Dataset Open Access

EDEN2020 Human Brain MRI Datasets for Healthy Volunteers

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


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3994749</identifier>
  <creators>
    <creator>
      <creatorName>Castellano, Antonella</creatorName>
      <givenName>Antonella</givenName>
      <familyName>Castellano</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4137-9016</nameIdentifier>
      <affiliation>Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Pieri, Valentina</creatorName>
      <givenName>Valentina</givenName>
      <familyName>Pieri</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9244-3834</nameIdentifier>
      <affiliation>Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Galvan, Stefano</creatorName>
      <givenName>Stefano</givenName>
      <familyName>Galvan</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9337-4156</nameIdentifier>
      <affiliation>Imperial College London</affiliation>
    </creator>
    <creator>
      <creatorName>Rodriguez y Baena, Ferdinando</creatorName>
      <givenName>Ferdinando</givenName>
      <familyName>Rodriguez y Baena</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5199-9083</nameIdentifier>
      <affiliation>Imperial College London</affiliation>
    </creator>
    <creator>
      <creatorName>Falini, Andrea</creatorName>
      <givenName>Andrea</givenName>
      <familyName>Falini</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1461-8755</nameIdentifier>
      <affiliation>Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy</affiliation>
    </creator>
  </creators>
  <titles>
    <title>EDEN2020 Human Brain MRI Datasets for Healthy Volunteers</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>MRI</subject>
    <subject>HARDI</subject>
    <subject>NODDI</subject>
    <subject>DTI</subject>
    <subject>Brain</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-07-16</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3994749</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3338449</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/eden2020</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;High-resolution&amp;nbsp; MR datasets of a cohort of 15 healthy adult subjects acquired on&amp;nbsp; a&amp;nbsp; 3T&amp;nbsp; scanner&amp;nbsp; at&amp;nbsp; the Neuroradiology Unit and CERMAC (Center&amp;nbsp; of Excellence for High Field Magnetic Resonance), Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy. The data includes:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;T1_3D_PROSET_Sag:&amp;nbsp; T1-weighted&amp;nbsp; volumetric&amp;nbsp; sequence&amp;nbsp; acquired&amp;nbsp; on&amp;nbsp; the sagittal plane for&amp;nbsp; morphological&amp;nbsp; characterization.&amp;nbsp; This&amp;nbsp; sequence&amp;nbsp; demonstrates&amp;nbsp; difference&amp;nbsp; in&amp;nbsp; the&amp;nbsp; T1 relaxation time of tissues and provide excellent contrast between GM and WM.&lt;/li&gt;
	&lt;li&gt;3D_FLAIR_Tra: Fluid‑Attenuated 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.&lt;/li&gt;
	&lt;li&gt;SWIp_axial: Susceptibility‑Weighted Imaging sequence acquired on the axial plane.This&amp;nbsp; is&amp;nbsp; a three-dimensional&amp;nbsp; high-spatial&amp;nbsp; resolution&amp;nbsp; Gradient&amp;nbsp; Echo&amp;nbsp; MRI&amp;nbsp; sequence providing excellent contrast for venous vascular modeling.&lt;/li&gt;
	&lt;li&gt;s3DI_MC_HR: three‑dimensional&amp;nbsp;&amp;nbsp; high‑resolution&amp;nbsp;&amp;nbsp; time‑of‑flight&amp;nbsp;&amp;nbsp; (TOF)&amp;nbsp;&amp;nbsp; MR angiography&amp;nbsp; acquisition&amp;nbsp; to visualize&amp;nbsp; flow&amp;nbsp; within&amp;nbsp; the&amp;nbsp; arterial&amp;nbsp; vessel.&amp;nbsp; It&amp;nbsp; is&amp;nbsp; based&amp;nbsp; on&amp;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.&lt;/li&gt;
	&lt;li&gt;MIP_s3DI_MC_HR:&amp;nbsp; angiographic&amp;nbsp; 3D&amp;nbsp; visualization&amp;nbsp; of&amp;nbsp; TOF&amp;nbsp; images&amp;nbsp; using&amp;nbsp; the maximum intensity projection (MIP) technique of reconstruction.&lt;/li&gt;
	&lt;li&gt;raw_data_DTI_32: Diffusion Tensor Imaging raw data. This is a diffusion-weighted Spin&amp;nbsp; Echo&amp;nbsp; EPI&amp;nbsp; single-shot&amp;nbsp; pulse&amp;nbsp; sequence&amp;nbsp; acquired&amp;nbsp; on&amp;nbsp; the axial&amp;nbsp; planealong&amp;nbsp; 32&amp;nbsp; gradient directions at a b-value of 1000 s/mm&lt;sup&gt;2&lt;/sup&gt; and one volume without diffusion-weighting (b0 image).&lt;/li&gt;
	&lt;li&gt;raw_data_NODDI: multi-compartmental dMRI sequence for advanced tractography and&amp;nbsp; NODDI&amp;nbsp; analyses,&amp;nbsp; including&amp;nbsp; an&amp;nbsp; axial&amp;nbsp; high&amp;nbsp; angular&amp;nbsp; resolution&amp;nbsp; diffusion-weighted&amp;nbsp; imaging (HARDI) acquisition along 60 gradient directions at a b-value of 3000 s/mm&lt;sup&gt;2&lt;/sup&gt;,a DTI acquisition along 35 directions at a b-value of 711 s/mm&lt;sup&gt;2&lt;/sup&gt; and 11 volumes without diffusion-weighting&amp;nbsp; (b0&amp;nbsp; images).&amp;nbsp; The&amp;nbsp; phase-encoding&amp;nbsp; direction was &amp;nbsp;anterior-to-posterior for all these acquisitions.&lt;/li&gt;
	&lt;li&gt;B0_reverse: a sequence without diffusion-weighting having the same geometrical parameters of the &amp;lsquo;raw_data_NODDI&amp;rsquo; images, but acquired using a reversed phase-encoding direction&amp;nbsp;(posterior-to-anterior).&amp;nbsp;&amp;nbsp; This&amp;nbsp;&amp;nbsp; volume&amp;nbsp;&amp;nbsp; allowed&amp;nbsp; &amp;nbsp;estimation&amp;nbsp;&amp;nbsp; and&amp;nbsp;&amp;nbsp; correction for susceptibility-induced distortions.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&amp;lsquo;DTI&amp;rsquo; Folder&amp;rsquo;: This folder contains the DTI-derived parametric maps calculated off-linefrom the &amp;lsquo;raw_data_DTI_32&amp;rsquo; acquisition (32 gradient directions, b-value = 1000 s/mm&lt;sup&gt;2&lt;/sup&gt;) and saved in the NIfTI-1 Data Format.&lt;/p&gt;

&lt;p&gt;&amp;lsquo;HARDI&amp;rsquo; Folder: This folder contains the parametric maps calculated off-linefrom the HARDI acquisition (60 gradient directions, b-value = 3000 s/mm&lt;sup&gt;2&lt;/sup&gt;) of the &amp;lsquo;raw_data_NODDI&amp;rsquo; sequence. Maps are saved in the NIfTI-1 Data Format.&lt;/p&gt;

&lt;p&gt;&amp;lsquo;Tractography&amp;rsquo; Folder: This&amp;nbsp; folder&amp;nbsp; contains&amp;nbsp; the&amp;nbsp; probabilistic&amp;nbsp; tractography&amp;nbsp; reconstructions&amp;nbsp; of&amp;nbsp; the&amp;nbsp; main&amp;nbsp; white&amp;nbsp; matter&amp;nbsp; fiber tracts,&amp;nbsp; calculated&amp;nbsp; from&amp;nbsp; the&amp;nbsp; HARDI&amp;nbsp; acquisition (60&amp;nbsp; gradient&amp;nbsp; directions, b-value&amp;nbsp; =&amp;nbsp; 3000&amp;nbsp; s/mm&lt;sup&gt;2&lt;/sup&gt;)&amp;nbsp; of&amp;nbsp; the &amp;lsquo;raw_data_NODDI&amp;rsquo; sequence. Dipy has been used for q-ball residual-bootstrap fiber tracking.&amp;nbsp;The folder contains a minimum number of two pair of tracts for each subjects.&lt;/p&gt;

&lt;p&gt;&amp;lsquo;NODDI&amp;rsquo; Folder: This folder contains the Neurite orientation dispersion and density imaging (NODDI) parametric maps calculated off-line from the &amp;lsquo;raw_data_NODDI&amp;rsquo; acquisition (60 gradient directions at b=3000 s/mm&lt;sup&gt;2&lt;/sup&gt;, 35&amp;nbsp; gradient&amp;nbsp; directions&amp;nbsp; at b=711&amp;nbsp; s/mm&lt;sup&gt;2&amp;nbsp;&lt;/sup&gt;and&amp;nbsp; 11&amp;nbsp; b0&amp;nbsp; volumes)&amp;nbsp; and&amp;nbsp; saved&amp;nbsp; in&amp;nbsp; the&amp;nbsp; NIfTI-1&amp;nbsp; Data&amp;nbsp; Format. Maps have been generated using the NODDI Matlab Toolbox (https://www.nitrc.org/projects/noddi_toolbox).&lt;/p&gt;

&lt;p&gt;Note that all MRI data files were converted from DICOM series using&amp;nbsp;Chris Rorden&amp;#39;s dcm2niiX version v1.0.20200331.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/688279/">688279</awardNumber>
      <awardTitle>Enhanced Delivery Ecosystem for Neurosurgery in 2020</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
594
8
views
downloads
All versions This version
Views 59440
Downloads 87
Data volume 2.8 GB2.3 GB
Unique views 52336
Unique downloads 54

Share

Cite as