High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing
Identifieur interne : 001A43 ( Ncbi/Merge ); précédent : 001A42; suivant : 001A44High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing
Auteurs : Siawoosh Mohammadi [Royaume-Uni, Allemagne] ; Karsten Tabelow [Allemagne] ; Lars Ruthotto [Canada] ; Thorsten Feiweier [Allemagne] ; Jörg Polzehl [Allemagne] ; Nikolaus Weiskopf [Royaume-Uni]Source :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2015.
Abstract
Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2–3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.
Url:
DOI: 10.3389/fnins.2014.00427
PubMed: 25620906
PubMed Central: 4285740
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<front><div type="abstract" xml:lang="en"><p>Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2–3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Neurosci</journal-id>
<journal-id journal-id-type="iso-abbrev">Front Neurosci</journal-id>
<journal-id journal-id-type="publisher-id">Front. Neurosci.</journal-id>
<journal-title-group><journal-title>Frontiers in Neuroscience</journal-title>
</journal-title-group>
<issn pub-type="ppub">1662-4548</issn>
<issn pub-type="epub">1662-453X</issn>
<publisher><publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25620906</article-id>
<article-id pub-id-type="pmc">4285740</article-id>
<article-id pub-id-type="doi">10.3389/fnins.2014.00427</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Neuroscience</subject>
<subj-group><subject>Original Research Article</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Mohammadi</surname>
<given-names>Siawoosh</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001"><sup>*</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/94676"></uri>
</contrib>
<contrib contrib-type="author"><name><surname>Tabelow</surname>
<given-names>Karsten</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/143525"></uri>
</contrib>
<contrib contrib-type="author"><name><surname>Ruthotto</surname>
<given-names>Lars</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/184348"></uri>
</contrib>
<contrib contrib-type="author"><name><surname>Feiweier</surname>
<given-names>Thorsten</given-names>
</name>
<xref ref-type="aff" rid="aff5"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Polzehl</surname>
<given-names>Jörg</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/184477"></uri>
</contrib>
<contrib contrib-type="author"><name><surname>Weiskopf</surname>
<given-names>Nikolaus</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/52916"></uri>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup>
<institution>Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London</institution>
<country>London, UK</country>
</aff>
<aff id="aff2"><sup>2</sup>
<institution>Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf</institution>
<country>Hamburg, Germany</country>
</aff>
<aff id="aff3"><sup>3</sup>
<institution>Stochastic Algorithms and Nonparametric Statistics, Weierstrass Institute for Applied Analysis and Stochastics</institution>
<country>Berlin, Germany</country>
</aff>
<aff id="aff4"><sup>4</sup>
<institution>Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia</institution>
<country>Vancouver, BC, Canada</country>
</aff>
<aff id="aff5"><sup>5</sup>
<institution>Healthcare Sector, Siemens AG</institution>
<country>Erlangen, Germany</country>
</aff>
<author-notes><fn fn-type="edited-by"><p>Edited by: Pedro Antonio Valdes-Sosa, Centro de Neurociencias de Cuba, Cuba</p>
</fn>
<fn fn-type="edited-by"><p>Reviewed by: Saad Jbabdi, University of Oxford, UK; Bibek Dhital, University Medical Center Freiburg, Germany</p>
</fn>
<corresp id="fn001">*Correspondence: Siawoosh Mohammadi, Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany e-mail: <email xlink:type="simple">siawooshm@googlemail.com</email>
</corresp>
<fn fn-type="other" id="fn002"><p>This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience.</p>
</fn>
</author-notes>
<pub-date pub-type="epub"><day>07</day>
<month>1</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection"><year>2014</year>
</pub-date>
<volume>8</volume>
<elocation-id>427</elocation-id>
<history><date date-type="received"><day>15</day>
<month>4</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>05</day>
<month>12</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-statement>Copyright © 2015 Mohammadi, Tabelow, Ruthotto, Feiweier, Polzehl and Weiskopf.</copyright-statement>
<copyright-year>2015</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract><p>Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2–3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.</p>
</abstract>
<kwd-group><kwd>DTI</kwd>
<kwd>DKI</kwd>
<kwd>diffusion kurtosis</kwd>
<kwd>gray matter</kwd>
<kwd>high-resolution</kwd>
<kwd>multi-shell dMRI</kwd>
<kwd>eddy current and motion artifacts</kwd>
<kwd>adaptive smoothing</kwd>
</kwd-group>
<counts><fig-count count="12"></fig-count>
<table-count count="0"></table-count>
<equation-count count="0"></equation-count>
<ref-count count="76"></ref-count>
<page-count count="14"></page-count>
<word-count count="10180"></word-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Allemagne</li>
<li>Canada</li>
<li>Royaume-Uni</li>
</country>
</list>
<tree><country name="Royaume-Uni"><noRegion><name sortKey="Mohammadi, Siawoosh" sort="Mohammadi, Siawoosh" uniqKey="Mohammadi S" first="Siawoosh" last="Mohammadi">Siawoosh Mohammadi</name>
</noRegion>
<name sortKey="Weiskopf, Nikolaus" sort="Weiskopf, Nikolaus" uniqKey="Weiskopf N" first="Nikolaus" last="Weiskopf">Nikolaus Weiskopf</name>
</country>
<country name="Allemagne"><noRegion><name sortKey="Mohammadi, Siawoosh" sort="Mohammadi, Siawoosh" uniqKey="Mohammadi S" first="Siawoosh" last="Mohammadi">Siawoosh Mohammadi</name>
</noRegion>
<name sortKey="Feiweier, Thorsten" sort="Feiweier, Thorsten" uniqKey="Feiweier T" first="Thorsten" last="Feiweier">Thorsten Feiweier</name>
<name sortKey="Polzehl, Jorg" sort="Polzehl, Jorg" uniqKey="Polzehl J" first="Jörg" last="Polzehl">Jörg Polzehl</name>
<name sortKey="Tabelow, Karsten" sort="Tabelow, Karsten" uniqKey="Tabelow K" first="Karsten" last="Tabelow">Karsten Tabelow</name>
</country>
<country name="Canada"><noRegion><name sortKey="Ruthotto, Lars" sort="Ruthotto, Lars" uniqKey="Ruthotto L" first="Lars" last="Ruthotto">Lars Ruthotto</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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