TY - CHAP
T1 - Automated quantification of FDOPA PET using XNAT
AU - Nordio, G.
AU - Easmin, R.
AU - Giacomel, A.
AU - Dipasquale, O.
AU - Martins, D.
AU - Schiulaz, A.
AU - Williams, S.
AU - Turkheimer, F.
AU - Howes, O.
AU - Veronese, M.
N1 - Publisher Copyright:
© 2023 Convegno Nazionale di Bioingegneria. All rights reserved.
PY - 2023
Y1 - 2023
N2 - In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables. An instance of XNAT imaging platform was used to store the King's College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. We found good reproducibility of the data analysis by the automated pipeline (for the controls ICC=0.71, for the psychotic patients ICC=0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F=10.7, p<0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid recourse for standardized and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.
AB - In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables. An instance of XNAT imaging platform was used to store the King's College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. We found good reproducibility of the data analysis by the automated pipeline (for the controls ICC=0.71, for the psychotic patients ICC=0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F=10.7, p<0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid recourse for standardized and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.
KW - big data repository
KW - data analysis pipeline
KW - FDOPA PET
KW - neuroimaging biomarker
UR - http://www.scopus.com/inward/record.url?scp=85175811121&partnerID=8YFLogxK
M3 - Conference paper
AN - SCOPUS:85175811121
T3 - Convegno Nazionale di Bioingegneria
BT - 8th National Congress of Bioengineering, GNB 2023 - Proceedings
PB - Patron Editore S.r.l.
T2 - 8th National Congress of Bioengineering, GNB 2023
Y2 - 21 June 2023 through 23 June 2023
ER -