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<meta name="DC.Title" content="Assessment of the Reproducibility of Postprocessing Dynamic CT Perfusion Data" />
<meta name="DC.Date" content="2004-01-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="Copyright © American Society of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Commercially available software programs for the conversion of dynamic CT perfusion (CTP) source data into cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) maps require operators to subjectively define parameters that are used in subsequent postprocessing calculations. Our purpose was to define the variability of CBV, CBF, and MTT values derived from CTP maps generated from the same source data postprocessed by three different CT technologists (CTTs).

METHODS: Raw data derived from dynamic CTP examinations performed in 20 subjects were postprocessed seven times by three experienced CTTs. Parenchymal regions of interest derived from each map (CBV, CBF, and MTT) were compared. The CBF maps generated by each technologist were also qualitatively assessed. Decisions made by each analyzer during postprocessing were assessed.

RESULTS: The intraclass correlation coefficients were 0.73 (95% CI, 0.64–0.81), 0.87 (0.83–0.91) and 0.89 (0.85–0.93), for the CBV, CBF, and MTT parenchymal regions of interest, respectively. All individual correlation coefficients between data sets were significant to a P value &lt;.05. Measurement error, made solely on the basis of different technologists postprocessing the same source data and expressed as the coefficients of variation, were 31%, 30%, and 14% for CBV, CBF, and MTT, respectively. The selection of the arterial input function (AIF) region of interest, venous function region of interest, and preenhancement interval were very reproducible. The technologists differed significantly with respect to the selection of the postenhancement image (PoEI) ( P &lt; .01). A retrospective review of the individual CBF maps indicated that variance in the PoEI selection accounted for much of the variation in the qualitative appearance of the CBF maps generated by different technologists. The PoEI was selected to demarcate the baseline of the AIF time-attenuation curve. It is likely that this method of PoEI selection significantly contributed to intra- and interanalyzer variability.

CONCLUSION: There is a high degree of correlation between parenchymal regions of interest derived from CBV, CBF, and MTT maps generated from the same dynamic CTP source data postprocessed by different operators. The level of agreement, however, may not be sufficient to incorporate quantitative values into clinical decision making. Quantitative differences between parenchymal regions of interest were not infrequently manifest as significant differences in the qualitative appearance of the CBF maps. It is likely that, with optimization of postprocessing parameter selection, the degree of variability may be substantially reduced." />
<meta name="DC.Contributor" content="David Fiorella" />
<meta name="DC.Contributor" content="Joseph Heiserman" />
<meta name="DC.Contributor" content="Erin Prenger" />
<meta name="DC.Contributor" content="Shahram Partovi" />
<meta name="article:published_time" content="2004-01-01" />
<meta name="article:section" content="BRAIN" />
<meta name="citation_title" content="Assessment of the Reproducibility of Postprocessing Dynamic CT Perfusion Data" />
<meta name="citation_abstract" lang="en" content="&lt;p&gt;&lt;i&gt;BACKGROUND AND PURPOSE:&lt;/i&gt; Commercially available software programs for the conversion of dynamic CT perfusion (CTP) source data into cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) maps require operators to subjectively define parameters that are used in subsequent postprocessing calculations. Our purpose was to define the variability of CBV, CBF, and MTT values derived from CTP maps generated from the same source data postprocessed by three different CT technologists (CTTs).&lt;/p&gt;&lt;p&gt;&lt;i&gt;METHODS:&lt;/i&gt; Raw data derived from dynamic CTP examinations performed in 20 subjects were postprocessed seven times by three experienced CTTs. Parenchymal regions of interest derived from each map (CBV, CBF, and MTT) were compared. The CBF maps generated by each technologist were also qualitatively assessed. Decisions made by each analyzer during postprocessing were assessed.&lt;/p&gt;&lt;p&gt;&lt;i&gt;RESULTS:&lt;/i&gt; The intraclass correlation coefficients were 0.73 (95% CI, 0.64–0.81), 0.87 (0.83–0.91) and 0.89 (0.85–0.93), for the CBV, CBF, and MTT parenchymal regions of interest, respectively. All individual correlation coefficients between data sets were significant to a &lt;i&gt;P&lt;/i&gt; value &amp;lt;.05. Measurement error, made solely on the basis of different technologists postprocessing the same source data and expressed as the coefficients of variation, were 31%, 30%, and 14% for CBV, CBF, and MTT, respectively. The selection of the arterial input function (AIF) region of interest, venous function region of interest, and preenhancement interval were very reproducible. The technologists differed significantly with respect to the selection of the postenhancement image (PoEI) (&lt;i&gt;P&lt;/i&gt; &amp;lt; .01). A retrospective review of the individual CBF maps indicated that variance in the PoEI selection accounted for much of the variation in the qualitative appearance of the CBF maps generated by different technologists. The PoEI was selected to demarcate the baseline of the AIF time-attenuation curve. It is likely that this method of PoEI selection significantly contributed to intra- and interanalyzer variability.&lt;/p&gt;&lt;p&gt;&lt;i&gt;CONCLUSION:&lt;/i&gt; There is a high degree of correlation between parenchymal regions of interest derived from CBV, CBF, and MTT maps generated from the same dynamic CTP source data postprocessed by different operators. The level of agreement, however, may not be sufficient to incorporate quantitative values into clinical decision making. Quantitative differences between parenchymal regions of interest were not infrequently manifest as significant differences in the qualitative appearance of the CBF maps. It is likely that, with optimization of postprocessing parameter selection, the degree of variability may be substantially reduced.&lt;/p&gt;" />
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<meta name="twitter:title" content="Assessment of the Reproducibility of Postprocessing Dynamic CT Perfusion Data" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Commercially available software programs for the conversion of dynamic CT perfusion (CTP) source data into cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) maps require operators to subjectively define parameters that are used in subsequent postprocessing calculations. Our purpose was to define the variability of CBV, CBF, and MTT values derived from CTP maps generated from the same source data postprocessed by three different CT technologists (CTTs).

METHODS: Raw data derived from dynamic CTP examinations performed in 20 subjects were postprocessed seven times by three experienced CTTs. Parenchymal regions of interest derived from each map (CBV, CBF, and MTT) were compared. The CBF maps generated by each technologist were also qualitatively assessed. Decisions made by each analyzer during postprocessing were assessed.

RESULTS: The intraclass correlation coefficients were 0.73 (95% CI, 0.64–0.81), 0.87 (0.83–0.91) and 0.89 (0.85–0.93), for the CBV, CBF, and MTT parenchymal regions of interest, respectively. All individual correlation coefficients between data sets were significant to a P value &lt;.05. Measurement error, made solely on the basis of different technologists postprocessing the same source data and expressed as the coefficients of variation, were 31%, 30%, and 14% for CBV, CBF, and MTT, respectively. The selection of the arterial input function (AIF) region of interest, venous function region of interest, and preenhancement interval were very reproducible. The technologists differed significantly with respect to the selection of the postenhancement image (PoEI) ( P &lt; .01). A retrospective review of the individual CBF maps indicated that variance in the PoEI selection accounted for much of the variation in the qualitative appearance of the CBF maps generated by different technologists. The PoEI was selected to demarcate the baseline of the AIF time-attenuation curve. It is likely that this method of PoEI selection significantly contributed to intra- and interanalyzer variability.

CONCLUSION: There is a high degree of correlation between parenchymal regions of interest derived from CBV, CBF, and MTT maps generated from the same dynamic CTP source data postprocessed by different operators. The level of agreement, however, may not be sufficient to incorporate quantitative values into clinical decision making. Quantitative differences between parenchymal regions of interest were not infrequently manifest as significant differences in the qualitative appearance of the CBF maps. It is likely that, with optimization of postprocessing parameter selection, the degree of variability may be substantially reduced." />
<meta name="og-title" property="og:title" content="Assessment of the Reproducibility of Postprocessing Dynamic CT Perfusion Data" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/25/1/97" />
<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: Commercially available software programs for the conversion of dynamic CT perfusion (CTP) source data into cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) maps require operators to subjectively define parameters that are used in subsequent postprocessing calculations. Our purpose was to define the variability of CBV, CBF, and MTT values derived from CTP maps generated from the same source data postprocessed by three different CT technologists (CTTs).

METHODS: Raw data derived from dynamic CTP examinations performed in 20 subjects were postprocessed seven times by three experienced CTTs. Parenchymal regions of interest derived from each map (CBV, CBF, and MTT) were compared. The CBF maps generated by each technologist were also qualitatively assessed. Decisions made by each analyzer during postprocessing were assessed.

RESULTS: The intraclass correlation coefficients were 0.73 (95% CI, 0.64–0.81), 0.87 (0.83–0.91) and 0.89 (0.85–0.93), for the CBV, CBF, and MTT parenchymal regions of interest, respectively. All individual correlation coefficients between data sets were significant to a P value &lt;.05. Measurement error, made solely on the basis of different technologists postprocessing the same source data and expressed as the coefficients of variation, were 31%, 30%, and 14% for CBV, CBF, and MTT, respectively. The selection of the arterial input function (AIF) region of interest, venous function region of interest, and preenhancement interval were very reproducible. The technologists differed significantly with respect to the selection of the postenhancement image (PoEI) ( P &lt; .01). A retrospective review of the individual CBF maps indicated that variance in the PoEI selection accounted for much of the variation in the qualitative appearance of the CBF maps generated by different technologists. The PoEI was selected to demarcate the baseline of the AIF time-attenuation curve. It is likely that this method of PoEI selection significantly contributed to intra- and interanalyzer variability.

CONCLUSION: There is a high degree of correlation between parenchymal regions of interest derived from CBV, CBF, and MTT maps generated from the same dynamic CTP source data postprocessed by different operators. The level of agreement, however, may not be sufficient to incorporate quantitative values into clinical decision making. Quantitative differences between parenchymal regions of interest were not infrequently manifest as significant differences in the qualitative appearance of the CBF maps. It is likely that, with optimization of postprocessing parameter selection, the degree of variability may be substantially reduced." />
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<div id="hw-article-author-popups-top-node-10326--631566799" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">David Fiorella </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=David%2BFiorella%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=Fiorella%20D&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%3ADavid%2BFiorella%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">Joseph Heiserman </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=Joseph%2BHeiserman%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=Heiserman%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 last"><a href="/search/author1%3AJoseph%2BHeiserman%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">Erin Prenger </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=Erin%2BPrenger%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=Prenger%20E&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%3AErin%2BPrenger%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">Shahram Partovi </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=Shahram%2BPartovi%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=Partovi%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%3AShahram%2BPartovi%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view"><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2>Abstract</h2><p id="p-1"><em>BACKGROUND AND PURPOSE:</em> Commercially available software programs for the conversion of dynamic CT perfusion (CTP) source data into cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) maps require operators to subjectively define parameters that are used in subsequent postprocessing calculations. Our purpose was to define the variability of CBV, CBF, and MTT values derived from CTP maps generated from the same source data postprocessed by three different CT technologists (CTTs).</p><p id="p-2"><em>METHODS:</em> Raw data derived from dynamic CTP examinations performed in 20 subjects were postprocessed seven times by three experienced CTTs. Parenchymal regions of interest derived from each map (CBV, CBF, and MTT) were compared. The CBF maps generated by each technologist were also qualitatively assessed. Decisions made by each analyzer during postprocessing were assessed.</p><p id="p-3"><em>RESULTS:</em> The intraclass correlation coefficients were 0.73 (95% CI, 0.64–0.81), 0.87 (0.83–0.91) and 0.89 (0.85–0.93), for the CBV, CBF, and MTT parenchymal regions of interest, respectively. All individual correlation coefficients between data sets were significant to a <em>P</em> value &lt;.05. Measurement error, made solely on the basis of different technologists postprocessing the same source data and expressed as the coefficients of variation, were 31%, 30%, and 14% for CBV, CBF, and MTT, respectively. The selection of the arterial input function (AIF) region of interest, venous function region of interest, and preenhancement interval were very reproducible. The technologists differed significantly with respect to the selection of the postenhancement image (PoEI) (<em>P</em> &lt; .01). A retrospective review of the individual CBF maps indicated that variance in the PoEI selection accounted for much of the variation in the qualitative appearance of the CBF maps generated by different technologists. The PoEI was selected to demarcate the baseline of the AIF time-attenuation curve. It is likely that this method of PoEI selection significantly contributed to intra- and interanalyzer variability.</p><p id="p-4"><em>CONCLUSION:</em> There is a high degree of correlation between parenchymal regions of interest derived from CBV, CBF, and MTT maps generated from the same dynamic CTP source data postprocessed by different operators. The level of agreement, however, may not be sufficient to incorporate quantitative values into clinical decision making. Quantitative differences between parenchymal regions of interest were not infrequently manifest as significant differences in the qualitative appearance of the CBF maps. It is likely that, with optimization of postprocessing parameter selection, the degree of variability may be substantially reduced.</p></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">Copyright © American Society of Neuroradiology</li></ul><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/25/1/97.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div>  </div>

  
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