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

Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [(11)C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitati...

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Veröffentlicht in:PloS one Jg. 9; H. 2; S. e89101
Hauptverfasser: Lyoo, Chul Hyoung, Zanotti-Fregonara, Paolo, Zoghbi, Sami S., Liow, Jeih-San, Xu, Rong, Pike, Victor W., Zarate, Carlos A., Fujita, Masahiro, Innis, Robert B.
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Veröffentlicht: United States Public Library of Science 20.02.2014
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Abstract Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [(11)C](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 [(11)C](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 (V T/f P) 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 V T/f P 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% [(11)C](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-V T/f P in [(11)C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.
AbstractList Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [.sup.11 C](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 [.sup.11 C](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 (V.sub.T /f.sub.P) 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 V.sub.T /f.sub.P error of 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% [.sup.11 C](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-V.sub.T /f.sub.P in [.sup.11 C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.
Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [(11)C](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 [(11)C](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 (V T/f P) 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 V T/f P 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% [(11)C](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-V T/f P in [(11)C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.
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.
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 (V T/f P) 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 V T/f P 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-V T/f P 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.
Audience Academic
Author Zoghbi, Sami S.
Fujita, Masahiro
Liow, Jeih-San
Xu, Rong
Innis, Robert B.
Lyoo, Chul Hyoung
Pike, Victor W.
Zarate, Carlos A.
Zanotti-Fregonara, Paolo
AuthorAffiliation 4 Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
3 University of Bordeaux, CNRS, INCIA, UMR 5287, Talence, France
University of Manchester, United Kingdom
2 Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
1 Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
AuthorAffiliation_xml – name: 2 Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
– name: University of Manchester, United Kingdom
– name: 4 Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
– name: 3 University of Bordeaux, CNRS, INCIA, UMR 5287, Talence, France
– name: 1 Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
Author_xml – sequence: 1
  givenname: Chul Hyoung
  surname: Lyoo
  fullname: Lyoo, Chul Hyoung
– sequence: 2
  givenname: Paolo
  surname: Zanotti-Fregonara
  fullname: Zanotti-Fregonara, Paolo
– sequence: 3
  givenname: Sami S.
  surname: Zoghbi
  fullname: Zoghbi, Sami S.
– sequence: 4
  givenname: Jeih-San
  surname: Liow
  fullname: Liow, Jeih-San
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  givenname: Rong
  surname: Xu
  fullname: Xu, Rong
– sequence: 6
  givenname: Victor W.
  surname: Pike
  fullname: Pike, Victor W.
– sequence: 7
  givenname: Carlos A.
  surname: Zarate
  fullname: Zarate, Carlos A.
– sequence: 8
  givenname: Masahiro
  surname: Fujita
  fullname: Fujita, Masahiro
– sequence: 9
  givenname: Robert B.
  surname: Innis
  fullname: Innis, Robert B.
BackLink https://www.ncbi.nlm.nih.gov/pubmed/24586526$$D View this record in MEDLINE/PubMed
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: CHL PZF RBI. Performed the experiments: CHL PZF SSZ JSL RX MF. Analyzed the data: CHL PZF MF. Contributed reagents/materials/analysis tools: CHL PZF SSZ JSL RX VWP MF RBI. Wrote the paper: CHL PZF SSZ JSL RX VWP CAZ MF RBI.
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Snippet Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [(11)C](R)-rolipram positron emission...
Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [.sup.11 C](R)-rolipram positron...
Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [11C](R)-rolipram positron emission...
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StartPage e89101
SubjectTerms Adult
Algorithms
Arteries
Automation
Biology
Brain
Brain research
Carbon Radioisotopes
Carotid arteries
Carotid Arteries - diagnostic imaging
Carotid Arteries - physiopathology
Carotid artery
Case-Control Studies
Cluster Analysis
Clustering
Clusters
Computer Science
Depressive Disorder, Major - blood
Depressive Disorder, Major - diagnostic imaging
Depressive Disorder, Major - metabolism
Depressive Disorder, Major - physiopathology
Female
Humans
Image processing
Image Processing, Computer-Assisted - methods
Kinetics
Magnetic resonance
Magnetic resonance imaging
Male
Mathematics
Medical imaging
Medicine
Mental depression
Mental health
Metabolites
Methods
Neuroimaging
Plasma
Positron emission
Positron emission tomography
Radioactive tracers
Rolipram
Rolipram - metabolism
Studies
Tomography
Variability
Veins & arteries
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Title Image-Derived Input Function Derived from a Supervised Clustering Algorithm: Methodology and Validation in a Clinical Protocol Using [11C](R)-Rolipram
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