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

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.