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<meta name="DC.Title" content="Transcranial MR Imaging–Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT" />
<meta name="DC.Identifier" content="10.3174/ajnr.A6758" />
<meta name="DC.Date" content="2020-10-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Transcranial MR imaging–guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging–guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging–guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging–guided focused ultrasound treatment planning.

MATERIALS AND METHODS: The U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging–guided focused ultrasound.

RESULTS: The derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation ( r  = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT.

CONCLUSIONS: The deep learning method can be used to simplify workflow associated with transcranial MR imaging–guided focused ultrasound.

DL
:   deep learning
MAE
:   mean absolute error
SDR
:   skull-density ratio
tcMRgFUS
:   transcranial MR imaging–guided focused ultrasound
UTE
:   ultrashort TE" />
<meta name="DC.Contributor" content="P. Su" />
<meta name="DC.Contributor" content="S. Guo" />
<meta name="DC.Contributor" content="S. Roys" />
<meta name="DC.Contributor" content="F. Maier" />
<meta name="DC.Contributor" content="H. Bhat" />
<meta name="DC.Contributor" content="E.R. Melhem" />
<meta name="DC.Contributor" content="D. Gandhi" />
<meta name="DC.Contributor" content="R. Gullapalli" />
<meta name="DC.Contributor" content="J. Zhuo" />
<meta name="article:published_time" content="2020-10-01" />
<meta name="article:section" content="Interventional" />
<meta name="citation_title" content="Transcranial MR Imaging–Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Transcranial MR imaging–guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging–guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging–guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging–guided focused ultrasound treatment planning.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;The U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging–guided focused ultrasound.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation (&lt;i&gt;r&lt;/i&gt; = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The deep learning method can be used to simplify workflow associated with transcranial MR imaging–guided focused ultrasound.&lt;/p&gt;" />
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<meta name="twitter:title" content="Transcranial MR Imaging–Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Transcranial MR imaging–guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging–guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging–guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging–guided focused ultrasound treatment planning.

MATERIALS AND METHODS: The U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging–guided focused ultrasound.

RESULTS: The derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation ( r  = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT.

CONCLUSIONS: The deep learning method can be used to simplify workflow associated with transcranial MR imaging–guided focused ultrasound.

DL
:   deep learning
MAE
:   mean absolute error
SDR
:   skull-density ratio
tcMRgFUS
:   transcranial MR imaging–guided focused ultrasound
UTE
:   ultrashort TE" />
<meta name="og-title" property="og:title" content="Transcranial MR Imaging–Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/41/10/1841" />
<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: Transcranial MR imaging–guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging–guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging–guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging–guided focused ultrasound treatment planning.

MATERIALS AND METHODS: The U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging–guided focused ultrasound.

RESULTS: The derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation ( r  = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT.

CONCLUSIONS: The deep learning method can be used to simplify workflow associated with transcranial MR imaging–guided focused ultrasound.

DL
:   deep learning
MAE
:   mean absolute error
SDR
:   skull-density ratio
tcMRgFUS
:   transcranial MR imaging–guided focused ultrasound
UTE
:   ultrashort TE" />
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      <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">Interventional</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Transcranial MR Imaging–Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">P. Su</span>, <span class="highwire-citation-author" data-delta="1">S. Guo</span>, <span class="highwire-citation-author" data-delta="2">S. Roys</span>, <span class="highwire-citation-author" data-delta="3">F. Maier</span>, <span class="highwire-citation-author" data-delta="4">H. Bhat</span>, <span class="highwire-citation-author" data-delta="5">E.R. Melhem</span>, <span class="highwire-citation-author" data-delta="6">D. Gandhi</span>, <span class="highwire-citation-author" data-delta="7">R. Gullapalli</span> and <span class="highwire-citation-author" data-delta="8">J. Zhuo</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 2020,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1841-1848; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A6758 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-78786--61951072967" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">P. Su </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 Diagnostic Radiology and Nuclear Medicine (P.S., S.G., S.R., E.R.M., D.G., R.G., J.Z.), University of Maryland School of Medicine, Baltimore, Maryland</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Siemens Medical Solutions USA (P.S., H.B.), Malvern, Pennsylvania</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=P.%2BSu%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=Su%20P&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%3AP.%2BSu%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/0000-0002-6728-5878" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for P. Su</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">S. Guo </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 Diagnostic Radiology and Nuclear Medicine (P.S., S.G., S.R., E.R.M., D.G., R.G., J.Z.), University of Maryland School of Medicine, Baltimore, Maryland</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Center for Metabolic Imaging and Therapeutics (S.G., S.R., R.G., J.Z.), University of Maryland Medical Center, Baltimore, Maryland</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.%2BGuo%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=Guo%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.%2BGuo%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/0000-0003-3815-3822" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S. Guo</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">S. Roys </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 Diagnostic Radiology and Nuclear Medicine (P.S., S.G., S.R., E.R.M., D.G., R.G., J.Z.), University of Maryland School of Medicine, Baltimore, Maryland</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Center for Metabolic Imaging and Therapeutics (S.G., S.R., R.G., J.Z.), University of Maryland Medical Center, Baltimore, Maryland</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.%2BRoys%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=Roys%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.%2BRoys%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/0000-0001-8685-213X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S. Roys</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">F. Maier </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Siemens Healthcare GmbH (F.M.), Erlangen, Germany</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=F.%2BMaier%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=Maier%20F&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%3AF.%2BMaier%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/0000-0003-1254-6556" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for F. Maier</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">H. Bhat </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Siemens Medical Solutions USA (P.S., H.B.), Malvern, Pennsylvania</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.%2BBhat%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=Bhat%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.%2BBhat%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/0000-0002-2503-2207" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H. Bhat</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">E.R. Melhem </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 Diagnostic Radiology and Nuclear Medicine (P.S., S.G., S.R., E.R.M., D.G., R.G., J.Z.), University of Maryland School of Medicine, Baltimore, Maryland</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=E.R.%2BMelhem%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=Melhem%20E&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AE.R.%2BMelhem%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/0000-0001-5869-9449" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for E.R. Melhem</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">D. Gandhi </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 Diagnostic Radiology and Nuclear Medicine (P.S., S.G., S.R., E.R.M., D.G., R.G., J.Z.), University of Maryland School of Medicine, Baltimore, Maryland</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=D.%2BGandhi%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=Gandhi%20D&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AD.%2BGandhi%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/0000-0001-6741-5970" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for D. 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Gullapalli </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 Diagnostic Radiology and Nuclear Medicine (P.S., S.G., S.R., E.R.M., D.G., R.G., J.Z.), University of Maryland School of Medicine, Baltimore, Maryland</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Center for Metabolic Imaging and Therapeutics (S.G., S.R., R.G., J.Z.), University of Maryland Medical Center, Baltimore, Maryland</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.%2BGullapalli%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=Gullapalli%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.%2BGullapalli%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/0000-0003-0551-0379" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R. Gullapalli</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">J. Zhuo </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 Diagnostic Radiology and Nuclear Medicine (P.S., S.G., S.R., E.R.M., D.G., R.G., J.Z.), University of Maryland School of Medicine, Baltimore, Maryland</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Center for Metabolic Imaging and Therapeutics (S.G., S.R., R.G., J.Z.), University of Maryland Medical Center, Baltimore, Maryland</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.%2BZhuo%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=Zhuo%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.%2BZhuo%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/0000-0001-6637-701X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. 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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" xmlns:xhtml="http://www.w3.org/1999/xhtml" class="section ref-list" id="ref-list-1"><h2 class="">References</h2><ol class="cit-list ref-use-labels"><li><span class="ref-label">1.</span><a class="rev-xref-ref" href="#xref-ref-1-1" title="View reference 1. in text" id="ref-1">↵</a><div class="cit ref-cit ref-journal" id="cit-41.10.1841.1" data-doi="10.1056/NEJMoa1300962"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Elias</span>  <span class="cit-name-given-names">WJ</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Huss</span>  <span class="cit-name-given-names">D</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Voss</span>  <span class="cit-name-given-names">T</span></span>, <span class="cit-etal">et al</span></li></ol><cite>. <span class="cit-article-title">A pilot study of focused ultrasound 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 41</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Oct 2020 </div></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">Oct 2020, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1841-1848; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6758 </span></div>
  
  
  
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      <div class="highwire-cite-title">Transcranial MR Imaging–Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT</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">P.</span> <span class="nlm-surname">Su</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Guo</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Roys</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Maier</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">H.</span> <span class="nlm-surname">Bhat</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">E.R.</span> <span class="nlm-surname">Melhem</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Gandhi</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">R.</span> <span class="nlm-surname">Gullapalli</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Zhuo</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 2020, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1841-1848; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6758 </span></div>
  
  
  
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