Data#
How to get 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()
Datasets List#
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. https://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. https://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. <a href=’https://figshare.com/articles/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375883’>figshare.com/articles/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375883</a> |
HCP bundle FA |
Human |
Fractional Anisotropy (FA); 2 bundles |
bundle_fa_hcp |
||
HCP tractogram |
Human |
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. <a href=’http://hardi.epfl.ch/static/events/2013_ISBI/testing_data.html’>HARDI reconstruction challenge 2013</a> |
|
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 |
Peterson, Eric (2016): IVIM dataset. figshare. Dataset. <a href=’https://doi.org/10.6084/m9.figshare.3395704.v1’>figshare.com/articles/dataset/IVIM_dataset/3395704/1</a> |
|
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 <a href=’https://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009’>ICBM 152 Nonlinear atlases version 2009</a> |
|
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. https://doi.org/10.5281/zenodo.996889 |
|
SCIL b0 |
b0 |
GE (1.5, 3 T), Philips (3 T); Siemens (1.5, 3 T) |
scil_b0 |
<a href=’http://scil.dinf.usherbrooke.ca’>Sherbrooke Connectivity Imaging Lab (SCIL)</a> |
|
Sherbrooke 3 shells |
Human |
Multi shell data: b-vals: [0, 1000, 2000; 3500] (s/mm^2); 193 gradient directions |
sherbrooke_3shell |
<a href=’http://scil.dinf.usherbrooke.ca’>Sherbrooke Connectivity Imaging Lab (SCIL)</a> |
|
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 |
<a href=’https://purl.stanford.edu/ng782rw8378’>Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths</a>. 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 |
<a href=’https://purl.stanford.edu/ng782rw8378’>Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths</a>. 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 |
<a href=’https://purl.stanford.edu/ng782rw8378’>Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths</a>. 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 |
<a href=’https://purl.stanford.edu/ng782rw8378’>Human brain diffusion-weighted MRI, collected with high diffusion-weighting angular resolution and repeated measurements at multiple diffusion-weighting strengths</a>. 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 |