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Introduction SUV measurements from static brain [18F]FDG PET acquisitions are a commonly used tool in preclinical research, providing a simple alternative for kinetic modelling, which requires complex and time-consuming dynamic acquisitions. However, SUV can be severely affected by the animal handling and preconditioning protocols, primarily by those that may induce changes in blood glucose levels (BGL). Here, we aimed at developing and investigating the feasibility of SUV-based approaches for a wide range of BGL far beyond normal values, and consequently, to develop and validate a new model to generate standardized and reproducible SUV measurements for any BGL.Material and methods We performed dynamic and static brain [18F]FDG PET acquisitions in 52 male Sprague-Dawley rats sorted into control (n = 10), non-fasting (n = 14), insulin-induced hypoglycemia (n = 12) and glucagon-induced hyperglycemia (n = 16) groups. Brain [18F]FDG PET images were cropped, aligned and co-registered to a standard template to calculate whole-brain and regional SUV. Cerebral Metabolic Rate of Glucose (CMRglc) was also estimated from 2-Tissue Compartment Model (2TCM) and Patlak plot for validation purposes.Results Our results showed that BGL=100±6 mg/dL can be considered a reproducible reference value for normoglycemia. Furthermore, we successfully established a 2nd-degree polynomial model (C1=0.66E-4, C2=-0.0408 and C3=7.298) relying exclusively on BGL measures at pre-[18F]FDG injection time, that characterizes more precisely the relationship between SUV and BGL for a wide range of BGL values (from 10 to 338 mg/dL). We confirmed the ability of this model to generate corrected SUV estimations that are highly correlated to CMRglc estimations (R2= 0.54 2TCM CMRgluc and R2= 0.49 Patlak CMRgluc). Besides, slight regional differences in SUV were found in animals from extreme BGL groups, showing that [18F]FDG uptake is mostly directed toward central regions of the brain when BGLs are significantly decreased.Conclusion Our study successfully established a non-linear model that relies exclusively on pre-scan BGL measurements to characterize the relationship between [18F]FDG SUV and BGL. The extensive validation confirmed its ability to generate SUV-based surrogates of CMRglu along a wide range of BGL and it holds the potential to be adopted as a standard protocol by the preclinical neuroimaging community using brain [18F]FDG PET imaging.

Background: [18F]flortaucipir (FTP) tau PET quantification is known to be affected by non-specific binding in off-target regions. Although partial volume correction (PVC) techniques partially account for this effect, their inclusion may also introduce noise and variability into the quantification process. While the impact of these effects has been studied in cross-sectional designs, the benefits and drawbacks of PVC on longitudinal FTP studies is still under scrutiny. The aim of this work was to study the performance of the most common PVC techniques for longitudinal FTP imaging. Methods: A cohort of 247 individuals from the Alzheimer's Disease Neuroimaging Initiative with concurrent baseline FTP-PET, amyloid-beta (Aβ) PET and structural MRI, as well as with follow-up FTP-PET and MRI were included in the study. FTP-PET scans were corrected for partial volume effects using Meltzer's, a simple and popular analytical PVC, and both the region-based voxel-wise (RBV) and the iterative Yang (iY) corrections. FTP SUVR values and their longitudinal rates of change were calculated for regions of interest (ROI) corresponding to Braak Areas I-VI, for a temporal meta-ROI and for regions typically displaying off-target FTP binding (caudate, putamen, pallidum, thalamus, choroid plexus, hemispheric white matter, cerebellar white matter, and cerebrospinal fluid). The longitudinal correlation between binding in off-target and target ROIs was analysed for the different PVCs. Additionally, group differences in longitudinal FTP SUVR rates of change between Aβ-negative (A-) and Aβ-positive (A+), and between cognitively unimpaired (CU) and cognitively impaired (CI) individuals, were studied. Finally, we compared the ability of different partial-volume-corrected baseline FTP SUVRs to predict longitudinal brain atrophy and cognitive decline. Results: Among off-target ROIs, hemispheric white matter showed the highest correlation with longitudinal FTP SUVR rates from cortical target ROIs (R2=0.28–0.82), with CSF coming in second (R2=0.28–0.42). Application of voxel-wise PVC techniques minimized this correlation, with RBV performing best (R2=0.00–0.07 for hemispheric white matter). PVC also increased group differences between CU and CI individuals in FTP SUVR rates of change across all target regions, with RBV again performing best (No PVC: Cohen's d = 0.26–0.66; RBV: Cohen's d = 0.43–0.74). These improvements were not observed for differentiating A- from A+ groups. Additionally, voxel-wise PVC techniques strengthened the correlation between baseline FTP SUVR and longitudinal grey matter atrophy and cognitive decline. Conclusion: Quantification of longitudinal FTP SUVR rates of change is affected by signal from off-target regions, especially the hemispheric white matter and the CSF. Voxel-wise PVC techniques significantly reduce this effect. PVC provided a significant but modest benefit for tasks involving the measurement of group-level longitudinal differences. These findings are particularly relevant for the estimations of sample sizes and analysis methodologies of longitudinal group studies.