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<meta name="DC.Title" content="Automated Optimization of Subcortical Cerebral MR Imaging−Atlas Coregistration for Improved Postoperative Electrode Localization in Deep Brain Stimulation" />
<meta name="DC.Identifier" content="10.3174/ajnr.A1741" />
<meta name="DC.Date" content="2009-11-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: The efficacy of deep brain stimulation in treating movement disorders depends critically on electrode localization, which is conventionally described by using coordinates relative to the midcommissural point. This approach requires manual measurement and lacks spatial normalization of anatomic variances. Normalization is based on intersubject spatial alignment (coregistration) of corresponding brain structures by using different geometric transformations. Here, we have devised and evaluated a scheme for automated subcortical optimization of coregistration (ASOC), which maximizes patient-to-atlas normalization accuracy of postoperative structural MR imaging into the standard Montreal Neurologic Institute (MNI) space for the basal ganglia.

MATERIALS AND METHODS: Postoperative T2-weighted MR imaging data from 39 patients with Parkinson disease and 32 patients with dystonia were globally normalized, representing the standard registration (control). The global transformations were regionally refined by 2 successive linear registration stages (RSs) (ASOC-1 and 2), focusing progressively on the basal ganglia with 2 anatomically selective brain masks, which specify the reference volume (weighted cost function). Accuracy of the RSs was quantified by spatial dispersion of 16 anatomic landmarks and their root-mean-square errors (RMSEs) with respect to predefined MNI-based reference points. The effects of CSF volume, age, and sex on RMSEs were calculated.

RESULTS: Mean RMSEs differed significantly ( P &lt; .001) between the global control (4.2 ± 2.0 mm), ASOC-1 (1.92 ± 1.02 mm), and ASOC-2 (1.29 ± 0.78 mm).

CONCLUSIONS: The present method improves the registration accuracy of postoperative structural MR imaging data into MNI space within the basal ganglia, allowing automated normalization with increased precision at stereotactic targets, and enables lead-contact localization in MNI coordinates for quantitative group analysis." />
<meta name="DC.Contributor" content="T. Schönecker" />
<meta name="DC.Contributor" content="A. Kupsch" />
<meta name="DC.Contributor" content="A.A. Kühn" />
<meta name="DC.Contributor" content="G.-H. Schneider" />
<meta name="DC.Contributor" content="K.-T. Hoffmann" />
<meta name="article:published_time" content="2009-11-01" />
<meta name="article:section" content="Brain" />
<meta name="citation_title" content="Automated Optimization of Subcortical Cerebral MR Imaging−Atlas Coregistration for Improved Postoperative Electrode Localization in Deep Brain Stimulation" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;The efficacy of deep brain stimulation in treating movement disorders depends critically on electrode localization, which is conventionally described by using coordinates relative to the midcommissural point. This approach requires manual measurement and lacks spatial normalization of anatomic variances. Normalization is based on intersubject spatial alignment (coregistration) of corresponding brain structures by using different geometric transformations. Here, we have devised and evaluated a scheme for automated subcortical optimization of coregistration (ASOC), which maximizes patient-to-atlas normalization accuracy of postoperative structural MR imaging into the standard Montreal Neurologic Institute (MNI) space for the basal ganglia.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Postoperative T2-weighted MR imaging data from 39 patients with Parkinson disease and 32 patients with dystonia were globally normalized, representing the standard registration (control). The global transformations were regionally refined by 2 successive linear registration stages (RSs) (ASOC-1 and 2), focusing progressively on the basal ganglia with 2 anatomically selective brain masks, which specify the reference volume (weighted cost function). Accuracy of the RSs was quantified by spatial dispersion of 16 anatomic landmarks and their root-mean-square errors (RMSEs) with respect to predefined MNI-based reference points. The effects of CSF volume, age, and sex on RMSEs were calculated.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Mean RMSEs differed significantly (&lt;i&gt;P&lt;/i&gt; &amp;lt; .001) between the global control (4.2 ± 2.0 mm), ASOC-1 (1.92 ± 1.02 mm), and ASOC-2 (1.29 ± 0.78 mm).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The present method improves the registration accuracy of postoperative structural MR imaging data into MNI space within the basal ganglia, allowing automated normalization with increased precision at stereotactic targets, and enables lead-contact localization in MNI coordinates for quantitative group analysis.&lt;/p&gt;" />
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<meta name="twitter:title" content="Automated Optimization of Subcortical Cerebral MR Imaging−Atlas Coregistration for Improved Postoperative Electrode Localization in Deep Brain Stimulation" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: The efficacy of deep brain stimulation in treating movement disorders depends critically on electrode localization, which is conventionally described by using coordinates relative to the midcommissural point. This approach requires manual measurement and lacks spatial normalization of anatomic variances. Normalization is based on intersubject spatial alignment (coregistration) of corresponding brain structures by using different geometric transformations. Here, we have devised and evaluated a scheme for automated subcortical optimization of coregistration (ASOC), which maximizes patient-to-atlas normalization accuracy of postoperative structural MR imaging into the standard Montreal Neurologic Institute (MNI) space for the basal ganglia.

MATERIALS AND METHODS: Postoperative T2-weighted MR imaging data from 39 patients with Parkinson disease and 32 patients with dystonia were globally normalized, representing the standard registration (control). The global transformations were regionally refined by 2 successive linear registration stages (RSs) (ASOC-1 and 2), focusing progressively on the basal ganglia with 2 anatomically selective brain masks, which specify the reference volume (weighted cost function). Accuracy of the RSs was quantified by spatial dispersion of 16 anatomic landmarks and their root-mean-square errors (RMSEs) with respect to predefined MNI-based reference points. The effects of CSF volume, age, and sex on RMSEs were calculated.

RESULTS: Mean RMSEs differed significantly ( P &lt; .001) between the global control (4.2 ± 2.0 mm), ASOC-1 (1.92 ± 1.02 mm), and ASOC-2 (1.29 ± 0.78 mm).

CONCLUSIONS: The present method improves the registration accuracy of postoperative structural MR imaging data into MNI space within the basal ganglia, allowing automated normalization with increased precision at stereotactic targets, and enables lead-contact localization in MNI coordinates for quantitative group analysis." />
<meta name="og-title" property="og:title" content="Automated Optimization of Subcortical Cerebral MR Imaging−Atlas Coregistration for Improved Postoperative Electrode Localization in Deep Brain Stimulation" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/30/10/1914" />
<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: The efficacy of deep brain stimulation in treating movement disorders depends critically on electrode localization, which is conventionally described by using coordinates relative to the midcommissural point. This approach requires manual measurement and lacks spatial normalization of anatomic variances. Normalization is based on intersubject spatial alignment (coregistration) of corresponding brain structures by using different geometric transformations. Here, we have devised and evaluated a scheme for automated subcortical optimization of coregistration (ASOC), which maximizes patient-to-atlas normalization accuracy of postoperative structural MR imaging into the standard Montreal Neurologic Institute (MNI) space for the basal ganglia.

MATERIALS AND METHODS: Postoperative T2-weighted MR imaging data from 39 patients with Parkinson disease and 32 patients with dystonia were globally normalized, representing the standard registration (control). The global transformations were regionally refined by 2 successive linear registration stages (RSs) (ASOC-1 and 2), focusing progressively on the basal ganglia with 2 anatomically selective brain masks, which specify the reference volume (weighted cost function). Accuracy of the RSs was quantified by spatial dispersion of 16 anatomic landmarks and their root-mean-square errors (RMSEs) with respect to predefined MNI-based reference points. The effects of CSF volume, age, and sex on RMSEs were calculated.

RESULTS: Mean RMSEs differed significantly ( P &lt; .001) between the global control (4.2 ± 2.0 mm), ASOC-1 (1.92 ± 1.02 mm), and ASOC-2 (1.29 ± 0.78 mm).

CONCLUSIONS: The present method improves the registration accuracy of postoperative structural MR imaging data into MNI space within the basal ganglia, allowing automated normalization with increased precision at stereotactic targets, and enables lead-contact localization in MNI coordinates for quantitative group analysis." />
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      <h1 class="highwire-cite-title" id="page-title">Automated Optimization of Subcortical Cerebral MR Imaging−Atlas Coregistration for Improved Postoperative Electrode Localization in Deep Brain Stimulation</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">T. Schönecker</span>, <span class="highwire-citation-author" data-delta="1">A. Kupsch</span>, <span class="highwire-citation-author" data-delta="2">A.A. Kühn</span>, <span class="highwire-citation-author" data-delta="3">G.-H. Schneider</span> and <span class="highwire-citation-author" data-delta="4">K.-T. Hoffmann</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 2009,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">30 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1914-1921; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A1741 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-8383--61800094509" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">T. Schönecker </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.%2BSch%C3%B6necker%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=Sch%C3%B6necker%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 last"><a href="/search/author1%3AT.%2BSch%25C3%25B6necker%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-1"><div class="author-tooltip-name">A. Kupsch </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.%2BKupsch%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=Kupsch%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 last"><a href="/search/author1%3AA.%2BKupsch%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-2"><div class="author-tooltip-name">A.A. Kühn </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.A.%2BK%C3%BChn%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=K%C3%BChn%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 last"><a href="/search/author1%3AA.A.%2BK%25C3%25BChn%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-3"><div class="author-tooltip-name">G.-H. Schneider </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=G.-H.%2BSchneider%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=Schneider%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 last"><a href="/search/author1%3AG.-H.%2BSchneider%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">K.-T. Hoffmann </div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=K.-T.%2BHoffmann%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=Hoffmann%20K&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AK.-T.%2BHoffmann%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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<p id="p-1"><strong>BACKGROUND AND PURPOSE:</strong> The efficacy of deep brain stimulation in treating movement disorders depends critically on electrode localization, which is conventionally described by using coordinates relative to the midcommissural point. This approach requires manual measurement and lacks spatial normalization of anatomic variances. Normalization is based on intersubject spatial alignment (coregistration) of corresponding brain structures by using different geometric transformations. Here, we have devised and evaluated a scheme for automated subcortical optimization of coregistration (ASOC), which maximizes patient-to-atlas normalization accuracy of postoperative structural MR imaging into the standard Montreal Neurologic Institute (MNI) space for the basal ganglia.</p>
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<p id="p-2"><strong>MATERIALS AND METHODS:</strong> Postoperative T2-weighted MR imaging data from 39 patients with Parkinson disease and 32 patients with dystonia were globally normalized, representing the standard registration (control). The global transformations were regionally refined by 2 successive linear registration stages (RSs) (ASOC-1 and 2), focusing progressively on the basal ganglia with 2 anatomically selective brain masks, which specify the reference volume (weighted cost function). Accuracy of the RSs was quantified by spatial dispersion of 16 anatomic landmarks and their root-mean-square errors (RMSEs) with respect to predefined MNI-based reference points. The effects of CSF volume, age, and sex on RMSEs were calculated.</p>
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<p id="p-3"><strong>RESULTS:</strong> Mean RMSEs differed significantly (<em>P</em> &lt; .001) between the global control (4.2 ± 2.0 mm), ASOC-1 (1.92 ± 1.02 mm), and ASOC-2 (1.29 ± 0.78 mm).</p>
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<p id="p-4"><strong>CONCLUSIONS:</strong> The present method improves the registration accuracy of postoperative structural MR imaging data into MNI space within the basal ganglia, allowing automated normalization with increased precision at stereotactic targets, and enables lead-contact localization in MNI coordinates for quantitative group analysis.</p>
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 30</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Nov 2009 </div></div>
      
      
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      <div class="highwire-cite-title">Automated Optimization of Subcortical Cerebral MR Imaging−Atlas Coregistration for Improved Postoperative Electrode Localization in Deep Brain Stimulation</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 2009, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">30 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1914-1921; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A1741 </span></div>
  
  
  
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