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<meta name="DC.Title" content="Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas" />
<meta name="DC.Identifier" content="10.3174/ajnr.A5027" />
<meta name="DC.Date" content="2017-03-01" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: DSC perfusion MR imaging assumes that the contrast agent remains intravascular; thus, disruptions in the blood-brain barrier common in brain tumors can lead to errors in the estimation of relative CBV. Acquisition strategies, including the choice of flip angle, TE, TR, and preload dose and incubation time, along with post hoc leakage-correction algorithms, have been proposed as means for combating these leakage effects. In the current study, we used DSC-MR imaging simulations to examine the influence of these various acquisition parameters and leakage-correction strategies on the faithful estimation of CBV.

MATERIALS AND METHODS: DSC-MR imaging simulations were performed in 250 tumors with perfusion characteristics randomly generated from the distributions of real tumor population data, and comparison of leakage-corrected CBV was performed with a theoretic curve with no permeability. Optimal strategies were determined by protocol with the lowest mean error.

RESULTS: The following acquisition strategies (flip angle/TE/TR and contrast dose allocation for preload and bolus) produced high CBV fidelity, as measured by the percentage difference from a hypothetic tumor with no leakage: 1) 35°/35 ms/1.5 seconds with no preload and full dose for DSC-MR imaging, 2) 35°/25 ms/1.5 seconds with ¼ dose preload and ¾ dose bolus, 3) 60°/35 ms/2.0 seconds with ½ dose preload and ½ dose bolus, and 4) 60°/35 ms/1.0 second with 1 dose preload and 1 dose bolus.

CONCLUSIONS: Results suggest that a variety of strategies can yield similarly high fidelity in CBV estimation, namely those that balance T1- and T2*-relaxation effects due to contrast agent extravasation.

AIF
:   arterial input function
Bidir
:   bidirectional leakage correction algorithm
CNR
:   contrast-to-noise ratio
EES
:   extravascular extracellular space
K trans
:   efflux rate of contrast agent from the vasculature
rCBV
:   relative CBV
Unidir
:   unidirectional leakage correction algorithm" />
<meta name="DC.Contributor" content="K. Leu" />
<meta name="DC.Contributor" content="J.L. Boxerman" />
<meta name="DC.Contributor" content="B.M. Ellingson" />
<meta name="article:published_time" content="2017-03-01" />
<meta name="article:section" content="ADULT BRAIN" />
<meta name="citation_title" content="Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;DSC perfusion MR imaging assumes that the contrast agent remains intravascular; thus, disruptions in the blood-brain barrier common in brain tumors can lead to errors in the estimation of relative CBV. Acquisition strategies, including the choice of flip angle, TE, TR, and preload dose and incubation time, along with post hoc leakage-correction algorithms, have been proposed as means for combating these leakage effects. In the current study, we used DSC-MR imaging simulations to examine the influence of these various acquisition parameters and leakage-correction strategies on the faithful estimation of CBV.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;DSC-MR imaging simulations were performed in 250 tumors with perfusion characteristics randomly generated from the distributions of real tumor population data, and comparison of leakage-corrected CBV was performed with a theoretic curve with no permeability. Optimal strategies were determined by protocol with the lowest mean error.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The following acquisition strategies (flip angle/TE/TR and contrast dose allocation for preload and bolus) produced high CBV fidelity, as measured by the percentage difference from a hypothetic tumor with no leakage: 1) 35°/35 ms/1.5 seconds with no preload and full dose for DSC-MR imaging, 2) 35°/25 ms/1.5 seconds with ¼ dose preload and ¾ dose bolus, 3) 60°/35 ms/2.0 seconds with ½ dose preload and ½ dose bolus, and 4) 60°/35 ms/1.0 second with 1 dose preload and 1 dose bolus.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Results suggest that a variety of strategies can yield similarly high fidelity in CBV estimation, namely those that balance T1- and T2*-relaxation effects due to contrast agent extravasation.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_publication_date" content="2017/03/01" />
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<meta name="citation_issn" content="0195-6108" />
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<meta name="citation_author" content="K. Leu" />
<meta name="citation_author_institution" content="aFrom the University of California, Los Angeles Brain Tumor Imaging Laboratory (K.A.B.L., B.M.E.), Center for Computer Vision and Imaging Biomarkers" />
<meta name="citation_author_institution" content="bDepartment of Bioengineering (K.A.B.L., B.M.E.), Henry Samueli School of Engineering and Applied Science" />
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<meta name="citation_author" content="J.L. Boxerman" />
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<meta name="citation_author" content="B.M. Ellingson" />
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<meta name="twitter:title" content="Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: DSC perfusion MR imaging assumes that the contrast agent remains intravascular; thus, disruptions in the blood-brain barrier common in brain tumors can lead to errors in the estimation of relative CBV. Acquisition strategies, including the choice of flip angle, TE, TR, and preload dose and incubation time, along with post hoc leakage-correction algorithms, have been proposed as means for combating these leakage effects. In the current study, we used DSC-MR imaging simulations to examine the influence of these various acquisition parameters and leakage-correction strategies on the faithful estimation of CBV.

MATERIALS AND METHODS: DSC-MR imaging simulations were performed in 250 tumors with perfusion characteristics randomly generated from the distributions of real tumor population data, and comparison of leakage-corrected CBV was performed with a theoretic curve with no permeability. Optimal strategies were determined by protocol with the lowest mean error.

RESULTS: The following acquisition strategies (flip angle/TE/TR and contrast dose allocation for preload and bolus) produced high CBV fidelity, as measured by the percentage difference from a hypothetic tumor with no leakage: 1) 35°/35 ms/1.5 seconds with no preload and full dose for DSC-MR imaging, 2) 35°/25 ms/1.5 seconds with ¼ dose preload and ¾ dose bolus, 3) 60°/35 ms/2.0 seconds with ½ dose preload and ½ dose bolus, and 4) 60°/35 ms/1.0 second with 1 dose preload and 1 dose bolus.

CONCLUSIONS: Results suggest that a variety of strategies can yield similarly high fidelity in CBV estimation, namely those that balance T1- and T2*-relaxation effects due to contrast agent extravasation.

AIF
:   arterial input function
Bidir
:   bidirectional leakage correction algorithm
CNR
:   contrast-to-noise ratio
EES
:   extravascular extracellular space
K trans
:   efflux rate of contrast agent from the vasculature
rCBV
:   relative CBV
Unidir
:   unidirectional leakage correction algorithm" />
<meta name="og-title" property="og:title" content="Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas" />
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<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: DSC perfusion MR imaging assumes that the contrast agent remains intravascular; thus, disruptions in the blood-brain barrier common in brain tumors can lead to errors in the estimation of relative CBV. Acquisition strategies, including the choice of flip angle, TE, TR, and preload dose and incubation time, along with post hoc leakage-correction algorithms, have been proposed as means for combating these leakage effects. In the current study, we used DSC-MR imaging simulations to examine the influence of these various acquisition parameters and leakage-correction strategies on the faithful estimation of CBV.

MATERIALS AND METHODS: DSC-MR imaging simulations were performed in 250 tumors with perfusion characteristics randomly generated from the distributions of real tumor population data, and comparison of leakage-corrected CBV was performed with a theoretic curve with no permeability. Optimal strategies were determined by protocol with the lowest mean error.

RESULTS: The following acquisition strategies (flip angle/TE/TR and contrast dose allocation for preload and bolus) produced high CBV fidelity, as measured by the percentage difference from a hypothetic tumor with no leakage: 1) 35°/35 ms/1.5 seconds with no preload and full dose for DSC-MR imaging, 2) 35°/25 ms/1.5 seconds with ¼ dose preload and ¾ dose bolus, 3) 60°/35 ms/2.0 seconds with ½ dose preload and ½ dose bolus, and 4) 60°/35 ms/1.0 second with 1 dose preload and 1 dose bolus.

CONCLUSIONS: Results suggest that a variety of strategies can yield similarly high fidelity in CBV estimation, namely those that balance T1- and T2*-relaxation effects due to contrast agent extravasation.

AIF
:   arterial input function
Bidir
:   bidirectional leakage correction algorithm
CNR
:   contrast-to-noise ratio
EES
:   extravascular extracellular space
K trans
:   efflux rate of contrast agent from the vasculature
rCBV
:   relative CBV
Unidir
:   unidirectional leakage correction algorithm" />
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    <title>Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas | American Journal of Neuroradiology</title>  
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node14659--5" data-node-nid="14659" id="top-node-14659--61502534962" data-pisa="ajnr;38/3/478" data-pisa-master="ajnr;ajnr.A5027" data-apath="/ajnr/38/3/478.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">ADULT BRAIN</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">K. Leu</span>, <span class="highwire-citation-author" data-delta="1">J.L. Boxerman</span> and <span class="highwire-citation-author" data-delta="2">B.M. Ellingson</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">March 2017,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">38 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(3) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">478-484; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A5027 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-14659--61502534962" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">K. Leu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the University of California, Los Angeles Brain Tumor Imaging Laboratory (K.A.B.L., B.M.E.), Center for Computer Vision and Imaging Biomarkers</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Bioengineering (K.A.B.L., B.M.E.), Henry Samueli School of Engineering and Applied Science</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Departments of Radiological Sciences (A.B.L., B.M.E.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=K.%2BLeu%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Leu%20K&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AK.%2BLeu%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="http://orcid.org/0000-0002-0523-1575" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for K. Leu</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">J.L. Boxerman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=J.L.%2BBoxerman%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Boxerman%20J&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AJ.L.%2BBoxerman%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="http://orcid.org/0000-0002-0594-1542" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J.L. Boxerman</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">B.M. Ellingson </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the University of California, Los Angeles Brain Tumor Imaging Laboratory (K.A.B.L., B.M.E.), Center for Computer Vision and Imaging Biomarkers</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Bioengineering (K.A.B.L., B.M.E.), Henry Samueli School of Engineering and Applied Science</div><div class='author-affiliation'><span class='nlm-sup'>c</span>University of California, Los Angeles Neuro-Oncology Program (B.M.E.), University of California, Los Angeles, Los Angeles, California</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Departments of Radiological Sciences (A.B.L., B.M.E.)</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Biomedical Physics (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=B.M.%2BEllingson%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Ellingson%20B&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AB.M.%2BEllingson%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="http://orcid.org/0000-0002-2764-6640" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B.M. 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 38</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 3 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Mar 2017 </div></div>
      
      
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      <div class="highwire-cite-title">Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">K.</span> <span class="nlm-surname">Leu</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">J.L.</span> <span class="nlm-surname">Boxerman</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">B.M.</span> <span class="nlm-surname">Ellingson</span></span></span></div>
  
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      <div class="highwire-cite-title">Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas</div>  
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    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Mar 2017, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">38 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(3) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">478-484; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A5027 </span></div>
  
  
  
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