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<meta name="DC.Title" content="Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8367" />
<meta name="DC.Date" content="2024-10-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2024 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Considering recent iodinated contrast shortages and a focus on reducing waste, developing protocols with lower contrast dosing while maintaining image quality through artificial intelligence is needed. This study compared reduced iodinated contrast media and standard dose CTP acquisitions, and the impact of deep learning denoising on CTP image quality in preclinical and clinical studies. The effect of reduced X-ray mAs dose was also investigated in preclinical studies.

MATERIALS AND METHODS: Twelve swine underwent 9 CTP examinations each, performed at combinations of 3 different x-ray (37, 67, and 127 mAs) and iodinated contrast media doses (10, 15, and 20 mL). Clinical CTP acquisitions performed before and during the iodinated contrast media shortage and protocol change (from 40 to 30 mL) were retrospectively included. Eleven patients with reduced iodinated contrast media dosages and 11 propensity-score-matched controls with the standard iodinated contrast media dosages were included. A residual encoder-decoder convolutional neural network (RED-CNN) was trained for CTP denoising using k- space-weighted image average filtered CTP images as the target. The standard, RED-CNN-denoised, and k- space-weighted image average noise-filtered images for animal and human studies were compared for quantitative SNR and qualitative image evaluation.

RESULTS: The SNR of animal CTP images decreased with reductions in iodinated contrast media and milliampere-second doses. Contrast dose reduction had a greater effect on SNR than milliampere-second reduction. Noise-filtering by k- space-weighted image average and RED-CNN denoising progressively improved the SNR of CTP maps, with RED-CNN resulting in the highest SNR. The SNR of clinical CTP images was generally lower with a reduced iodinated contrast media dose, which was improved by the k- space-weighted image average and RED-CNN denoising ( P &lt; .05). Qualitative readings consistently rated RED-CNN denoised CTP as the best quality, followed by k- space-weighted image average and then standard CTP images.

CONCLUSIONS: Deep learning-denoising can improve image quality for low iodinated contrast media CTP protocols, and could approximate standard iodinated contrast media dose CTP, in addition to potentially improving image quality for low milliampere-second acquisitions.

AI
:   artificial intelligence
DL
:   deep learning
ICM
:   iodinated contrast media
KWIA
:   k- space-weighted image average
LCD
:   low contrast dose
PSNR
:   peak SNR
RED-CNN
:   residual encoder-decoder convolutional neural network
RMSE
:   root mean square error
SCD
:   standard contrast dose
SSIM
:   structural similarity index measure" />
<meta name="DC.Contributor" content="Mahmud Mossa-Basha" />
<meta name="DC.Contributor" content="Chengcheng Zhu" />
<meta name="DC.Contributor" content="Tanya Pandhi" />
<meta name="DC.Contributor" content="Steve Mendoza" />
<meta name="DC.Contributor" content="Javid Azadbakht" />
<meta name="DC.Contributor" content="Ahmed Safwat" />
<meta name="DC.Contributor" content="Dean Homen" />
<meta name="DC.Contributor" content="Carlos Zamora" />
<meta name="DC.Contributor" content="Dinesh Kumar Gnanasekaran" />
<meta name="DC.Contributor" content="Ruiyue Peng" />
<meta name="DC.Contributor" content="Steven Cen" />
<meta name="DC.Contributor" content="Vinay Duddalwar" />
<meta name="DC.Contributor" content="Jeffry R. Alger" />
<meta name="DC.Contributor" content="Danny J.J. Wang" />
<meta name="article:published_time" content="2024-10-01" />
<meta name="article:section" content="Neurovascular/Stroke Imaging" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
<meta name="citation_publisher" content="American Journal of Neuroradiology" />
<meta name="citation_title" content="Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study" />
<meta name="citation_publication_date" content="2024/10/01" />
<meta name="citation_mjid" content="ajnr;45/10/1468" />
<meta name="citation_id" content="45/10/1468" />
<meta name="citation_public_url" content="http://ajnr-stage2.stage.highwire.org/content/45/10/1468" />
<meta name="citation_abstract_html_url" content="http://ajnr-stage2.stage.highwire.org/content/45/10/1468.abstract" />
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<meta name="citation_issn" content="0195-6108" />
<meta name="citation_issn" content="1936-959X" />
<meta name="citation_doi" content="10.3174/ajnr.A8367" />
<meta name="citation_pmid" content="38844370" />
<meta name="citation_volume" content="45" />
<meta name="citation_issue" content="10" />
<meta name="citation_article_type" content="Research Article" />
<meta name="citation_section" content="Neurovascular/Stroke Imaging" />
<meta name="citation_firstpage" content="1468" />
<meta name="citation_lastpage" content="1474" />
<meta name="citation_author" content="Mahmud Mossa-Basha" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.M.-B., C.Z., A.S), University of Washington, Seattle, Washington" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-7798-8158" />
<meta name="citation_author" content="Chengcheng Zhu" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.M.-B., C.Z., A.S), University of Washington, Seattle, Washington" />
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<meta name="twitter:title" content="Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Considering recent iodinated contrast shortages and a focus on reducing waste, developing protocols with lower contrast dosing while maintaining image quality through artificial intelligence is needed. This study compared reduced iodinated contrast media and standard dose CTP acquisitions, and the impact of deep learning denoising on CTP image quality in preclinical and clinical studies. The effect of reduced X-ray mAs dose was also investigated in preclinical studies.

MATERIALS AND METHODS: Twelve swine underwent 9 CTP examinations each, performed at combinations of 3 different x-ray (37, 67, and 127 mAs) and iodinated contrast media doses (10, 15, and 20 mL). Clinical CTP acquisitions performed before and during the iodinated contrast media shortage and protocol change (from 40 to 30 mL) were retrospectively included. Eleven patients with reduced iodinated contrast media dosages and 11 propensity-score-matched controls with the standard iodinated contrast media dosages were included. A residual encoder-decoder convolutional neural network (RED-CNN) was trained for CTP denoising using k- space-weighted image average filtered CTP images as the target. The standard, RED-CNN-denoised, and k- space-weighted image average noise-filtered images for animal and human studies were compared for quantitative SNR and qualitative image evaluation.

RESULTS: The SNR of animal CTP images decreased with reductions in iodinated contrast media and milliampere-second doses. Contrast dose reduction had a greater effect on SNR than milliampere-second reduction. Noise-filtering by k- space-weighted image average and RED-CNN denoising progressively improved the SNR of CTP maps, with RED-CNN resulting in the highest SNR. The SNR of clinical CTP images was generally lower with a reduced iodinated contrast media dose, which was improved by the k- space-weighted image average and RED-CNN denoising ( P &lt; .05). Qualitative readings consistently rated RED-CNN denoised CTP as the best quality, followed by k- space-weighted image average and then standard CTP images.

CONCLUSIONS: Deep learning-denoising can improve image quality for low iodinated contrast media CTP protocols, and could approximate standard iodinated contrast media dose CTP, in addition to potentially improving image quality for low milliampere-second acquisitions.

AI
:   artificial intelligence
DL
:   deep learning
ICM
:   iodinated contrast media
KWIA
:   k- space-weighted image average
LCD
:   low contrast dose
PSNR
:   peak SNR
RED-CNN
:   residual encoder-decoder convolutional neural network
RMSE
:   root mean square error
SCD
:   standard contrast dose
SSIM
:   structural similarity index measure" />
<meta name="og-title" property="og:title" content="Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/10/1468" />
<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: Considering recent iodinated contrast shortages and a focus on reducing waste, developing protocols with lower contrast dosing while maintaining image quality through artificial intelligence is needed. This study compared reduced iodinated contrast media and standard dose CTP acquisitions, and the impact of deep learning denoising on CTP image quality in preclinical and clinical studies. The effect of reduced X-ray mAs dose was also investigated in preclinical studies.

MATERIALS AND METHODS: Twelve swine underwent 9 CTP examinations each, performed at combinations of 3 different x-ray (37, 67, and 127 mAs) and iodinated contrast media doses (10, 15, and 20 mL). Clinical CTP acquisitions performed before and during the iodinated contrast media shortage and protocol change (from 40 to 30 mL) were retrospectively included. Eleven patients with reduced iodinated contrast media dosages and 11 propensity-score-matched controls with the standard iodinated contrast media dosages were included. A residual encoder-decoder convolutional neural network (RED-CNN) was trained for CTP denoising using k- space-weighted image average filtered CTP images as the target. The standard, RED-CNN-denoised, and k- space-weighted image average noise-filtered images for animal and human studies were compared for quantitative SNR and qualitative image evaluation.

RESULTS: The SNR of animal CTP images decreased with reductions in iodinated contrast media and milliampere-second doses. Contrast dose reduction had a greater effect on SNR than milliampere-second reduction. Noise-filtering by k- space-weighted image average and RED-CNN denoising progressively improved the SNR of CTP maps, with RED-CNN resulting in the highest SNR. The SNR of clinical CTP images was generally lower with a reduced iodinated contrast media dose, which was improved by the k- space-weighted image average and RED-CNN denoising ( P &lt; .05). Qualitative readings consistently rated RED-CNN denoised CTP as the best quality, followed by k- space-weighted image average and then standard CTP images.

CONCLUSIONS: Deep learning-denoising can improve image quality for low iodinated contrast media CTP protocols, and could approximate standard iodinated contrast media dose CTP, in addition to potentially improving image quality for low milliampere-second acquisitions.

AI
:   artificial intelligence
DL
:   deep learning
ICM
:   iodinated contrast media
KWIA
:   k- space-weighted image average
LCD
:   low contrast dose
PSNR
:   peak SNR
RED-CNN
:   residual encoder-decoder convolutional neural network
RMSE
:   root mean square error
SCD
:   standard contrast dose
SSIM
:   structural similarity index measure" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node110792--5" data-node-nid="110792" id="top-node-110792--6377909230" data-pisa="ajnr;45/10/1468" data-pisa-master="ajnr;ajnr.A8367" data-apath="/ajnr/45/10/1468.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">Neurovascular/Stroke Imaging</span></span></div>
  
      <div class="highwire-cite-access"><span class="highwire-citation-access highwire-citation-access-check" data-pisa-id="ajnr;ajnr.A8367" data-atom-uri="/ajnr/45/10/1468.atom" data-request-view="full"></span></div>
  
      <h1 class="highwire-cite-title" id="page-title">Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Mahmud Mossa-Basha</span>, <span class="highwire-citation-author" data-delta="1">Chengcheng Zhu</span>, <span class="highwire-citation-author" data-delta="2">Tanya Pandhi</span>, <span class="highwire-citation-author" data-delta="3">Steve Mendoza</span>, <span class="highwire-citation-author" data-delta="4">Javid Azadbakht</span>, <span class="highwire-citation-author" data-delta="5">Ahmed Safwat</span>, <span class="highwire-citation-author" data-delta="6">Dean Homen</span>, <span class="highwire-citation-author" data-delta="7">Carlos Zamora</span>, <span class="highwire-citation-author" data-delta="8">Dinesh Kumar Gnanasekaran</span>, <span class="highwire-citation-author" data-delta="9">Ruiyue Peng</span>, <span class="highwire-citation-author" data-delta="10">Steven Cen</span>, <span class="highwire-citation-author" data-delta="11">Vinay Duddalwar</span>, <span class="highwire-citation-author" data-delta="12">Jeffry R. Alger</span> and <span class="highwire-citation-author" data-delta="13">Danny J.J. Wang</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">October 2024,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1468-1474; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8367 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-110792--6377909230" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Mahmud Mossa-Basha </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 (M.M.-B., C.Z., A.S), University of Washington, Seattle, Washington</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=Mahmud%2BMossa-Basha%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=Mossa-Basha%20M&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li 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D.J.J.W.), Keck School of Medicine, University of Southern California, 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=Tanya%2BPandhi%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=Pandhi%20T&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%3ATanya%2BPandhi%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">Steve Mendoza </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Mark and Mary Stevens Neuroimaging and Informatics Institute (T.P., S.M., D.K.G., D.J.J.W.), Keck School of Medicine, University of Southern California, 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=Steve%2BMendoza%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=Mendoza%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%3ASteve%2BMendoza%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">Javid Azadbakht </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Tabesh Radiology and Sonography (J.A.), Tehran, Iran</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=Javid%2BAzadbakht%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=Azadbakht%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%3AJavid%2BAzadbakht%2B" rel="nofollow" class="" data-icon-position="" 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Kumar Gnanasekaran </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Mark and Mary Stevens Neuroimaging and Informatics Institute (T.P., S.M., D.K.G., D.J.J.W.), Keck School of Medicine, University of Southern California, 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=Dinesh%2BKumar%2BGnanasekaran%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=Gnanasekaran%20DK&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 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      <div class="highwire-cite-title">Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study</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">Mahmud</span> <span class="nlm-surname">Mossa-Basha</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Chengcheng</span> <span class="nlm-surname">Zhu</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Tanya</span> <span class="nlm-surname">Pandhi</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Steve</span> <span class="nlm-surname">Mendoza</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Javid</span> <span class="nlm-surname">Azadbakht</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Ahmed</span> <span class="nlm-surname">Safwat</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Dean</span> <span class="nlm-surname">Homen</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Carlos</span> <span class="nlm-surname">Zamora</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Dinesh Kumar</span> <span class="nlm-surname">Gnanasekaran</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Ruiyue</span> <span class="nlm-surname">Peng</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Steven</span> <span class="nlm-surname">Cen</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">Vinay</span> <span class="nlm-surname">Duddalwar</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">Jeffry R.</span> <span class="nlm-surname">Alger</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">Danny J.J.</span> <span class="nlm-surname">Wang</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">Oct 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1468-1474; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8367 </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">Mahmud</span> <span class="nlm-surname">Mossa-Basha</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Chengcheng</span> <span class="nlm-surname">Zhu</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Tanya</span> <span class="nlm-surname">Pandhi</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Steve</span> <span class="nlm-surname">Mendoza</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Javid</span> <span class="nlm-surname">Azadbakht</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Ahmed</span> <span class="nlm-surname">Safwat</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Dean</span> <span class="nlm-surname">Homen</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Carlos</span> <span class="nlm-surname">Zamora</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Dinesh Kumar</span> <span class="nlm-surname">Gnanasekaran</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Ruiyue</span> <span class="nlm-surname">Peng</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Steven</span> <span class="nlm-surname">Cen</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">Vinay</span> <span class="nlm-surname">Duddalwar</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">Jeffry R.</span> <span class="nlm-surname">Alger</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">Danny J.J.</span> <span class="nlm-surname">Wang</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">Oct 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1468-1474; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8367 </span></div>
  
  
  
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