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<meta name="DC.Title" content="Deep Learning–Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8382" />
<meta name="DC.Date" content="2024-11-01" />
<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: Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning–optimized sequence using T1-weighted imaging.

MATERIALS AND METHODS: Clinical and optimized deep learning–based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1−4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples t tests.

RESULTS: The scan time for the clinical and deep learning–based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning–based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning–based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning–based image reconstruction.

CONCLUSIONS: Our preliminary findings suggest that deep learning–based image reconstruction–optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.

ACA
:   anterior cerebral artery
AI
:   artificial intelligence
CAIPIRINHA
:   Controlled Aliasing in Parallel Imaging Results in Higher Acceleration
DL
:   deep learning
DLBIR
:   deep learning–based image reconstruction
IC-VWI
:   intracranial vessel wall imaging
PCA
:   posterior cerebral artery" />
<meta name="DC.Contributor" content="Girish Bathla" />
<meta name="DC.Contributor" content="Steven A. Messina" />
<meta name="DC.Contributor" content="David F. Black" />
<meta name="DC.Contributor" content="John C. Benson" />
<meta name="DC.Contributor" content="Peter Kollasch" />
<meta name="DC.Contributor" content="Marcel D. Nickel" />
<meta name="DC.Contributor" content="Neetu Soni" />
<meta name="DC.Contributor" content="Brian C. Rucker" />
<meta name="DC.Contributor" content="Ian T. Mark" />
<meta name="DC.Contributor" content="Felix E. Diehn" />
<meta name="DC.Contributor" content="Amit K. Agarwal" />
<meta name="article:published_time" content="2024-11-01" />
<meta name="article:section" content="Neurovascular/Stroke Imaging" />
<meta name="citation_title" content="Deep Learning–Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning–optimized sequence using T1-weighted imaging.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Clinical and optimized deep learning–based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1−4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples &lt;i&gt;t&lt;/i&gt; tests.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The scan time for the clinical and deep learning–based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning–based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning–based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning–based image reconstruction.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Our preliminary findings suggest that deep learning–based image reconstruction–optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.&lt;/p&gt;" />
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<meta name="twitter:title" content="Deep Learning–Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning–optimized sequence using T1-weighted imaging.

MATERIALS AND METHODS: Clinical and optimized deep learning–based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1−4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples t tests.

RESULTS: The scan time for the clinical and deep learning–based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning–based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning–based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning–based image reconstruction.

CONCLUSIONS: Our preliminary findings suggest that deep learning–based image reconstruction–optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.

ACA
:   anterior cerebral artery
AI
:   artificial intelligence
CAIPIRINHA
:   Controlled Aliasing in Parallel Imaging Results in Higher Acceleration
DL
:   deep learning
DLBIR
:   deep learning–based image reconstruction
IC-VWI
:   intracranial vessel wall imaging
PCA
:   posterior cerebral artery" />
<meta name="og-title" property="og:title" content="Deep Learning–Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/11/1655" />
<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: Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning–optimized sequence using T1-weighted imaging.

MATERIALS AND METHODS: Clinical and optimized deep learning–based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1−4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples t tests.

RESULTS: The scan time for the clinical and deep learning–based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning–based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning–based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning–based image reconstruction.

CONCLUSIONS: Our preliminary findings suggest that deep learning–based image reconstruction–optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.

ACA
:   anterior cerebral artery
AI
:   artificial intelligence
CAIPIRINHA
:   Controlled Aliasing in Parallel Imaging Results in Higher Acceleration
DL
:   deep learning
DLBIR
:   deep learning–based image reconstruction
IC-VWI
:   intracranial vessel wall imaging
PCA
:   posterior cerebral artery" />
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      <h1 class="highwire-cite-title" id="page-title">Deep Learning–Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Girish Bathla</span>, <span class="highwire-citation-author" data-delta="1">Steven A. Messina</span>, <span class="highwire-citation-author" data-delta="2">David F. Black</span>, <span class="highwire-citation-author" data-delta="3">John C. Benson</span>, <span class="highwire-citation-author" data-delta="4">Peter Kollasch</span>, <span class="highwire-citation-author" data-delta="5">Marcel D. Nickel</span>, <span class="highwire-citation-author" data-delta="6">Neetu Soni</span>, <span class="highwire-citation-author" data-delta="7">Brian C. Rucker</span>, <span class="highwire-citation-author" data-delta="8">Ian T. Mark</span>, <span class="highwire-citation-author" data-delta="9">Felix E. Diehn</span> and <span class="highwire-citation-author" data-delta="10">Amit K. Agarwal</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-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1655-1660; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8382 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-111458--6107710034" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Girish Bathla </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 (G.B., S.A.M., D.F.B., J.C.B., B.C.R., I.T.M., F.E.D.), Mayo Clinic, Rochester, Minnesota</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=Girish%2BBathla%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=Bathla%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%3AGirish%2BBathla%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/0000-0003-3170-242X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Girish Bathla</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Steven A. 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      <div class="highwire-cite-highlight"><a href="/content/45/11"  class="highlight-image-linked"><img class="highlight-image" src="http://ajnr-stage2.stage.highwire.org/sites/default/files/styles/medium/public/highwire/ajnr/45/11.cover-source.jpg?itok=9FRhlSZb" alt="American Journal of Neuroradiology: 45 (11)" /></a></div>
  
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          <a href="/content/45/11" class="highwire-cite-linked-title" data-icon-position="" data-hide-link-title="0"><span class="highwire-cite-title">American Journal of Neuroradiology</span></a>      
      
        	<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 11 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Nov 2024 </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-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1655-1660; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8382 </span></div>
  
  
  
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    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Nov 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1655-1660; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8382 </span></div>
  
  
  
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