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<meta name="DC.Title" content="Deep Learning–Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7920" />
<meta name="DC.Date" content="2023-08-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2023 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR.

MATERIALS AND METHODS: From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, n = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, n = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%.

RESULTS: For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and t tests showed higher image-quality scores for synthetically created STIR over acquired STIR ( P  &lt; .0001).

CONCLUSIONS: Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.

Acq-STIR
:   acquired STIR
CNN
:   convolutional neural network
DL
:   deep learning
IQ
:   image quality
RMSE
:   root mean square error
RMSPE
:   root mean square percentage error
Syn-STIR
:   synthetically created STIR" />
<meta name="DC.Contributor" content="L.N. Tanenbaum" />
<meta name="DC.Contributor" content="S.C. Bash" />
<meta name="DC.Contributor" content="G. Zaharchuk" />
<meta name="DC.Contributor" content="A. Shankaranarayanan" />
<meta name="DC.Contributor" content="R. Chamberlain" />
<meta name="DC.Contributor" content="M. Wintermark" />
<meta name="DC.Contributor" content="C. Beaulieu" />
<meta name="DC.Contributor" content="M. Novick" />
<meta name="DC.Contributor" content="L. Wang" />
<meta name="article:published_time" content="2023-08-01" />
<meta name="article:section" content="Spine" />
<meta name="citation_title" content="Deep Learning–Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, &lt;i&gt;n&lt;/i&gt; = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, &lt;i&gt;n&lt;/i&gt; = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and &lt;i&gt;t&lt;/i&gt; tests showed higher image-quality scores for synthetically created STIR over acquired STIR (&lt;i&gt;P &lt;/i&gt;&amp;lt; .0001).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.&lt;/p&gt;" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR.

MATERIALS AND METHODS: From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, n = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, n = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%.

RESULTS: For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and t tests showed higher image-quality scores for synthetically created STIR over acquired STIR ( P  &lt; .0001).

CONCLUSIONS: Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.

Acq-STIR
:   acquired STIR
CNN
:   convolutional neural network
DL
:   deep learning
IQ
:   image quality
RMSE
:   root mean square error
RMSPE
:   root mean square percentage error
Syn-STIR
:   synthetically created STIR" />
<meta name="og-title" property="og:title" content="Deep Learning–Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/44/8/987" />
<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: Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR.

MATERIALS AND METHODS: From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, n = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, n = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%.

RESULTS: For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and t tests showed higher image-quality scores for synthetically created STIR over acquired STIR ( P  &lt; .0001).

CONCLUSIONS: Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.

Acq-STIR
:   acquired STIR
CNN
:   convolutional neural network
DL
:   deep learning
IQ
:   image quality
RMSE
:   root mean square error
RMSPE
:   root mean square percentage error
Syn-STIR
:   synthetically created STIR" />
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    <title>Deep Learning–Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial | American Journal of Neuroradiology</title>  
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      <h1 class="highwire-cite-title" id="page-title">Deep Learning–Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">L.N. Tanenbaum</span>, <span class="highwire-citation-author" data-delta="1">S.C. Bash</span>, <span class="highwire-citation-author" data-delta="2">G. Zaharchuk</span>, <span class="highwire-citation-author" data-delta="3">A. Shankaranarayanan</span>, <span class="highwire-citation-author" data-delta="4">R. Chamberlain</span>, <span class="highwire-citation-author" data-delta="5">M. Wintermark</span>, <span class="highwire-citation-author" data-delta="6">C. Beaulieu</span>, <span class="highwire-citation-author" data-delta="7">M. Novick</span> and <span class="highwire-citation-author" data-delta="8">L. 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">August 2023,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(8) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">987-993; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7920 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-103503--61899781701" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">L.N. Tanenbaum </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From RadNet (L.N.T., S.C.B.), New York, New York</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.N.%2BTanenbaum%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=Tanenbaum%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"><a href="/search/author1%3AL.N.%2BTanenbaum%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0003-3326-3192" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L.N. Tanenbaum</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">S.C. Bash </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From RadNet (L.N.T., S.C.B.), New York, New York</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.C.%2BBash%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=Bash%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"><a href="/search/author1%3AS.C.%2BBash%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-2572-1010" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S.C. Bash</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">G. Zaharchuk </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Stanford University Medical Center (G.Z., C.B.), Stanford, 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=G.%2BZaharchuk%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=Zaharchuk%20G&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%3AG.%2BZaharchuk%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0001-5781-8848" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for G. Zaharchuk</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">A. Shankaranarayanan </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Subtle Medical (A.S., R.C., L.W.), Menlo Park, 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=A.%2BShankaranarayanan%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=Shankaranarayanan%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"><a href="/search/author1%3AA.%2BShankaranarayanan%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0001-9395-8768" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Shankaranarayanan</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">R. Chamberlain </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Subtle Medical (A.S., R.C., L.W.), Menlo Park, 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=R.%2BChamberlain%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=Chamberlain%20R&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%3AR.%2BChamberlain%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-2705-1268" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R. Chamberlain</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">M. Wintermark </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>MD Anderson Cancer Center (M.W.), University of Texas, Houston, Texas</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.%2BWintermark%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=Wintermark%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"><a href="/search/author1%3AM.%2BWintermark%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-6726-3951" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M. Wintermark</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">C. Beaulieu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Stanford University Medical Center (G.Z., C.B.), Stanford, 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=C.%2BBeaulieu%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=Beaulieu%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"><a href="/search/author1%3AC.%2BBeaulieu%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-6036-1962" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for C. 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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">L.N.</span> <span class="nlm-surname">Tanenbaum</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">S.C.</span> <span class="nlm-surname">Bash</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">G.</span> <span class="nlm-surname">Zaharchuk</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Shankaranarayanan</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">R.</span> <span class="nlm-surname">Chamberlain</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Wintermark</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Beaulieu</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Novick</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">L.</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">Aug 2023, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(8) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">987-993; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7920 </span></div>
  
  
  
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      <div class="highwire-cite-title">Deep Learning–Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial</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.N.</span> <span class="nlm-surname">Tanenbaum</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">S.C.</span> <span class="nlm-surname">Bash</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">G.</span> <span class="nlm-surname">Zaharchuk</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Shankaranarayanan</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">R.</span> <span class="nlm-surname">Chamberlain</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Wintermark</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Beaulieu</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Novick</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">L.</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">Aug 2023, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(8) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">987-993; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7920 </span></div>
  
  
  
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