TY - JOUR
T1 - Feasibility of pharmacokinetic parametric PET images in scaled subprofile modelling using principal component analysis
AU - Peretti, D. bora E.
AU - Renken, Remco J.
AU - Reesink, Fransje E.
AU - de Jong, Bauke M.
AU - de Deyn, Peter P.
AU - Dierckx, Rudi A. J. O.
AU - Doorduin, Janine
AU - Boellaard, Ronald
AU - Vállez García, David
N1 - Funding Information: Ronald Boellaard has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No. 764458, which is not related to this work. Publisher Copyright: © 2021 The Author(s) Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Scaled subprofile model using principal component analysis (SSM/PCA) is a multivariate analysis technique used, mainly in [18F]-2-fluoro-2-deoxy-D-glucose (FDG) PET studies, for the generation of disease-specific metabolic patterns (DP) that may aid with the classification of subjects with neurological disorders, like Alzheimer's disease (AD). The aim of this study was to explore the feasibility of using quantitative parametric images for this type of analysis, with dynamic [11C]-labelled Pittsburgh Compound B (PIB) PET data as an example. Therefore, 15 AD patients and 15 healthy control subjects were included in an SSM/PCA analysis to generate four AD-DPs using relative cerebral blood flow (R1), binding potential (BPND) and SUVR images derived from dynamic PIB and static FDG-PET studies. Furthermore, 49 new subjects with a variety of neurodegenerative cognitive disorders were tested against these DPs. The AD-DP was characterized by a reduction in the frontal, parietal, and temporal lobes voxel values for R1 and SUVR-FDG DPs; and by a general increase of values in cortical areas for BPND and SUVR-PIB DPs. In conclusion, the results suggest that the combination of parametric images derived from a single dynamic scan might be a good alternative for subject classification instead of using 2 independent PET studies.
AB - Scaled subprofile model using principal component analysis (SSM/PCA) is a multivariate analysis technique used, mainly in [18F]-2-fluoro-2-deoxy-D-glucose (FDG) PET studies, for the generation of disease-specific metabolic patterns (DP) that may aid with the classification of subjects with neurological disorders, like Alzheimer's disease (AD). The aim of this study was to explore the feasibility of using quantitative parametric images for this type of analysis, with dynamic [11C]-labelled Pittsburgh Compound B (PIB) PET data as an example. Therefore, 15 AD patients and 15 healthy control subjects were included in an SSM/PCA analysis to generate four AD-DPs using relative cerebral blood flow (R1), binding potential (BPND) and SUVR images derived from dynamic PIB and static FDG-PET studies. Furthermore, 49 new subjects with a variety of neurodegenerative cognitive disorders were tested against these DPs. The AD-DP was characterized by a reduction in the frontal, parietal, and temporal lobes voxel values for R1 and SUVR-FDG DPs; and by a general increase of values in cortical areas for BPND and SUVR-PIB DPs. In conclusion, the results suggest that the combination of parametric images derived from a single dynamic scan might be a good alternative for subject classification instead of using 2 independent PET studies.
KW - Alzheimer's disease
KW - Disease pattern
KW - Pharmacokinetic modelling
KW - Pittsburgh compound B
KW - SSM/PCA
UR - http://www.scopus.com/inward/record.url?scp=85102789873&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.nicl.2021.102625
DO - https://doi.org/10.1016/j.nicl.2021.102625
M3 - Article
C2 - 33756179
SN - 2213-1582
VL - 30
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
M1 - 102625
ER -