestilli, F., Kay, K.N., Mezer A., van der Walt, S., and Wandell, B.A. (2015) Evaluating the Accuracy of Diffusion MRI Models in White Matter. PLoS ONE 10(4): e0123272. doi:10.1371/journal.pone.0123272
SyN data Human T1; b0 syn_data
Taiwan NTU DSI DSI-like data; Multi-shell data: b-vals: [0, 308 ,615, 923, 1231, 1538, 1538, 1846, 1846, 2462, 2769, 3077, 3385, 3692, 4000] (s/mm^2); 203 gradient directions Siemens Trio taiwan_ntu_dsi National Taiwan University (NTU) Hospital Advanced Biomedical MRI Lab DSI MRI data
Tissue data Human T1; denoised T1; Power map tissue_data

The list of datasets can be retrieved using:

from dipy.workflows.io import FetchFlow

available_data = FetchFlow.get_fetcher_datanames().keys()

To retrieve all datasets, the following workflow can be run:

from dipy.workflows.io import FetchFlow

fetch_flow = FetchFlow()

with TemporaryDirectory() as out_dir:
    fetch_flow.run(['all'])

If you want to download a particular dataset, you can do:

from dipy.workflows.io import FetchFlow

fetch_flow = FetchFlow()

with TemporaryDirectory() as out_dir:
    fetch_flow.run(['bundle_fa_hcp'])

or:

from dipy.data import fetch_bundle_fa_hcp

files, folder = fetch_bundle_fa_hcp()