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Cited 11 times in

Image-Derived Input Function Derived from a Supervised Clustering Algorithm: Methodology and Validation in a Clinical Protocol Using [11C](R)-Rolipram

DC FieldValueLanguage
dc.contributor.author류철형-
dc.date.accessioned2015-01-06T16:27:48Z-
dc.date.available2015-01-06T16:27:48Z-
dc.date.issued2014-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/98151-
dc.description.abstractImage-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [11C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [11C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (VT/fP) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a VT/fP error of <5%, and only one had error >10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [11C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-VT/fP in [11C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.-
dc.description.statementOfResponsibilityopen-
dc.format.extente89101-
dc.relation.isPartOfPLOS ONE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHAdult-
dc.subject.MESHAlgorithms*-
dc.subject.MESHCarbon Radioisotopes-
dc.subject.MESHCarotid Arteries/diagnostic imaging-
dc.subject.MESHCarotid Arteries/physiopathology-
dc.subject.MESHCase-Control Studies-
dc.subject.MESHCluster Analysis-
dc.subject.MESHDepressive Disorder, Major/blood-
dc.subject.MESHDepressive Disorder, Major/diagnostic imaging-
dc.subject.MESHDepressive Disorder, Major/metabolism-
dc.subject.MESHDepressive Disorder, Major/physiopathology-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHImage Processing, Computer-Assisted/methods*-
dc.subject.MESHMale-
dc.subject.MESHPositron-Emission Tomography*-
dc.subject.MESHRolipram*/metabolism-
dc.titleImage-Derived Input Function Derived from a Supervised Clustering Algorithm: Methodology and Validation in a Clinical Protocol Using [11C](R)-Rolipram-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Neurology (신경과학)-
dc.contributor.googleauthorChul Hyoung Lyoo-
dc.contributor.googleauthorPaolo Zanotti-Fregonara-
dc.contributor.googleauthorSami S. Zoghbi-
dc.contributor.googleauthorJeih-San Liow-
dc.contributor.googleauthorRong Xu-
dc.contributor.googleauthorVictor W. Pike-
dc.contributor.googleauthorCarlos A. Zarate Jr-
dc.contributor.googleauthorMasahiro Fujita-
dc.contributor.googleauthorRobert B. Innis-
dc.identifier.doi10.1371/journal.pone.0089101-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA01333-
dc.relation.journalcodeJ02540-
dc.identifier.eissn1932-6203-
dc.identifier.pmid24586526-
dc.contributor.alternativeNameLyoo, Chul Hyoung-
dc.contributor.affiliatedAuthorLyoo, Chul Hyoung-
dc.citation.volume9-
dc.citation.number2-
dc.citation.startPagee89101-
dc.identifier.bibliographicCitationPLOS ONE, Vol.9(2) : e89101, 2014-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers

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