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<meta name="DC.Title" content="Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7589" />
<meta name="DC.Date" content="2022-08-01" />
<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: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning–based reconstruction could provide a relevant strategy while shortening the imaging examination.

MATERIALS AND METHODS: Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning–based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed.

RESULTS: Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning–based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning–based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning–based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence.

CONCLUSIONS: Denoising using deep learning–based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.

AVD
:   absolute volume difference
dDLR
:   denoising using deep learning–based reconstruction
DSC
:   Dice similarity coefficient
HD
:   Hausdorff distance
MS
:   multiple sclerosis
PPV
:   positive predictive value
TPR
:   true-positive rate" />
<meta name="DC.Contributor" content="T. Yamamoto" />
<meta name="DC.Contributor" content="C. Lacheret" />
<meta name="DC.Contributor" content="H. Fukutomi" />
<meta name="DC.Contributor" content="R.A. Kamraoui" />
<meta name="DC.Contributor" content="L. Denat" />
<meta name="DC.Contributor" content="B. Zhang" />
<meta name="DC.Contributor" content="V. Prevost" />
<meta name="DC.Contributor" content="L. Zhang" />
<meta name="DC.Contributor" content="A. Ruet" />
<meta name="DC.Contributor" content="B. Triaire" />
<meta name="DC.Contributor" content="V. Dousset" />
<meta name="DC.Contributor" content="P. Coupé" />
<meta name="DC.Contributor" content="T. Tourdias" />
<meta name="article:published_time" content="2022-08-01" />
<meta name="article:section" content="Adult Brain" />
<meta name="citation_title" content="Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning–based reconstruction could provide a relevant strategy while shortening the imaging examination.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning–based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning–based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning–based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning–based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Denoising using deep learning–based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.&lt;/p&gt;" />
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<meta name="twitter:title" content="Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning–based reconstruction could provide a relevant strategy while shortening the imaging examination.

MATERIALS AND METHODS: Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning–based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed.

RESULTS: Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning–based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning–based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning–based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence.

CONCLUSIONS: Denoising using deep learning–based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.

AVD
:   absolute volume difference
dDLR
:   denoising using deep learning–based reconstruction
DSC
:   Dice similarity coefficient
HD
:   Hausdorff distance
MS
:   multiple sclerosis
PPV
:   positive predictive value
TPR
:   true-positive rate" />
<meta name="og-title" property="og:title" content="Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/43/8/1099" />
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<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning–based reconstruction could provide a relevant strategy while shortening the imaging examination.

MATERIALS AND METHODS: Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning–based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed.

RESULTS: Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning–based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning–based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning–based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence.

CONCLUSIONS: Denoising using deep learning–based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.

AVD
:   absolute volume difference
dDLR
:   denoising using deep learning–based reconstruction
DSC
:   Dice similarity coefficient
HD
:   Hausdorff distance
MS
:   multiple sclerosis
PPV
:   positive predictive value
TPR
:   true-positive rate" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node98303--7" data-node-nid="98303" id="top-node-98303--8395880962" data-pisa="ajnr;43/8/1099" data-pisa-master="ajnr;ajnr.A7589" data-apath="/ajnr/43/8/1099.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">Adult Brain</span></span></div>
  
      <div class="highwire-cite-access"><span class="highwire-citation-access"><i class="highwire-access-icon highwire-access-icon-open-access open-access hw-icon-open-access" title="Open Access" aria-hidden="true"></i><span  class="element-invisible highwire-access-icon highwire-access-icon-open-access" style="">Open Access</span></span></div>
  
      <h1 class="highwire-cite-title" id="page-title">Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">T. Yamamoto</span>, <span class="highwire-citation-author" data-delta="1">C. Lacheret</span>, <span class="highwire-citation-author" data-delta="2">H. Fukutomi</span>, <span class="highwire-citation-author" data-delta="3">R.A. Kamraoui</span>, <span class="highwire-citation-author" data-delta="4">L. Denat</span>, <span class="highwire-citation-author" data-delta="5">B. Zhang</span>, <span class="highwire-citation-author" data-delta="6">V. Prevost</span>, <span class="highwire-citation-author" data-delta="7">L. Zhang</span>, <span class="highwire-citation-author" data-delta="8">A. Ruet</span>, <span class="highwire-citation-author" data-delta="9">B. Triaire</span>, <span class="highwire-citation-author" data-delta="10">V. Dousset</span>, <span class="highwire-citation-author" data-delta="11">P. Coupé</span> and <span class="highwire-citation-author" data-delta="12">T. Tourdias</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">August 2022,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(8) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1099-1106; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7589 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-98303--8395880962" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">T. Yamamoto </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France</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.%2BYamamoto%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=Yamamoto%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.%2BYamamoto%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/0000-0003-0306-3691" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for T. Yamamoto</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">C. Lacheret </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Neuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)</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=C.%2BLacheret%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=Lacheret%20C&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%3AC.%2BLacheret%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/0000-0001-9513-5060" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for C. Lacheret</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">H. Fukutomi </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France</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.%2BFukutomi%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=Fukutomi%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.%2BFukutomi%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/0000-0003-4262-3890" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H. Fukutomi</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">R.A. Kamraoui </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Laboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre National de la Recherche Scientifique, Bordeaux Institut National Polytechnique, Talence, France</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.A.%2BKamraoui%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=Kamraoui%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.A.%2BKamraoui%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/0000-0002-6603-3302" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R.A. Kamraoui</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">L. Denat </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France</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=L.%2BDenat%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=Denat%20L&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%3AL.%2BDenat%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/0000-0001-9627-920X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L. Denat</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">B. Zhang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Canon Medical Systems Europe (B.Z.), Zoetermeer, the Netherlands</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=B.%2BZhang%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=Zhang%20B&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%3AB.%2BZhang%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/0000-0001-8113-3988" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B. Zhang</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">V. Prevost </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>Canon Medical Systems (V.P., B.T.), Tochigi, Japan</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=V.%2BPrevost%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=Prevost%20V&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%3AV.%2BPrevost%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/0000-0002-6162-6942" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for V. Prevost</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">L. Zhang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>g</span>Canon Medical Systems China (L.Z.), Beijing, China</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=L.%2BZhang%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=Zhang%20L&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%3AL.%2BZhang%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/0000-0003-0665-2440" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L. Zhang</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">A. Ruet </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Service de Neurologie (A.R.), Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France</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=A.%2BRuet%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=Ruet%20A&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%3AA.%2BRuet%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/0000-0002-0020-8739" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Ruet</a></li></ul></div><div class="author-tooltip-9"><div class="author-tooltip-name">B. Triaire </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>Canon Medical Systems (V.P., B.T.), Tochigi, Japan</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=B.%2BTriaire%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=Triaire%20B&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%3AB.%2BTriaire%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/0000-0002-0462-705X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B. Triaire</a></li></ul></div><div class="author-tooltip-10"><div class="author-tooltip-name">V. Dousset </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Neuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)</div><div class='author-affiliation'><span class='nlm-sup'>h</span>NeurocentreMagendie (V.D., T.T.), University of Bordeaux, L’Institut National de la Santé et de la Recherche Médicale, Bordeaux, France</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=V.%2BDousset%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=Dousset%20V&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%3AV.%2BDousset%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/0000-0003-0427-3236" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for V. Dousset</a></li></ul></div><div class="author-tooltip-11"><div class="author-tooltip-name">P. Coupé </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Laboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre National de la Recherche Scientifique, Bordeaux Institut National Polytechnique, Talence, France</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.%2BCoup%C3%A9%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=Coup%C3%A9%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.%2BCoup%25C3%25A9%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/0000-0003-2709-3350" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for P. Coupé</a></li></ul></div><div class="author-tooltip-12"><div class="author-tooltip-name">T. Tourdias </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Neuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)</div><div class='author-affiliation'><span class='nlm-sup'>h</span>NeurocentreMagendie (V.D., T.T.), University of Bordeaux, L’Institut National de la Santé et de la Recherche Médicale, Bordeaux, France</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.%2BTourdias%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=Tourdias%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.%2BTourdias%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/0000-0002-7151-6325" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for T. Tourdias</a></li></ul></div></div></div>  </div>

  
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    <div class="field field-name-field-highwire-publication-history field-type-text field-label-inline clearfix"><div class="field-label">History&nbsp;</div><div class="field-items"><div class="field-item even"><ul class="publication-history"><li>Received  December 20, 2021</li><li>Accepted after revision  June 13, 2022</li><li class="published"><span class="published-label">Published online</span> August 8, 2022.</li></ul></div></div></div>  </div>

  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div xmlns:xhtml="http://www.w3.org/1999/xhtml" class="contributors"><ol class="contributor-list" id="contrib-group-1"><li class="contributor" id="contrib-1"><span class="name">T. Yamamoto</span><a id="xref-aff-1-1" class="xref-aff" href="#aff-1">a*</a>, </li><li class="contributor" id="contrib-2"><span class="name">C. Lacheret</span><a id="xref-aff-2-1" class="xref-aff" href="#aff-2">b*</a>, </li><li class="contributor" id="contrib-3"><span class="name">H. Fukutomi</span><a id="xref-aff-1-2" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-4"><span class="name">R.A. Kamraoui</span><a id="xref-aff-4-1" class="xref-aff" href="#aff-4">d</a>, </li><li class="contributor" id="contrib-5"><span class="name">L. Denat</span><a id="xref-aff-1-3" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-6"><span class="name">B. Zhang</span><a id="xref-aff-5-1" class="xref-aff" href="#aff-5">e</a>, </li><li class="contributor" id="contrib-7"><span class="name">V. Prevost</span><a id="xref-aff-6-1" class="xref-aff" href="#aff-6">f</a>, </li><li class="contributor" id="contrib-8"><span class="name">L. Zhang</span><a id="xref-aff-7-1" class="xref-aff" href="#aff-7">g</a>, </li><li class="contributor" id="contrib-9"><span class="name">A. Ruet</span><a id="xref-aff-3-1" class="xref-aff" href="#aff-3">c</a>, </li><li class="contributor" id="contrib-10"><span class="name">B. Triaire</span><a id="xref-aff-6-2" class="xref-aff" href="#aff-6">f</a>, </li><li class="contributor" id="contrib-11"><span class="name">V. Dousset</span><a id="xref-aff-1-4" class="xref-aff" href="#aff-1">a</a><span class="xref-sep">,</span><a id="xref-aff-2-2" class="xref-aff" href="#aff-2">b</a><span class="xref-sep">,</span><a id="xref-aff-8-1" class="xref-aff" href="#aff-8">h</a>, </li><li class="contributor" id="contrib-12"><span class="name">P. Coupé</span><a id="xref-aff-4-2" class="xref-aff" href="#aff-4">d</a> and </li><li class="last" id="contrib-13"><span class="name">T. Tourdias</span><a id="xref-aff-1-5" class="xref-aff" href="#aff-1">a</a><span class="xref-sep">,</span><a id="xref-aff-2-3" class="xref-aff" href="#aff-2">b</a><span class="xref-sep">,</span><a id="xref-aff-8-2" class="xref-aff" href="#aff-8">h</a></li></ol><ol class="affiliation-list"><li class="aff"><a id="aff-1" name="aff-1"></a><address><sup>a</sup>From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France</address></li><li class="aff"><a id="aff-2" name="aff-2"></a><address><sup>b</sup>Neuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)</address></li><li class="aff"><a id="aff-3" name="aff-3"></a><address><sup>c</sup>Service de Neurologie (A.R.), Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France</address></li><li class="aff"><a id="aff-4" name="aff-4"></a><address><sup>d</sup>Laboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre National de la Recherche Scientifique, Bordeaux Institut National Polytechnique, Talence, France</address></li><li class="aff"><a id="aff-5" name="aff-5"></a><address><sup>e</sup>Canon Medical Systems Europe (B.Z.), Zoetermeer, the Netherlands</address></li><li class="aff"><a id="aff-6" name="aff-6"></a><address><sup>f</sup>Canon Medical Systems (V.P., B.T.), Tochigi, Japan</address></li><li class="aff"><a id="aff-7" name="aff-7"></a><address><sup>g</sup>Canon Medical Systems China (L.Z.), Beijing, China</address></li><li class="aff"><a id="aff-8" name="aff-8"></a><address><sup>h</sup>NeurocentreMagendie (V.D., T.T.), University of Bordeaux, L’Institut National de la Santé et de la Recherche Médicale, Bordeaux, France</address></li></ol><ol class="corresp-list"><li class="corresp" id="corresp-1">Please address correspondence to Thomas Tourdias MD, PhD, <span class="addr-line">Centre Hospitalier Universitaire de Bordeaux, Neuroimagerie Diagnostique et Thérapeutique, Place Amélie Raba Léon, 33300 Bordeaux, France</span>; e-mail: <span class="em-link"><span class="em-addr">thomas.tourdias{at}chu-bordeaux.fr</span></span></li></ol></div></div><a href="http://ajnr-stage2.stage.highwire.org/content/43/8/1099.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-title">Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging</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">T.</span> <span class="nlm-surname">Yamamoto</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Lacheret</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">H.</span> <span class="nlm-surname">Fukutomi</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">R.A.</span> <span class="nlm-surname">Kamraoui</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Denat</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Zhang</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">V.</span> <span class="nlm-surname">Prevost</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Zhang</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Ruet</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Triaire</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">V.</span> <span class="nlm-surname">Dousset</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">P.</span> <span class="nlm-surname">Coupé</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">T.</span> <span class="nlm-surname">Tourdias</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Aug 2022, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(8) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1099-1106; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7589 </span></div>
  
  
  
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