Dataset Open Access

EDEN2020 Human Brain MRI Datasets for Healthy Volunteers

Castellano, Antonella; Pieri, Valentina; Galvan, Stefano; Rodriguez y Baena, Ferdinando; Falini, Andrea


JSON-LD (schema.org) Export

{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>High-resolution&nbsp; MR datasets of a cohort of 15 healthy adult subjects acquired on&nbsp; a&nbsp; 3T&nbsp; scanner&nbsp; at&nbsp; the Neuroradiology Unit and CERMAC (Center&nbsp; of Excellence for High Field Magnetic Resonance), Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy. The data includes:</p>\n\n<ul>\n\t<li>T1_3D_PROSET_Sag:&nbsp; T1-weighted&nbsp; volumetric&nbsp; sequence&nbsp; acquired&nbsp; on&nbsp; the sagittal plane for&nbsp; morphological&nbsp; characterization.&nbsp; This&nbsp; sequence&nbsp; demonstrates&nbsp; difference&nbsp; in&nbsp; the&nbsp; T1 relaxation time of tissues and provide excellent contrast between GM and WM.</li>\n\t<li>3D_FLAIR_Tra: Fluid\u2011Attenuated Inversion Recovery volumetric sequence acquired on the axial planefor morphological characterization. This is an inversion recovery sequence with a long inversion time (TI), which results in removing signal from the cerebrospinal fluid from the images.</li>\n\t<li>SWIp_axial: Susceptibility\u2011Weighted Imaging sequence acquired on the axial plane.This&nbsp; is&nbsp; a three-dimensional&nbsp; high-spatial&nbsp; resolution&nbsp; Gradient&nbsp; Echo&nbsp; MRI&nbsp; sequence providing excellent contrast for venous vascular modeling.</li>\n\t<li>s3DI_MC_HR: three\u2011dimensional&nbsp;&nbsp; high\u2011resolution&nbsp;&nbsp; time\u2011of\u2011flight&nbsp;&nbsp; (TOF)&nbsp;&nbsp; MR angiography&nbsp; acquisition&nbsp; to visualize&nbsp; flow&nbsp; within&nbsp; the&nbsp; arterial&nbsp; vessel.&nbsp; It&nbsp; is&nbsp; based&nbsp; on&nbsp; the phenomenon of flow-related enhancement of spins entering into an imaging slice. As a result of being unsaturated, these spins give more signal that surrounding stationary spins.</li>\n\t<li>MIP_s3DI_MC_HR:&nbsp; angiographic&nbsp; 3D&nbsp; visualization&nbsp; of&nbsp; TOF&nbsp; images&nbsp; using&nbsp; the maximum intensity projection (MIP) technique of reconstruction.</li>\n\t<li>raw_data_DTI_32: Diffusion Tensor Imaging raw data. This is a diffusion-weighted Spin&nbsp; Echo&nbsp; EPI&nbsp; single-shot&nbsp; pulse&nbsp; sequence&nbsp; acquired&nbsp; on&nbsp; the axial&nbsp; planealong&nbsp; 32&nbsp; gradient directions at a b-value of 1000 s/mm<sup>2</sup> and one volume without diffusion-weighting (b0 image).</li>\n\t<li>raw_data_NODDI: multi-compartmental dMRI sequence for advanced tractography and&nbsp; NODDI&nbsp; analyses,&nbsp; including&nbsp; an&nbsp; axial&nbsp; high&nbsp; angular&nbsp; resolution&nbsp; diffusion-weighted&nbsp; imaging (HARDI) acquisition along 60 gradient directions at a b-value of 3000 s/mm<sup>2</sup>,a DTI acquisition along 35 directions at a b-value of 711 s/mm<sup>2</sup> and 11 volumes without diffusion-weighting&nbsp; (b0&nbsp; images).&nbsp; The&nbsp; phase-encoding&nbsp; direction was &nbsp;anterior-to-posterior for all these acquisitions.</li>\n\t<li>B0_reverse: a sequence without diffusion-weighting having the same geometrical parameters of the &lsquo;raw_data_NODDI&rsquo; images, but acquired using a reversed phase-encoding direction&nbsp;(posterior-to-anterior).&nbsp;&nbsp; This&nbsp;&nbsp; volume&nbsp;&nbsp; allowed&nbsp; &nbsp;estimation&nbsp;&nbsp; and&nbsp;&nbsp; correction for susceptibility-induced distortions.</li>\n</ul>\n\n<p>&lsquo;DTI&rsquo; Folder&rsquo;: This folder contains the DTI-derived parametric maps calculated off-linefrom the &lsquo;raw_data_DTI_32&rsquo; acquisition (32 gradient directions, b-value = 1000 s/mm<sup>2</sup>) and saved in the NIfTI-1 Data Format.</p>\n\n<p>&lsquo;HARDI&rsquo; Folder: This folder contains the parametric maps calculated off-linefrom the HARDI acquisition (60 gradient directions, b-value = 3000 s/mm<sup>2</sup>) of the &lsquo;raw_data_NODDI&rsquo; sequence. Maps are saved in the NIfTI-1 Data Format.</p>\n\n<p>&lsquo;Tractography&rsquo; Folder: This&nbsp; folder&nbsp; contains&nbsp; the&nbsp; probabilistic&nbsp; tractography&nbsp; reconstructions&nbsp; of&nbsp; the&nbsp; main&nbsp; white&nbsp; matter&nbsp; fiber tracts,&nbsp; calculated&nbsp; from&nbsp; the&nbsp; HARDI&nbsp; acquisition (60&nbsp; gradient&nbsp; directions, b-value&nbsp; =&nbsp; 3000&nbsp; s/mm<sup>2</sup>)&nbsp; of&nbsp; the &lsquo;raw_data_NODDI&rsquo; sequence. Dipy has been used for q-ball residual-bootstrap fiber tracking.&nbsp;The folder contains a minimum number of two pair of tracts for each subjects.</p>\n\n<p>&lsquo;NODDI&rsquo; Folder: This folder contains the Neurite orientation dispersion and density imaging (NODDI) parametric maps calculated off-line from the &lsquo;raw_data_NODDI&rsquo; acquisition (60 gradient directions at b=3000 s/mm<sup>2</sup>, 35&nbsp; gradient&nbsp; directions&nbsp; at b=711&nbsp; s/mm<sup>2&nbsp;</sup>and&nbsp; 11&nbsp; b0&nbsp; volumes)&nbsp; and&nbsp; saved&nbsp; in&nbsp; the&nbsp; NIfTI-1&nbsp; Data&nbsp; Format. Maps have been generated using the NODDI Matlab Toolbox (https://www.nitrc.org/projects/noddi_toolbox).</p>\n\n<p>Note that all MRI data files were converted from DICOM series using&nbsp;Chris Rorden&#39;s dcm2niiX version v1.0.20200331.</p>\n\n<p>&nbsp;</p>", 
  "license": "https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy", 
      "@id": "https://orcid.org/0000-0002-4137-9016", 
      "@type": "Person", 
      "name": "Castellano, Antonella"
    }, 
    {
      "affiliation": "Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy", 
      "@id": "https://orcid.org/0000-0001-9244-3834", 
      "@type": "Person", 
      "name": "Pieri, Valentina"
    }, 
    {
      "affiliation": "Imperial College London", 
      "@id": "https://orcid.org/0000-0001-9337-4156", 
      "@type": "Person", 
      "name": "Galvan, Stefano"
    }, 
    {
      "affiliation": "Imperial College London", 
      "@id": "https://orcid.org/0000-0002-5199-9083", 
      "@type": "Person", 
      "name": "Rodriguez y Baena, Ferdinando"
    }, 
    {
      "affiliation": "Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy", 
      "@id": "https://orcid.org/0000-0002-1461-8755", 
      "@type": "Person", 
      "name": "Falini, Andrea"
    }
  ], 
  "url": "https://zenodo.org/record/3994749", 
  "datePublished": "2019-07-16", 
  "version": "1.0", 
  "keywords": [
    "MRI", 
    "HARDI", 
    "NODDI", 
    "DTI", 
    "Brain"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_0866.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_0933.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_1277.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_1686.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_2020.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_2476.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_3352.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_4871.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_5069.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_5361.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_5900.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_5960.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_6685.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_8275.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/CTRL_9647.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/568ad2e2-1af2-40e2-a24e-07061d849edd/Readme.md", 
      "encodingFormat": "md", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.3994749", 
  "@id": "https://doi.org/10.5281/zenodo.3994749", 
  "@type": "Dataset", 
  "name": "EDEN2020 Human Brain MRI Datasets for Healthy Volunteers"
}
595
8
views
downloads
All versions This version
Views 59541
Downloads 87
Data volume 2.8 GB2.3 GB
Unique views 52437
Unique downloads 54

Share

Cite as