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<meta name="citation_funding_source" content="citation_funder_id=https://doi.org/10.13039/100000065;citation_grant_number=R01-NS092207;citation_grant_number=R01-NS125635;" />
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<meta name="HW.identifier" content="/ajnr/early/2024/11/14/ajnr.A8424.atom" />
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<meta name="DC.Language" content="en" />
<meta name="DC.Title" content="DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning–Based Image Quality Improvement for Vessel Wall MRI" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8424" />
<meta name="DC.Date" content="2024-11-14" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2025 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times.

MATERIALS AND METHODS: Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed.

RESULTS: DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1–1.75] versus 3 [3–4], P &lt; .001) and venous flow artifacts (1 [1–2] versus 3 [3–4], P &lt; .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1–2], versus 3 [2–3], P &lt; .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) ( P &lt; .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL.

CONCLUSIONS: DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.

CAIPI
:   controlled aliasing in parallel imaging
CNR
:   contrast-to-noise ratio
DANTE
:   delay alternating with nutation for tailored excitation
DL
:   deep learning
ICC
:   intraclass correlation coefficient
IVW
:   intracranial vessel wall MRI
SOC
:   standard of care
SPACE
:   sampling perfection with application-optimized contrasts by using different flip angle evolution" />
<meta name="DC.Contributor" content="Mona Kharaji" />
<meta name="DC.Contributor" content="Gador Canton" />
<meta name="DC.Contributor" content="Yin Guo" />
<meta name="DC.Contributor" content="Mohamad Hosaam Mosi" />
<meta name="DC.Contributor" content="Zechen Zhou" />
<meta name="DC.Contributor" content="Niranjan Balu" />
<meta name="DC.Contributor" content="Mahmud Mossa-Basha" />
<meta name="article:published_time" content="2024-11-14" />
<meta name="article:section" content="NEUROVASCULAR/STROKE IMAGING" />
<meta name="citation_title" content="DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning–Based Image Quality Improvement for Vessel Wall MRI" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1–1.75] versus 3 [3–4], &lt;i&gt;P&lt;/i&gt; &amp;lt; .001) and venous flow artifacts (1 [1–2] versus 3 [3–4], &lt;i&gt;P&lt;/i&gt; &amp;lt; .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1–2], versus 3 [2–3], &lt;i&gt;P&lt;/i&gt; &amp;lt; .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) (&lt;i&gt;P&lt;/i&gt; &amp;lt; .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.&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="2024/11/14" />
<meta name="citation_mjid" content="ajnr;ajnr.A8424v2" />
<meta name="citation_id" content="ajnr.A8424v2" />
<meta name="citation_public_url" content="http://ajnr-stage2.stage.highwire.org/content/early/2024/11/14/ajnr.A8424" />
<meta name="citation_abstract_html_url" content="http://ajnr-stage2.stage.highwire.org/content/early/2024/11/14/ajnr.A8424.abstract" />
<meta name="citation_full_html_url" content="http://ajnr-stage2.stage.highwire.org/content/early/2024/11/14/ajnr.A8424.full" />
<meta name="citation_pdf_url" content="http://ajnr-stage2.stage.highwire.org/content/ajnr/early/2024/11/14/ajnr.A8424.full.pdf" />
<meta name="citation_issn" content="0195-6108" />
<meta name="citation_issn" content="1936-959X" />
<meta name="citation_doi" content="10.3174/ajnr.A8424" />
<meta name="citation_pmid" content="39038956" />
<meta name="citation_article_type" content="Research Article" />
<meta name="citation_section" content="NEUROVASCULAR/STROKE IMAGING" />
<meta name="citation_author" content="Mona Kharaji" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington" />
<meta name="citation_author" content="Gador Canton" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0002-0594-2764" />
<meta name="citation_author" content="Yin Guo" />
<meta name="citation_author_institution" content="bDepartment of Bioengineering (Y.G.), University of Washington, Seattle, Washington" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0002-6006-9849" />
<meta name="citation_author" content="Mohamad Hosaam Mosi" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington" />
<meta name="citation_author" content="Zechen Zhou" />
<meta name="citation_author_institution" content="cSubtle Medical Inc (Z.Z.), Menlo Park, California" />
<meta name="citation_author" content="Niranjan Balu" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0002-1489-6683" />
<meta name="citation_author" content="Mahmud Mossa-Basha" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-7798-8158" />
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<meta name="twitter:title" content="DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning–Based Image Quality Improvement for Vessel Wall MRI" />
<meta name="twitter:card" content="summary" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times.

MATERIALS AND METHODS: Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed.

RESULTS: DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1–1.75] versus 3 [3–4], P &lt; .001) and venous flow artifacts (1 [1–2] versus 3 [3–4], P &lt; .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1–2], versus 3 [2–3], P &lt; .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) ( P &lt; .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL.

CONCLUSIONS: DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.

CAIPI
:   controlled aliasing in parallel imaging
CNR
:   contrast-to-noise ratio
DANTE
:   delay alternating with nutation for tailored excitation
DL
:   deep learning
ICC
:   intraclass correlation coefficient
IVW
:   intracranial vessel wall MRI
SOC
:   standard of care
SPACE
:   sampling perfection with application-optimized contrasts by using different flip angle evolution" />
<meta name="og-title" property="og:title" content="DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning–Based Image Quality Improvement for Vessel Wall MRI" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/early/2024/11/14/ajnr.A8424" />
<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: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times.

MATERIALS AND METHODS: Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed.

RESULTS: DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1–1.75] versus 3 [3–4], P &lt; .001) and venous flow artifacts (1 [1–2] versus 3 [3–4], P &lt; .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1–2], versus 3 [2–3], P &lt; .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) ( P &lt; .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL.

CONCLUSIONS: DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.

CAIPI
:   controlled aliasing in parallel imaging
CNR
:   contrast-to-noise ratio
DANTE
:   delay alternating with nutation for tailored excitation
DL
:   deep learning
ICC
:   intraclass correlation coefficient
IVW
:   intracranial vessel wall MRI
SOC
:   standard of care
SPACE
:   sampling perfection with application-optimized contrasts by using different flip angle evolution" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node111871--5" data-node-nid="111871" id="top-node-111871--61956668741" data-pisa="ajnr;ajnr.A8424v2" data-pisa-master="ajnr;ajnr.A8424" data-apath="/ajnr/early/2024/11/14/ajnr.A8424.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">NEUROVASCULAR/STROKE IMAGING</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning–Based Image Quality Improvement for Vessel Wall MRI</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Mona Kharaji</span>, <span class="highwire-citation-author" data-delta="1">Gador Canton</span>, <span class="highwire-citation-author" data-delta="2">Yin Guo</span>, <span class="highwire-citation-author" data-delta="3">Mohamad Hosaam Mosi</span>, <span class="highwire-citation-author" data-delta="4">Zechen Zhou</span>, <span class="highwire-citation-author" data-delta="5">Niranjan Balu</span> and <span class="highwire-citation-author" data-delta="6">Mahmud Mossa-Basha</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">November 2024,  </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8424 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-111871--61956668741" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Mona Kharaji </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 (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, 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=Mona%2BKharaji%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=Kharaji%20M&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li 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href="/lookup/external-ref?access_num=Canton%20G&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AGador%2BCanton%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/0000-0002-0594-2764" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Gador Canton</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Yin Guo </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Bioengineering (Y.G.), 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=Yin%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%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%3AYin%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/0000-0002-6006-9849" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Yin Guo</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Mohamad Hosaam Mosi </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 (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, 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=Mohamad%2BHosaam%2BMosi%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=Mosi%20MH&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%3AMohamad%2BHosaam%2BMosi%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">Zechen Zhou </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Subtle Medical Inc (Z.Z.), Menlo Park, California</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Zechen%2BZhou%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=Zhou%20Z&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%3AZechen%2BZhou%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">Niranjan Balu </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 (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, 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=Niranjan%2BBalu%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=Balu%20N&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 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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=Mossa-Basha%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%3AMahmud%2BMossa-Basha%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/0000-0001-7798-8158" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Mahmud Mossa-Basha</a></li></ul></div></div></div>  </div>

  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2>Abstract</h2><div id="sec-1" class="subsection"><p id="p-1"><strong>BACKGROUND AND PURPOSE:</strong> Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times.</p></div><div id="sec-2" class="subsection"><p id="p-2"><strong>MATERIALS AND METHODS:</strong> Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed.</p></div><div id="sec-3" class="subsection"><p id="p-3"><strong>RESULTS:</strong> DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1–1.75] versus 3 [3–4], <em>P</em> &lt; .001) and venous flow artifacts (1 [1–2] versus 3 [3–4], <em>P</em> &lt; .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1–2], versus 3 [2–3], <em>P</em> &lt; .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) (<em>P</em> &lt; .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL.</p></div><div id="sec-4" class="subsection"><p id="p-4"><strong>CONCLUSIONS:</strong> DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.</p></div></div><div class="section glossary" id="glossary-1"><h2 class="">ABBREVIATIONS:</h2><dl id="def-list-1"><dt id="def-item-1">CAIPI<span class="def-item-dt-sep"></span></dt><dd>controlled aliasing in parallel imaging<span class="def-item-dd-sep"></span></dd><dt id="def-item-2">CNR<span class="def-item-dt-sep"></span></dt><dd>contrast-to-noise ratio<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">DANTE<span class="def-item-dt-sep"></span></dt><dd>delay alternating with nutation for tailored excitation<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">DL<span class="def-item-dt-sep"></span></dt><dd>deep learning<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">ICC<span class="def-item-dt-sep"></span></dt><dd>intraclass correlation coefficient<span class="def-item-dd-sep"></span></dd><dt id="def-item-6">IVW<span class="def-item-dt-sep"></span></dt><dd>intracranial vessel wall MRI<span class="def-item-dd-sep"></span></dd><dt id="def-item-7">SOC<span class="def-item-dt-sep"></span></dt><dd>standard of care<span class="def-item-dd-sep"></span></dd><dt id="def-item-8">SPACE<span class="def-item-dt-sep"></span></dt><dd>sampling perfection with application-optimized contrasts by using different flip angle evolution</dd></dl></div><div class="section fn-group" id="fn-group-1"><h2>Footnotes</h2><ul><li class="fn-supported-by" id="fn-1"><p id="p-52">This research has been supported by the National Institutes of Health under Grants R01-NS125635 and R01-NS092207.</p></li><li class="fn-financial-disclosure" id="fn-2"><p id="p-53"><a href="https://www.ajnr.org/sites/default/files/additional-assets/Disclosures/January%202025/0412.pdf">Disclosure forms</a> provided by the authors are available with the full text and PDF of this article at <a href="http://www.ajnr.org">www.ajnr.org</a>.</p></li></ul></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2025 by American Journal of Neuroradiology</li></ul><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/early/2024/11/14/ajnr.A8424.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></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">Nov 2024, </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8424 </span></div>
  
  
  
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