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<meta name="DC.Title" content="Strategies of Collateral Blood Flow Assessment in Ischemic Stroke: Prediction of the Follow-Up Infarct Volume in Conventional and Dynamic CTA" />
<meta name="DC.Identifier" content="10.3174/ajnr.A4131" />
<meta name="DC.Date" content="2015-03-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2015 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Collateral blood flow is an important prognostic marker in the acute stroke situation but approaches for assessment vary widely. Our aim was to compare strategies of collateral blood flow assessment in dynamic and conventional CTA in their ability to predict the follow-up infarction volume.

MATERIALS AND METHODS: We retrospectively included all patients with an M1 occlusion from an existing cohort of 1912 consecutive patients who underwent initial multimodal stroke CT and follow-up MR imaging or nonenhanced CT. Collateralization was assessed in both conventional CT angiography and dynamic CT angiography by using 3 different collateral grading scores and segmentation of the volume of hypoattenuation. Arterial, arteriovenous, and venous phases were reconstructed for dynamic CT angiography, and all collateral scores and the volume of hypoattenuation were individually assessed for all phases. Different grading systems were compared by using the Bayesian information criterion calculated for multivariate regression analyses (Bayesian information criterion difference = 2–6, “positive”; Bayesian information criterion difference = 6–10, “strong”; Bayesian information criterion difference = &gt;10, “very strong”).

RESULTS: One hundred thirty-six patients (mean age, 70.4 years; male sex, 41.2%) were included. In the multivariate analysis, models containing the volume of hypoattenuation showed a significantly better model fit than models containing any of the 3 collateral grading scores in conventional CT angiography (Bayesian information criterion difference = &gt;10) and dynamic CT angiography (Bayesian information criterion difference = &gt;10). All grading systems showed the best model fit in the arteriovenous phase. For the volume of hypoattenuation, model fit was significantly higher for models containing the volume of hypoattenuation as assessed in the arteriovenous phase of dynamic CT angiography compared with the venous phase (Bayesian information criterion difference = 6.2) and the arterial phase of dynamic CT angiography (Bayesian information criterion difference = &gt;10) and in comparison with conventional CT angiography (Bayesian information criterion difference = &gt;10).

CONCLUSIONS: The use of dynamic CT angiography within the arteriovenous phase by using quantification of the volume of hypoattenuation is the superior technique for assessment of collateralization among the tested approaches.

BIC
:   Bayesian information criterion
NECT
:   nonenhanced CT
VH
:   volume of hypoattenuation" />
<meta name="DC.Contributor" content="S.E. Beyer" />
<meta name="DC.Contributor" content="K.M. Thierfelder" />
<meta name="DC.Contributor" content="L. von Baumgarten" />
<meta name="DC.Contributor" content="M. Rottenkolber" />
<meta name="DC.Contributor" content="F.G. Meinel" />
<meta name="DC.Contributor" content="H. Janssen" />
<meta name="DC.Contributor" content="B. Ertl-Wagner" />
<meta name="DC.Contributor" content="M.F. Reiser" />
<meta name="DC.Contributor" content="W.H. Sommer" />
<meta name="article:published_time" content="2015-03-01" />
<meta name="article:section" content="Brain" />
<meta name="citation_title" content="Strategies of Collateral Blood Flow Assessment in Ischemic Stroke: Prediction of the Follow-Up Infarct Volume in Conventional and Dynamic CTA" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Collateral blood flow is an important prognostic marker in the acute stroke situation but approaches for assessment vary widely. Our aim was to compare strategies of collateral blood flow assessment in dynamic and conventional CTA in their ability to predict the follow-up infarction volume.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;We retrospectively included all patients with an M1 occlusion from an existing cohort of 1912 consecutive patients who underwent initial multimodal stroke CT and follow-up MR imaging or nonenhanced CT. Collateralization was assessed in both conventional CT angiography and dynamic CT angiography by using 3 different collateral grading scores and segmentation of the volume of hypoattenuation. Arterial, arteriovenous, and venous phases were reconstructed for dynamic CT angiography, and all collateral scores and the volume of hypoattenuation were individually assessed for all phases. Different grading systems were compared by using the Bayesian information criterion calculated for multivariate regression analyses (Bayesian information criterion difference = 2–6, “positive”; Bayesian information criterion difference = 6–10, “strong”; Bayesian information criterion difference = &amp;gt;10, “very strong”).&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;One hundred thirty-six patients (mean age, 70.4 years; male sex, 41.2%) were included. In the multivariate analysis, models containing the volume of hypoattenuation showed a significantly better model fit than models containing any of the 3 collateral grading scores in conventional CT angiography (Bayesian information criterion difference = &amp;gt;10) and dynamic CT angiography (Bayesian information criterion difference = &amp;gt;10). All grading systems showed the best model fit in the arteriovenous phase. For the volume of hypoattenuation, model fit was significantly higher for models containing the volume of hypoattenuation as assessed in the arteriovenous phase of dynamic CT angiography compared with the venous phase (Bayesian information criterion difference = 6.2) and the arterial phase of dynamic CT angiography (Bayesian information criterion difference = &amp;gt;10) and in comparison with conventional CT angiography (Bayesian information criterion difference = &amp;gt;10).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The use of dynamic CT angiography within the arteriovenous phase by using quantification of the volume of hypoattenuation is the superior technique for assessment of collateralization among the tested approaches.&lt;/p&gt;" />
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<meta name="twitter:title" content="Strategies of Collateral Blood Flow Assessment in Ischemic Stroke: Prediction of the Follow-Up Infarct Volume in Conventional and Dynamic CTA" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Collateral blood flow is an important prognostic marker in the acute stroke situation but approaches for assessment vary widely. Our aim was to compare strategies of collateral blood flow assessment in dynamic and conventional CTA in their ability to predict the follow-up infarction volume.

MATERIALS AND METHODS: We retrospectively included all patients with an M1 occlusion from an existing cohort of 1912 consecutive patients who underwent initial multimodal stroke CT and follow-up MR imaging or nonenhanced CT. Collateralization was assessed in both conventional CT angiography and dynamic CT angiography by using 3 different collateral grading scores and segmentation of the volume of hypoattenuation. Arterial, arteriovenous, and venous phases were reconstructed for dynamic CT angiography, and all collateral scores and the volume of hypoattenuation were individually assessed for all phases. Different grading systems were compared by using the Bayesian information criterion calculated for multivariate regression analyses (Bayesian information criterion difference = 2–6, “positive”; Bayesian information criterion difference = 6–10, “strong”; Bayesian information criterion difference = &gt;10, “very strong”).

RESULTS: One hundred thirty-six patients (mean age, 70.4 years; male sex, 41.2%) were included. In the multivariate analysis, models containing the volume of hypoattenuation showed a significantly better model fit than models containing any of the 3 collateral grading scores in conventional CT angiography (Bayesian information criterion difference = &gt;10) and dynamic CT angiography (Bayesian information criterion difference = &gt;10). All grading systems showed the best model fit in the arteriovenous phase. For the volume of hypoattenuation, model fit was significantly higher for models containing the volume of hypoattenuation as assessed in the arteriovenous phase of dynamic CT angiography compared with the venous phase (Bayesian information criterion difference = 6.2) and the arterial phase of dynamic CT angiography (Bayesian information criterion difference = &gt;10) and in comparison with conventional CT angiography (Bayesian information criterion difference = &gt;10).

CONCLUSIONS: The use of dynamic CT angiography within the arteriovenous phase by using quantification of the volume of hypoattenuation is the superior technique for assessment of collateralization among the tested approaches.

BIC
:   Bayesian information criterion
NECT
:   nonenhanced CT
VH
:   volume of hypoattenuation" />
<meta name="og-title" property="og:title" content="Strategies of Collateral Blood Flow Assessment in Ischemic Stroke: Prediction of the Follow-Up Infarct Volume in Conventional and Dynamic CTA" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/36/3/488" />
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<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Collateral blood flow is an important prognostic marker in the acute stroke situation but approaches for assessment vary widely. Our aim was to compare strategies of collateral blood flow assessment in dynamic and conventional CTA in their ability to predict the follow-up infarction volume.

MATERIALS AND METHODS: We retrospectively included all patients with an M1 occlusion from an existing cohort of 1912 consecutive patients who underwent initial multimodal stroke CT and follow-up MR imaging or nonenhanced CT. Collateralization was assessed in both conventional CT angiography and dynamic CT angiography by using 3 different collateral grading scores and segmentation of the volume of hypoattenuation. Arterial, arteriovenous, and venous phases were reconstructed for dynamic CT angiography, and all collateral scores and the volume of hypoattenuation were individually assessed for all phases. Different grading systems were compared by using the Bayesian information criterion calculated for multivariate regression analyses (Bayesian information criterion difference = 2–6, “positive”; Bayesian information criterion difference = 6–10, “strong”; Bayesian information criterion difference = &gt;10, “very strong”).

RESULTS: One hundred thirty-six patients (mean age, 70.4 years; male sex, 41.2%) were included. In the multivariate analysis, models containing the volume of hypoattenuation showed a significantly better model fit than models containing any of the 3 collateral grading scores in conventional CT angiography (Bayesian information criterion difference = &gt;10) and dynamic CT angiography (Bayesian information criterion difference = &gt;10). All grading systems showed the best model fit in the arteriovenous phase. For the volume of hypoattenuation, model fit was significantly higher for models containing the volume of hypoattenuation as assessed in the arteriovenous phase of dynamic CT angiography compared with the venous phase (Bayesian information criterion difference = 6.2) and the arterial phase of dynamic CT angiography (Bayesian information criterion difference = &gt;10) and in comparison with conventional CT angiography (Bayesian information criterion difference = &gt;10).

CONCLUSIONS: The use of dynamic CT angiography within the arteriovenous phase by using quantification of the volume of hypoattenuation is the superior technique for assessment of collateralization among the tested approaches.

BIC
:   Bayesian information criterion
NECT
:   nonenhanced CT
VH
:   volume of hypoattenuation" />
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">S.E. Beyer</span>, <span class="highwire-citation-author" data-delta="1">K.M. Thierfelder</span>, <span class="highwire-citation-author" data-delta="2">L. von Baumgarten</span>, <span class="highwire-citation-author" data-delta="3">M. Rottenkolber</span>, <span class="highwire-citation-author" data-delta="4">F.G. Meinel</span>, <span class="highwire-citation-author" data-delta="5">H. Janssen</span>, <span class="highwire-citation-author" data-delta="6">B. Ertl-Wagner</span>, <span class="highwire-citation-author" data-delta="7">M.F. Reiser</span> and <span class="highwire-citation-author" data-delta="8">W.H. Sommer</span></span></div>
  
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<div id="hw-article-author-popups-top-node-14010--61443977165" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">S.E. Beyer </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institute for Clinical Radiology (S.E.B., K.M.T., F.G.M., B.E.-W., M.F.R., W.H.S.)</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=S.E.%2BBeyer%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=Beyer%20S&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%3AS.E.%2BBeyer%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">K.M. Thierfelder </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institute for Clinical Radiology (S.E.B., K.M.T., F.G.M., B.E.-W., M.F.R., W.H.S.)</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.M.%2BThierfelder%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=Thierfelder%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 last"><a href="/search/author1%3AK.M.%2BThierfelder%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">L. von Baumgarten </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Departments of Neurology (L.v.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.%2Bvon%2BBaumgarten%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=von%20Baumgarten%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.%2Bvon%2BBaumgarten%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">M. 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Reiser </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institute for Clinical Radiology (S.E.B., K.M.T., F.G.M., B.E.-W., M.F.R., W.H.S.)</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=M.F.%2BReiser%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=Reiser%20M&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%3AM.F.%2BReiser%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-8"><div class="author-tooltip-name">W.H. 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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" xmlns:xhtml="http://www.w3.org/1999/xhtml" class="section ref-list" id="ref-list-1"><h2 class="">REFERENCES</h2><ol class="cit-list ref-use-labels"><li><span class="ref-label">1.</span><a class="rev-xref-ref" href="#xref-ref-1-1" title="View reference 1. in text" id="ref-1">↵</a>

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      <div class="highwire-cite-highlight"><a href="/content/36/3"  class="highlight-image-linked"><img class="highlight-image" src="http://ajnr-stage2.stage.highwire.org/sites/default/files/styles/medium/public/highwire/ajnr/36/3.cover-source.jpg?itok=RbtKCsor" alt="American Journal of Neuroradiology: 36 (3)" /></a></div>
  
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          <a href="/content/36/3" class="highwire-cite-linked-title" data-icon-position="" data-hide-link-title="0"><span class="highwire-cite-title">American Journal of Neuroradiology</span></a>      
      
        	<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 3 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Mar 2015 </div></div>
      
      
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      <div class="highwire-cite-title">Strategies of Collateral Blood Flow Assessment in Ischemic Stroke: Prediction of the Follow-Up Infarct Volume in Conventional and Dynamic CTA</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 2015, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(3) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">488-494; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A4131 </span></div>
  
  
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">S.E.</span> <span class="nlm-surname">Beyer</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">K.M.</span> <span class="nlm-surname">Thierfelder</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">L.</span> <span class="nlm-surname">von Baumgarten</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Rottenkolber</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">F.G.</span> <span class="nlm-surname">Meinel</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">H.</span> <span class="nlm-surname">Janssen</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Ertl-Wagner</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">M.F.</span> <span class="nlm-surname">Reiser</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">W.H.</span> <span class="nlm-surname">Sommer</span></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">Mar 2015, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(3) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">488-494; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A4131 </span></div>
  
  
  
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