Optimization of Supervised Cluster Analysis for Extracting Reference Tissue Input Curves in (R)-[11C]PK11195 Brain PET Studies

Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[11C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cer...

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Published inJournal of cerebral blood flow and metabolism Vol. 32; no. 8; pp. 1600 - 1608
Main Authors Yaqub, Maqsood, van Berckel, Bart NM, Schuitemaker, Alie, Hinz, Rainer, Turkheimer, Federico E, Tomasi, Giampaolo, Lammertsma, Adriaan A, Boellaard, Ronald
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.08.2012
Nature Publishing Group
Sage Publications Ltd
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Abstract Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[11C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPMVb). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions (Vb) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of Vb in RPM improved both parametric images and binding potential contrast between groups. Incorporation of Vb within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[11C]PK11195 neurodegeneration studies.
AbstractList Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[ super(11)C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPMV sub(b)). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions (V sub(b)) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V sub(b) in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V sub(b) within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[ super(11)C]PK11195 neurodegeneration studies.
Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[11C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPMVb). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions (Vb) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of Vb in RPM improved both parametric images and binding potential contrast between groups. Incorporation of Vb within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[11C]PK11195 neurodegeneration studies.
Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[ 11 C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPM V b ). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions ( V b ) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V b in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V b within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[ 11 C]PK11195 neurodegeneration studies.
Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R) -[ 11 C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPM V b ). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions ( V b ) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V b in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V b within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R) -[ 11 C]PK11195 neurodegeneration studies.
Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[(11)C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPMV(b)). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions (V(b)) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V(b) in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V(b) within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[(11)C]PK11195 neurodegeneration studies.
Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[(11)C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPMV(b)). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions (V(b)) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V(b) in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V(b) within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[(11)C]PK11195 neurodegeneration studies.
Author Hinz, Rainer
Lammertsma, Adriaan A
van Berckel, Bart NM
Schuitemaker, Alie
Boellaard, Ronald
Turkheimer, Federico E
Tomasi, Giampaolo
Yaqub, Maqsood
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  surname: van Berckel
  fullname: van Berckel, Bart NM
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  givenname: Alie
  surname: Schuitemaker
  fullname: Schuitemaker, Alie
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  givenname: Rainer
  surname: Hinz
  fullname: Hinz, Rainer
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  givenname: Federico E
  surname: Turkheimer
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  surname: Tomasi
  fullname: Tomasi, Giampaolo
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https://www.ncbi.nlm.nih.gov/pubmed/22588187$$D View this record in MEDLINE/PubMed
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Issue 8
Keywords parametric analysis
clustering
(R)-[11C]PK11195
reference tissue
Cluster analysis
Nervous system diseases
Optimization
Cerebral disorder
Encephalon
C]PK11195
Central nervous system disease
Positron emission tomography
(R)-
Cerebrovascular disease
Emission tomography
Language English
License CC BY 4.0
This work is licensed under the Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0
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Snippet Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic...
Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)...
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StartPage 1600
SubjectTerms Adult
Aged
Aged, 80 and over
Algorithms
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - physiopathology
Alzheimer's disease
Biological and medical sciences
Blood Volume - physiology
Blood. Blood coagulation. Reticuloendothelial system
Brain
Carbon Radioisotopes
Cerebellum
Cerebellum - blood supply
Cerebellum - diagnostic imaging
Cerebral blood flow
Cluster Analysis
Cognitive ability
Cognitive Dysfunction - diagnostic imaging
Cognitive Dysfunction - physiopathology
Computer Simulation
Data processing
Humans
Image Interpretation, Computer-Assisted
Isoquinolines - pharmacokinetics
Kinetics
Medical sciences
Middle Aged
Models, Neurological
Neurodegenerative diseases
Neurology
Original
Pharmacology. Drug treatments
Positron emission tomography
Positron-Emission Tomography - methods
Protein Binding
Protein transport
Reference Values
Reproducibility of Results
Thalamus - blood supply
Thalamus - diagnostic imaging
Tissue Distribution
Vascular diseases and vascular malformations of the nervous system
Young Adult
Title Optimization of Supervised Cluster Analysis for Extracting Reference Tissue Input Curves in (R)-[11C]PK11195 Brain PET Studies
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