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

Authors
 Chul Hyoung Lyoo  ;  Paolo Zanotti-Fregonara  ;  Sami S. Zoghbi  ;  Jeih-San Liow  ;  Rong Xu  ;  Victor W. Pike  ;  Carlos A. Zarate Jr  ;  Masahiro Fujita  ;  Robert B. Innis 
Citation
 PLOS ONE, Vol.9(2) : e89101, 2014 
Journal Title
 PLOS ONE 
Issue Date
2014
MeSH
Adult ; Algorithms* ; Carbon Radioisotopes ; Carotid Arteries/diagnostic imaging ; Carotid Arteries/physiopathology ; Case-Control Studies ; Cluster Analysis ; Depressive Disorder, Major/blood ; Depressive Disorder, Major/diagnostic imaging ; Depressive Disorder, Major/metabolism ; Depressive Disorder, Major/physiopathology ; Female ; Humans ; Image Processing, Computer-Assisted/methods* ; Male ; Positron-Emission Tomography* ; Rolipram*/metabolism
Abstract
Image-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.
Files in This Item:
T201400350.pdf Download
DOI
10.1371/journal.pone.0089101
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
Yonsei Authors
Lyoo, Chul Hyoung(류철형) ORCID logo https://orcid.org/0000-0003-2231-672X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/98151
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