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Quantifying Brain [18F]FDG Uptake Noninvasively by Combining Medical Health Records and Dynamic PET Imaging Data

Research output: Contribution to journalArticlepeer-review

Elisa Roccia, Arthur Mikhno, R. Todd Ogden, J. John Mann, Andrew F. Laine, Elsa Angelini, Francesca Zanderigo

Original languageEnglish
Article number8598919
Pages (from-to)2576-2582
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number6
DOIs
Accepted/In press12 Dec 2018
Published1 Nov 2019

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Abstract

Full quantification of regional cerebral metabolic rate of glucose (rCMRglu) with [ 18F]fluorodeoxy-glucose ([ 18F]FDG) positron emission tomography (PET) imaging requires measurement of an arterial input function (AIF) curve, which is obtained with an invasive arterial blood sampling procedure during the scan. We previously proposed a non-invasive simultaneous estimation (nSIME) method that quantifies binding of a PET radioligand by combining individual electronic health records information and a pharmacokinetic AIF (PK-AIF) model. Initially applied only to [ 11C]DASB data, in this study we validate nSIME for a different radioligand, [ 18F]FDG, adapting the algorithm to the specific distribution and metabolism of this radioligand. We evaluate the impact of the PK-AIF model, the number of [ 18F]FDG-specific soft constraints, and the type of predictive strategy. The accuracy of nSIME is then compared to a population-based approach. All analyses are conducted on 67 [ 18F]FDG PET scans with arterial blood data available for comparison. nSIME performance is optimal for [ 18F]FDG when using the PK-AIF model, two soft constraints, and an aggregate model to predict the soft constraint values. Higher correlation and lower Bland-Altman spread against gold standard rCMRglu values based on arterial blood measurements are observed for nSIME (r = 0.83, spread = 1.55) compared to the population-based approach (r = 0.77, spread = 2.12). nSIME provides a data-driven estimation of both amplitude and shape of the AIF curve at the individual level and potentially enables non-invasive quantification of PET data across radioligands, avoiding the need for arterial blood sampling.

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