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<meta name="DC.Title" content="Investigating Simultaneity for Deep Learning–Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7410" />
<meta name="DC.Date" content="2022-03-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2022 by American Journal of Neuroradiology. Indicates open access to non-subscribers at www.ajnr.org" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging–assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

MATERIALS AND METHODS: We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [18F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [18F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90–110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net–based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads.

RESULTS: Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.

CONCLUSIONS: This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.

CNN
:   convolutional neural network
NS
:   nonsimultaneous
S
:   simultaneous
SUVR
:   standard uptake value ratio" />
<meta name="DC.Contributor" content="K.T. Chen" />
<meta name="DC.Contributor" content="O. Adeyeri" />
<meta name="DC.Contributor" content="T.N. Toueg" />
<meta name="DC.Contributor" content="M. Zeineh" />
<meta name="DC.Contributor" content="E. Mormino" />
<meta name="DC.Contributor" content="M. Khalighi" />
<meta name="DC.Contributor" content="G. Zaharchuk" />
<meta name="article:published_time" content="2022-03-01" />
<meta name="article:section" content="Adult Brain" />
<meta name="citation_title" content="Investigating Simultaneity for Deep Learning–Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging–assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [&lt;sup&gt;18&lt;/sup&gt;F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [&lt;sup&gt;18&lt;/sup&gt;F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90–110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net–based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_publication_date" content="2022/03/01" />
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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.A7410" />
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<meta name="citation_author" content="K.T. Chen" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California" />
<meta name="citation_author_institution" content="bDepartment of Biomedical Engineering (K.T.C.), National Taiwan University, Taipei, Taiwan" />
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<meta name="twitter:title" content="Investigating Simultaneity for Deep Learning–Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging–assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

MATERIALS AND METHODS: We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [18F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [18F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90–110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net–based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads.

RESULTS: Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.

CONCLUSIONS: This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.

CNN
:   convolutional neural network
NS
:   nonsimultaneous
S
:   simultaneous
SUVR
:   standard uptake value ratio" />
<meta name="og-title" property="og:title" content="Investigating Simultaneity for Deep Learning–Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/43/3/354" />
<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: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging–assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

MATERIALS AND METHODS: We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [18F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [18F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90–110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net–based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads.

RESULTS: Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.

CONCLUSIONS: This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.

CNN
:   convolutional neural network
NS
:   nonsimultaneous
S
:   simultaneous
SUVR
:   standard uptake value ratio" />
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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">Adult Brain</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Investigating Simultaneity for Deep Learning–Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">K.T. Chen</span>, <span class="highwire-citation-author" data-delta="1">O. Adeyeri</span>, <span class="highwire-citation-author" data-delta="2">T.N. Toueg</span>, <span class="highwire-citation-author" data-delta="3">M. Zeineh</span>, <span class="highwire-citation-author" data-delta="4">E. Mormino</span>, <span class="highwire-citation-author" data-delta="5">M. Khalighi</span> and <span class="highwire-citation-author" data-delta="6">G. Zaharchuk</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">March 2022,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(3) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">354-360; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7410 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-96092--61305448600" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">K.T. Chen </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 (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Biomedical Engineering (K.T.C.), National Taiwan University, Taipei, Taiwan</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=K.T.%2BChen%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=Chen%20K&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%3AK.T.%2BChen%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/0000-0001-8556-5307" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for K.T. Chen</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">O. Adeyeri </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Computer Science (O.A.), Salem State University, Salem, Massachusetts</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=O.%2BAdeyeri%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=Adeyeri%20O&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%3AO.%2BAdeyeri%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/0000-0001-7654-4168" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for O. Adeyeri</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">T.N. Toueg </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, 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=T.N.%2BToueg%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=Toueg%20T&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AT.N.%2BToueg%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/0000-0002-1925-1622" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for T.N. Toueg</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">M. Zeineh </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 (K.T.C., M.Z., M.K., G.Z.), Stanford University, 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=M.%2BZeineh%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=Zeineh%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.%2BZeineh%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/0000-0002-6940-9096" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M. Zeineh</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">E. Mormino </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, 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=E.%2BMormino%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=Mormino%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.%2BMormino%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/0000-0002-4542-6537" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for E. 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Khalighi </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 (K.T.C., M.Z., M.K., G.Z.), Stanford University, 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=M.%2BKhalighi%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=Khalighi%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.%2BKhalighi%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/0000-0001-5623-1922" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M. 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