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<meta name="DC.Title" content="Thin-Slice Pituitary MRI with Deep Learning–Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7387" />
<meta name="DC.Date" content="2022-02-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2022 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning–based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI.

MATERIALS AND METHODS: This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery.

RESULTS: The diagnostic performance of 1-mm-slice-thickness MRI + deep learning–based reconstruction (area under the curve, 0.79; 95% CI, 0.69 − 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52–0.70; P  &lt; .001). One-millimeter-slice-thickness MRI + deep learning–based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, P  &lt; .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI + deep learning–based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, P  &lt; .001; &gt;270°, 38.8% versus 16.4%, P  &lt; .001).

CONCLUSIONS: Compared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning–based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.

AUC
:   area under the receiver operating characteristic curve
CNN
:   convolutional neural network
DLR
:   deep learning–based reconstruction
1-mmMRI
:   1-mm-slice-thickness MRI without deep learning–based reconstruction
3-mmMRI
:   3-mm-slice-thickness MRI
1-mmMRI+DLR
:   1-mm-slice-thickness MRI with deep learning–based reconstruction
TSA
:   transsphenoidal approach" />
<meta name="DC.Contributor" content="M. Kim" />
<meta name="DC.Contributor" content="H.S. Kim" />
<meta name="DC.Contributor" content="J.E. Park" />
<meta name="DC.Contributor" content="S.Y. Park" />
<meta name="DC.Contributor" content="Y.-H. Kim" />
<meta name="DC.Contributor" content="S.J. Kim" />
<meta name="DC.Contributor" content="J. Lee" />
<meta name="DC.Contributor" content="M.R. Lebel" />
<meta name="article:published_time" content="2022-02-01" />
<meta name="article:section" content="Head &amp;amp; Neck" />
<meta name="citation_title" content="Thin-Slice Pituitary MRI with Deep Learning–Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning–based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The diagnostic performance of 1-mm-slice-thickness MRI + deep learning–based reconstruction (area under the curve, 0.79; 95% CI, 0.69 − 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52–0.70; &lt;i&gt;P &lt;/i&gt;&amp;lt; .001). One-millimeter-slice-thickness MRI + deep learning–based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, &lt;i&gt;P &lt;/i&gt;&amp;lt; .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI + deep learning–based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, &lt;i&gt;P &lt;/i&gt;&amp;lt; .001; &amp;gt;270°, 38.8% versus 16.4%, &lt;i&gt;P &lt;/i&gt;&amp;lt; .001).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Compared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning–based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
<meta name="citation_publisher" content="American Journal of Neuroradiology" />
<meta name="citation_publication_date" content="2022/02/01" />
<meta name="citation_mjid" content="ajnr;43/2/280" />
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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.A7387" />
<meta name="citation_pmid" content="34992127" />
<meta name="citation_volume" content="43" />
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<meta name="citation_firstpage" content="280" />
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<meta name="twitter:title" content="Thin-Slice Pituitary MRI with Deep Learning–Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning–based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI.

MATERIALS AND METHODS: This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery.

RESULTS: The diagnostic performance of 1-mm-slice-thickness MRI + deep learning–based reconstruction (area under the curve, 0.79; 95% CI, 0.69 − 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52–0.70; P  &lt; .001). One-millimeter-slice-thickness MRI + deep learning–based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, P  &lt; .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI + deep learning–based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, P  &lt; .001; &gt;270°, 38.8% versus 16.4%, P  &lt; .001).

CONCLUSIONS: Compared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning–based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.

AUC
:   area under the receiver operating characteristic curve
CNN
:   convolutional neural network
DLR
:   deep learning–based reconstruction
1-mmMRI
:   1-mm-slice-thickness MRI without deep learning–based reconstruction
3-mmMRI
:   3-mm-slice-thickness MRI
1-mmMRI+DLR
:   1-mm-slice-thickness MRI with deep learning–based reconstruction
TSA
:   transsphenoidal approach" />
<meta name="og-title" property="og:title" content="Thin-Slice Pituitary MRI with Deep Learning–Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/43/2/280" />
<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: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning–based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI.

MATERIALS AND METHODS: This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning–based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery.

RESULTS: The diagnostic performance of 1-mm-slice-thickness MRI + deep learning–based reconstruction (area under the curve, 0.79; 95% CI, 0.69 − 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52–0.70; P  &lt; .001). One-millimeter-slice-thickness MRI + deep learning–based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, P  &lt; .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI + deep learning–based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, P  &lt; .001; &gt;270°, 38.8% versus 16.4%, P  &lt; .001).

CONCLUSIONS: Compared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning–based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.

AUC
:   area under the receiver operating characteristic curve
CNN
:   convolutional neural network
DLR
:   deep learning–based reconstruction
1-mmMRI
:   1-mm-slice-thickness MRI without deep learning–based reconstruction
3-mmMRI
:   3-mm-slice-thickness MRI
1-mmMRI+DLR
:   1-mm-slice-thickness MRI with deep learning–based reconstruction
TSA
:   transsphenoidal approach" />
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      <h1 class="highwire-cite-title" id="page-title">Thin-Slice Pituitary MRI with Deep Learning–Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">M. Kim</span>, <span class="highwire-citation-author" data-delta="1">H.S. Kim</span>, <span class="highwire-citation-author" data-delta="2">J.E. Park</span>, <span class="highwire-citation-author" data-delta="3">S.Y. Park</span>, <span class="highwire-citation-author" data-delta="4">Y.-H. Kim</span>, <span class="highwire-citation-author" data-delta="5">S.J. Kim</span>, <span class="highwire-citation-author" data-delta="6">J. Lee</span> and <span class="highwire-citation-author" data-delta="7">M.R. Lebel</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">February 2022,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(2) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">280-285; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7387 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-95481--6769063695" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">M. Kim </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 and Research Institute of Radiology (M.K., H.S.K., J.E.P., S.J.K.)</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.%2BKim%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=Kim%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.%2BKim%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/0000-0002-5382-9360" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M. Kim</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">H.S. Kim </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 and Research Institute of Radiology (M.K., H.S.K., J.E.P., S.J.K.)</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=H.S.%2BKim%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=Kim%20H&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%3AH.S.%2BKim%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/0000-0002-9477-7421" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H.S. Kim</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">J.E. Park </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 and Research Institute of Radiology (M.K., H.S.K., J.E.P., S.J.K.)</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=J.E.%2BPark%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=Park%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%3AJ.E.%2BPark%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/0000-0002-4419-4682" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J.E. Park</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">S.Y. Park </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Departments of Clinical Epidemiology and Biostatistics (S.Y.P.)</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.Y.%2BPark%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=Park%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.Y.%2BPark%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/0000-0002-2702-1536" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S.Y. Park</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Y.-H. Kim </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Neurosurgery (Y.-H.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea</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=Y.-H.%2BKim%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=Kim%20Y&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%3AY.-H.%2BKim%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/0000-0002-8852-6503" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Y.-H. Kim</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">S.J. Kim </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 and Research Institute of Radiology (M.K., H.S.K., J.E.P., S.J.K.)</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.J.%2BKim%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=Kim%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.J.%2BKim%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/0000-0001-7070-7333" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S.J. Kim</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">J. Lee </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>GE Healthcare (J.L.), Seoul, Korea</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=J.%2BLee%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=Lee%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%3AJ.%2BLee%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/0000-0002-0164-1139" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Lee</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">M.R. Lebel </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>GE Healthcare (M.R.L.), Calgary, Alberta, Canada</div><div class='author-affiliation'><span class='nlm-sup'>f</span>Department of Radiology (M.R.L.), University of Calgary, Calgary, Alberta, Canada</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.R.%2BLebel%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=Lebel%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.R.%2BLebel%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/0000-0002-7526-9974" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M.R. 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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">Feb 2022, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(2) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">280-285; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7387 </span></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">Feb 2022, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(2) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">280-285; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7387 </span></div>
  
  
  
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