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<meta name="DC.Title" content="Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma" />
<meta name="DC.Identifier" content="10.3174/ajnr.A4451" />
<meta name="DC.Date" content="2015-12-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2015 by American Journal of Neuroradiology. Indicates open access to non-subscribers at www.ajnr.org" />
<meta name="DC.AccessRights" content="open-access" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.

MATERIALS AND METHODS: This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose– and preload dose–corrected DSC acquisitions, with 16 patients undergoing preload dose–corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV &gt; 1.75, and percentage of voxels with relative CBV &gt; 1.0 (fractional tumor burden). We determined Pearson ( r ) and Spearman (ρ) correlations between non-preload dose– and preload dose–corrected metrics. In a subset of patients with recurrent glioblastoma ( n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies ( n = 29) in 12 patients.

RESULTS: With IB Neuro, relative CBV metrics correlated highly between non-preload dose– and preload dose–corrected conditions for fractional tumor burden ( r = 0.96, ρ = 0.94), percentage &gt; 1.75 ( r = 0.93, ρ = 0.91), mean ( r = 0.87, ρ = 0.86), and mode ( r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) ( P &lt; .01). The highest relative CBV–microvessel area correlations required preload dose and IB Neuro ( r = 0.64, ρ = 0.58, P = .001).

CONCLUSIONS: Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.

FTB
:   fractional tumor burden
GBCA
:   gadolinium-based contrast agents
IBN
:   IB Neuro
MVA
:   microvessel area
NICE
:   nordicICE
PLD
:   preload dose
pMRI
:   perfusion MR imaging
rCBV
:   relative cerebral blood volume" />
<meta name="DC.Contributor" content="L.S. Hu" />
<meta name="DC.Contributor" content="Z. Kelm" />
<meta name="DC.Contributor" content="P. Korfiatis" />
<meta name="DC.Contributor" content="A.C. Dueck" />
<meta name="DC.Contributor" content="C. Elrod" />
<meta name="DC.Contributor" content="B.M. Ellingson" />
<meta name="DC.Contributor" content="T.J. Kaufmann" />
<meta name="DC.Contributor" content="J.M. Eschbacher" />
<meta name="DC.Contributor" content="J.P. Karis" />
<meta name="DC.Contributor" content="K. Smith" />
<meta name="DC.Contributor" content="P. Nakaji" />
<meta name="DC.Contributor" content="D. Brinkman" />
<meta name="DC.Contributor" content="D. Pafundi" />
<meta name="DC.Contributor" content="L.C. Baxter" />
<meta name="DC.Contributor" content="B.J. Erickson" />
<meta name="article:published_time" content="2015-12-01" />
<meta name="article:section" content="ADULT BRAIN" />
<meta name="citation_title" content="Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose– and preload dose–corrected DSC acquisitions, with 16 patients undergoing preload dose–corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV &amp;gt; 1.75, and percentage of voxels with relative CBV &amp;gt; 1.0 (fractional tumor burden). We determined Pearson (&lt;i&gt;r&lt;/i&gt;) and Spearman (ρ) correlations between non-preload dose– and preload dose–corrected metrics. In a subset of patients with recurrent glioblastoma (&lt;i&gt;n&lt;/i&gt; = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (&lt;i&gt;n&lt;/i&gt; = 29) in 12 patients.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;With IB Neuro, relative CBV metrics correlated highly between non-preload dose– and preload dose–corrected conditions for fractional tumor burden (&lt;i&gt;r&lt;/i&gt; = 0.96, ρ = 0.94), percentage &amp;gt; 1.75 (&lt;i&gt;r&lt;/i&gt; = 0.93, ρ = 0.91), mean (&lt;i&gt;r&lt;/i&gt; = 0.87, ρ = 0.86), and mode (&lt;i&gt;r&lt;/i&gt; = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (&lt;i&gt;P&lt;/i&gt; &amp;lt; .01). The highest relative CBV–microvessel area correlations required preload dose and IB Neuro (&lt;i&gt;r&lt;/i&gt; = 0.64, ρ = 0.58, &lt;i&gt;P&lt;/i&gt; = .001).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
<meta name="citation_publisher" content="American Journal of Neuroradiology" />
<meta name="citation_publication_date" content="2015/12/01" />
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<meta name="citation_author_institution" content="dKeller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.)" />
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<meta name="citation_author_institution" content="cthe Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota" />
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<meta name="citation_author_institution" content="cthe Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota" />
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<meta name="twitter:title" content="Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.

MATERIALS AND METHODS: This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose– and preload dose–corrected DSC acquisitions, with 16 patients undergoing preload dose–corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV &gt; 1.75, and percentage of voxels with relative CBV &gt; 1.0 (fractional tumor burden). We determined Pearson ( r ) and Spearman (ρ) correlations between non-preload dose– and preload dose–corrected metrics. In a subset of patients with recurrent glioblastoma ( n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies ( n = 29) in 12 patients.

RESULTS: With IB Neuro, relative CBV metrics correlated highly between non-preload dose– and preload dose–corrected conditions for fractional tumor burden ( r = 0.96, ρ = 0.94), percentage &gt; 1.75 ( r = 0.93, ρ = 0.91), mean ( r = 0.87, ρ = 0.86), and mode ( r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) ( P &lt; .01). The highest relative CBV–microvessel area correlations required preload dose and IB Neuro ( r = 0.64, ρ = 0.58, P = .001).

CONCLUSIONS: Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.

FTB
:   fractional tumor burden
GBCA
:   gadolinium-based contrast agents
IBN
:   IB Neuro
MVA
:   microvessel area
NICE
:   nordicICE
PLD
:   preload dose
pMRI
:   perfusion MR imaging
rCBV
:   relative cerebral blood volume" />
<meta name="og-title" property="og:title" content="Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/36/12/2242" />
<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: Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.

MATERIALS AND METHODS: This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose– and preload dose–corrected DSC acquisitions, with 16 patients undergoing preload dose–corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV &gt; 1.75, and percentage of voxels with relative CBV &gt; 1.0 (fractional tumor burden). We determined Pearson ( r ) and Spearman (ρ) correlations between non-preload dose– and preload dose–corrected metrics. In a subset of patients with recurrent glioblastoma ( n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies ( n = 29) in 12 patients.

RESULTS: With IB Neuro, relative CBV metrics correlated highly between non-preload dose– and preload dose–corrected conditions for fractional tumor burden ( r = 0.96, ρ = 0.94), percentage &gt; 1.75 ( r = 0.93, ρ = 0.91), mean ( r = 0.87, ρ = 0.86), and mode ( r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) ( P &lt; .01). The highest relative CBV–microvessel area correlations required preload dose and IB Neuro ( r = 0.64, ρ = 0.58, P = .001).

CONCLUSIONS: Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.

FTB
:   fractional tumor burden
GBCA
:   gadolinium-based contrast agents
IBN
:   IB Neuro
MVA
:   microvessel area
NICE
:   nordicICE
PLD
:   preload dose
pMRI
:   perfusion MR imaging
rCBV
:   relative cerebral blood volume" />
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">L.S. Hu</span>, <span class="highwire-citation-author" data-delta="1">Z. Kelm</span>, <span class="highwire-citation-author" data-delta="2">P. Korfiatis</span>, <span class="highwire-citation-author" data-delta="3">A.C. Dueck</span>, <span class="highwire-citation-author" data-delta="4">C. Elrod</span>, <span class="highwire-citation-author" data-delta="5">B.M. Ellingson</span>, <span class="highwire-citation-author" data-delta="6">T.J. Kaufmann</span>, <span class="highwire-citation-author" data-delta="7">J.M. Eschbacher</span>, <span class="highwire-citation-author" data-delta="8">J.P. Karis</span>, <span class="highwire-citation-author" data-delta="9">K. Smith</span>, <span class="highwire-citation-author" data-delta="10">P. Nakaji</span>, <span class="highwire-citation-author" data-delta="11">D. Brinkman</span>, <span class="highwire-citation-author" data-delta="12">D. Pafundi</span>, <span class="highwire-citation-author" data-delta="13">L.C. Baxter</span> and <span class="highwire-citation-author" data-delta="14">B.J. Erickson</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">December 2015,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2242-2249; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A4451 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-14188--6574807753" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">L.S. Hu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (L.S.H.)</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.)</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=L.S.%2BHu%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=Hu%20L&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 last"><a href="/search/author1%3AL.S.%2BHu%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Z. Kelm </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota</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=Z.%2BKelm%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=Kelm%20Z&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 last"><a href="/search/author1%3AZ.%2BKelm%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">P. Korfiatis </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota</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=P.%2BKorfiatis%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=Korfiatis%20P&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 last"><a href="/search/author1%3AP.%2BKorfiatis%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">A.C. Dueck </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Biostatistics (A.C.D.), Mayo Clinic, Phoenix/Scottsdale, Arizona</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=A.C.%2BDueck%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=Dueck%20A&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 last"><a href="/search/author1%3AA.C.%2BDueck%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">C. Elrod </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.)</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=C.%2BElrod%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=Elrod%20C&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 last"><a href="/search/author1%3AC.%2BElrod%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">B.M. 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 36</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 12 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Dec 2015 </div></div>
      
      
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      <div class="highwire-cite-title">Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma</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">L.S.</span> <span class="nlm-surname">Hu</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Z.</span> <span class="nlm-surname">Kelm</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">P.</span> <span class="nlm-surname">Korfiatis</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">A.C.</span> <span class="nlm-surname">Dueck</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Elrod</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">B.M.</span> <span class="nlm-surname">Ellingson</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">T.J.</span> <span class="nlm-surname">Kaufmann</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">J.M.</span> <span class="nlm-surname">Eschbacher</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">J.P.</span> <span class="nlm-surname">Karis</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">K.</span> <span class="nlm-surname">Smith</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">P.</span> <span class="nlm-surname">Nakaji</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Brinkman</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Pafundi</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">L.C.</span> <span class="nlm-surname">Baxter</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">B.J.</span> <span class="nlm-surname">Erickson</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2015, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2242-2249; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A4451 </span></div>
  
  
  
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      <div class="highwire-cite-title">Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma</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">L.S.</span> <span class="nlm-surname">Hu</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Z.</span> <span class="nlm-surname">Kelm</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">P.</span> <span class="nlm-surname">Korfiatis</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">A.C.</span> <span class="nlm-surname">Dueck</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Elrod</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">B.M.</span> <span class="nlm-surname">Ellingson</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">T.J.</span> <span class="nlm-surname">Kaufmann</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">J.M.</span> <span class="nlm-surname">Eschbacher</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">J.P.</span> <span class="nlm-surname">Karis</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">K.</span> <span class="nlm-surname">Smith</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">P.</span> <span class="nlm-surname">Nakaji</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Brinkman</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Pafundi</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">L.C.</span> <span class="nlm-surname">Baxter</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">B.J.</span> <span class="nlm-surname">Erickson</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2015, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2242-2249; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A4451 </span></div>
  
  
  
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