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<meta name="DC.Title" content="Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8343" />
<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: Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs.

MATERIALS AND METHODS: The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years.

RESULTS: The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set.

CONCLUSIONS: An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.

IoU
:   intersection over union
m2ABQ
:   modified-2 algorithm based qualitative fracture classification system
MrOS
:   Osteoporotic Fractures in Men Study
OCF
:   osteoporotic compression fracture
PPV
:   positive predictive value
VB
:   vertebral body" />
<meta name="DC.Contributor" content="Brian C. Chang" />
<meta name="DC.Contributor" content="Jonathan Renslo" />
<meta name="DC.Contributor" content="Qifei Dong" />
<meta name="DC.Contributor" content="Sandra K. Johnston" />
<meta name="DC.Contributor" content="Jessica Perry" />
<meta name="DC.Contributor" content="David R. Haynor" />
<meta name="DC.Contributor" content="Gang Luo" />
<meta name="DC.Contributor" content="Nancy E. Lane" />
<meta name="DC.Contributor" content="Jeffrey G. Jarvik" />
<meta name="DC.Contributor" content="Nathan M. Cross" />
<meta name="article:published_time" content="2024-10-01" />
<meta name="article:section" content="Artificial Intelligence" />
<meta name="citation_title" content="Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_author" content="Brian C. Chang" />
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<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-8327-2084" />
<meta name="citation_author" content="Jonathan Renslo" />
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<meta name="citation_author" content="Qifei Dong" />
<meta name="citation_author_institution" content="aFrom the Departments of Biomedical Informatics and Medical Education (B.C.C., Q.D., G.L.), University of Washington, Seattle, Washington" />
<meta name="citation_author" content="Sandra K. Johnston" />
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<meta name="twitter:title" content="Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs" />
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<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/45/10.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs.

MATERIALS AND METHODS: The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years.

RESULTS: The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set.

CONCLUSIONS: An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.

IoU
:   intersection over union
m2ABQ
:   modified-2 algorithm based qualitative fracture classification system
MrOS
:   Osteoporotic Fractures in Men Study
OCF
:   osteoporotic compression fracture
PPV
:   positive predictive value
VB
:   vertebral body" />
<meta name="og-title" property="og:title" content="Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/10/1512" />
<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: Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs.

MATERIALS AND METHODS: The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years.

RESULTS: The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set.

CONCLUSIONS: An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.

IoU
:   intersection over union
m2ABQ
:   modified-2 algorithm based qualitative fracture classification system
MrOS
:   Osteoporotic Fractures in Men Study
OCF
:   osteoporotic compression fracture
PPV
:   positive predictive value
VB
:   vertebral body" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node110789--5" data-node-nid="110789" id="top-node-110789--61677014603" data-pisa="ajnr;45/10/1512" data-pisa-master="ajnr;ajnr.A8343" data-apath="/ajnr/45/10/1512.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">Artificial Intelligence</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Brian C. Chang</span>, <span class="highwire-citation-author" data-delta="1">Jonathan Renslo</span>, <span class="highwire-citation-author" data-delta="2">Qifei Dong</span>, <span class="highwire-citation-author" data-delta="3">Sandra K. Johnston</span>, <span class="highwire-citation-author" data-delta="4">Jessica Perry</span>, <span class="highwire-citation-author" data-delta="5">David R. Haynor</span>, <span class="highwire-citation-author" data-delta="6">Gang Luo</span>, <span class="highwire-citation-author" data-delta="7">Nancy E. Lane</span>, <span class="highwire-citation-author" data-delta="8">Jeffrey G. Jarvik</span> and <span class="highwire-citation-author" data-delta="9">Nathan M. Cross</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">1512-1520; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8343 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-110789--61677014603" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Brian C. Chang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Biomedical Informatics and Medical Education (B.C.C., Q.D., G.L.), 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=Brian%2BC.%2BChang%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=Chang%20BC&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%3ABrian%2BC.%2BChang%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/0000-0001-8327-2084" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Brian C. Chang</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Jonathan Renslo </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Keck School of Medicine (J.R.), 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=Jonathan%2BRenslo%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=Renslo%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%3AJonathan%2BRenslo%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">Qifei Dong </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Biomedical Informatics and Medical Education (B.C.C., Q.D., G.L.), 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=Qifei%2BDong%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=Dong%20Q&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%3AQifei%2BDong%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">Sandra K. Johnston </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Departments of Radiology (S.K.J., D.R.H., J.G.J., N.M.C.), 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=Sandra%2BK.%2BJohnston%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=Johnston%20SK&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%3ASandra%2BK.%2BJohnston%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">Jessica Perry </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Departments of Biostatistics (J.P.), 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=Jessica%2BPerry%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=Perry%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%3AJessica%2BPerry%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-5"><div class="author-tooltip-name">David R. 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<span class='nlm-sup'>f</span>Departments of Neurological Surgery (J.G.J.), 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=Jeffrey%2BG.%2BJarvik%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=Jarvik%20JG&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%3AJeffrey%2BG.%2BJarvik%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/0000-0002-0528-3455" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Jeffrey G. Jarvik</a></li></ul></div><div class="author-tooltip-9"><div class="author-tooltip-name">Nathan M. Cross </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Departments of Radiology (S.K.J., D.R.H., J.G.J., N.M.C.), 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=Nathan%2BM.%2BCross%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=Cross%20NM&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%3ANathan%2BM.%2BCross%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/0000-0002-5040-4340" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Nathan M. Cross</a></li></ul></div></div></div>  </div>

  
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 45</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Oct 2024 </div></div>
      
      
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      <div class="highwire-cite-title">Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs</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">Brian C.</span> <span class="nlm-surname">Chang</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Jonathan</span> <span class="nlm-surname">Renslo</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Qifei</span> <span class="nlm-surname">Dong</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Sandra K.</span> <span class="nlm-surname">Johnston</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Jessica</span> <span class="nlm-surname">Perry</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">David R.</span> <span class="nlm-surname">Haynor</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Gang</span> <span class="nlm-surname">Luo</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Nancy E.</span> <span class="nlm-surname">Lane</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Jeffrey G.</span> <span class="nlm-surname">Jarvik</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Nathan M.</span> <span class="nlm-surname">Cross</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">1512-1520; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8343 </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">Brian C.</span> <span class="nlm-surname">Chang</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Jonathan</span> <span class="nlm-surname">Renslo</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Qifei</span> <span class="nlm-surname">Dong</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Sandra K.</span> <span class="nlm-surname">Johnston</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Jessica</span> <span class="nlm-surname">Perry</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">David R.</span> <span class="nlm-surname">Haynor</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Gang</span> <span class="nlm-surname">Luo</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Nancy E.</span> <span class="nlm-surname">Lane</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Jeffrey G.</span> <span class="nlm-surname">Jarvik</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Nathan M.</span> <span class="nlm-surname">Cross</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">1512-1520; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8343 </span></div>
  
  
  
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