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3D Dataset "Computation of Exact g-Factor Maps in 3D GRAPPA Reconstructions"

Rabanillo, Iñaki; Zhu, Ante; Aja-Fernández, Santiago; Alberola-López, Carlos; Hernando, Diego


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  <identifier identifierType="DOI">10.5281/zenodo.1283312</identifier>
  <creators>
    <creator>
      <creatorName>Rabanillo, Iñaki</creatorName>
      <givenName>Iñaki</givenName>
      <familyName>Rabanillo</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5309-6446</nameIdentifier>
      <affiliation>Universidad de Valladolid</affiliation>
    </creator>
    <creator>
      <creatorName>Zhu, Ante</creatorName>
      <givenName>Ante</givenName>
      <familyName>Zhu</familyName>
      <affiliation>University of Wisconsin-Madison</affiliation>
    </creator>
    <creator>
      <creatorName>Aja-Fernández, Santiago</creatorName>
      <givenName>Santiago</givenName>
      <familyName>Aja-Fernández</familyName>
      <affiliation>Universidad de Valladolid</affiliation>
    </creator>
    <creator>
      <creatorName>Alberola-López, Carlos</creatorName>
      <givenName>Carlos</givenName>
      <familyName>Alberola-López</familyName>
      <affiliation>Universidad de VAlladolid</affiliation>
    </creator>
    <creator>
      <creatorName>Hernando, Diego</creatorName>
      <givenName>Diego</givenName>
      <familyName>Hernando</familyName>
      <affiliation>University of Wisconsin-Madison</affiliation>
    </creator>
  </creators>
  <titles>
    <title>3D Dataset "Computation of Exact g-Factor Maps in 3D GRAPPA Reconstructions"</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Phantom</subject>
    <subject>MRI</subject>
    <subject>Noise</subject>
    <subject>GRAPPA</subject>
    <subject>Parallel Imaging</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-06-05</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1283312</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1283311</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Datasets used in the paper entitled &amp;quot;&amp;quot;, containing the following acquisitions:&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;strong&gt;1) Simulated abdomen data set&lt;/strong&gt;: we have synthetized a 3D volume using the simulation environment XCAT based on the extended cardio-torso phantom. We simulated a T1-weighted acquisition using the following acquisition parameters: TE/TR=1.5/3ms, flip angle=60&amp;ordm;, acquisition matrix size=60x60x32. A 32-coil acquisition was simulated by modulating the image using artificial sensitivity maps coded for each coil. The noise-free coil images were transformed into the \bk--space and corrupted with synthetic Gaussian noise characterized by the matrices &lt;span class="math-tex"&gt;\(\Gamma_k\)&lt;/span&gt;and&amp;nbsp;&lt;span class="math-tex"&gt;\(C_k\)&lt;/span&gt; with SNR=25 for each coil, and the correlation coefficient between coils was set to &lt;span class="math-tex"&gt;\(\rho\)&lt;/span&gt;=0.1$. For statistical purposes, 4000 realizations of each image were used.&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;strong&gt;2) Water phantom acquisition&lt;/strong&gt;: A MR phantom sphere with solution (GE Medical Systems, Milwaukee, WI) was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition with 100 realizations of the same fully-encoded k-space sampling was used. Acquisition parameters included: coronal view, TE/TR=0.96/3.69ms, flip angle=12&amp;ordm;, field of view=22x22$x30.7&lt;span class="math-tex"&gt;\(cm³\)&lt;/span&gt;, acquisition matrix size=60x60x32, bandwidth=62.5KHz. We corrected for &lt;span class="math-tex"&gt;\(B_0\)&lt;/span&gt; field drift related phase variations and magnitude decay by a pre-processing step. First we estimated the phase-shift between realizations from the center of the k-space as a cubic function of time and removed it afterwards. And, second, we estimated the magnitude-decay in the k-space as a linear function and substracted it in order not to affect the noise.&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;strong&gt;3) In vivo acquisition&lt;/strong&gt;: in order to assess the feasibility of the proposed method, after obtaining the approval fo the local institutional review board (IRB), a volunteer was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition of a fully-encoded \bk--space sampling was used. Acquisition parameters included: coronal view, TE/TR=2.2/5.7ms, flip angle=12&amp;ordm;,field of view=22x22x22&lt;span class="math-tex"&gt;\(cm³\)&lt;/span&gt;, matrix size=220x220x220, bandwidth=62.5$KHz.&lt;/p&gt;</description>
    <description descriptionType="Other">The authors acknowledge MICIN for grants TEC2013-44194P, TEC 2014-57428 and TEC2017-82408-R, as well as Junta de Castilla y León for grant VA069U16. The first author acknowledges MINECO for FPI grant BES-2014-069524.</description>
  </descriptions>
</resource>
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