Sources of Variability in Normative Cerebral [18F]FDG-PET imaging

[18F]FDG PET outperforms MRI for detecting early tau-related neurodegeneration.

MRI-free amyloid PET quantification using a deep learning model and white matter reference

Deep learning–based amyloid PET segmentation enables accurate MRI-free quantification of gray and white matter SUVRs and simplifies clinical workflow while maintaining diagnostic performance comparable to MRI-based methods for AD diagnosis.

Regional differences in 18F-flutemetamol accumulation between amyloid-positive and -negative brains using high-resolution dedicated head PET scanner.

High-resolution dhPET revealed significantly higher SUVRs in Aβ⁺ compared with Aβ⁻ subjects in supratentorial structures, including cerebral white matter, and in cerebellar gray matter.

Revisiting intensity normalization in brain [18F]FDG PET: What is reliable in autoimmune encephalitis?

Purpose Data-driven intensity normalization has emerged as an alternative to proportional scaling (PS) and reference-region methods in brain [18F]FDG PET. However, no consensus exists for autoimmune encephalitis (AE), whose variable metabolic patterns and lack of a reliable disease-free reference region complicate normalization. We compared three methods; PS, iterative PS (iPS), and pons-based reference-region (RR) normalization, in patients with definite AE (n = 29, 42 ± 20 years) and healthy controls (HC; n = 53, 46 ± 15 y/o) examined with brain [18F]FDG PET at diagnosis. Methods Three sets of normalized [18F]FDG PET images (2 MBq/kg, Biograph mCT Flow PET/CT system, Siemens Healthcare) were generated using: (1) PS with an eroded AAL grey-matter mask; (2) iPS, applying PS then excluding voxels deviating from controls (SPM12, F-contrast p < 0.01 uncorrected) to create a subject-specific mask; and (3) RR, normalizing to mean pons uptake. Effects were assessed through voxel-based AE vs. HC comparisons in SPM12. Two limbic AE cases were analyzed longitudinally. Results PS and iPS revealed basal ganglia and mesiotemporal hypermetabolism in AE vs. HC that RR failed to detect (p < 0.05 FWE corrected). All methods identified cortical hypometabolism, slightly more extensive with RR. PS and iPS yielded broadly similar results. Follow-up showed that iPS and PS better captured initial abnormalities than RR. Conclusion PS and iterative PS may provide more robust normalization than RR in AE. As interest in neuroinflammatory disorders grows, standardized normalization protocols are needed to ensure consistency across studies.

Translating SUVR bias correction to amyloid PET enables early imaging and more accurate simplified quantification

Differential associations of tau extent and load with brain metabolic and cognitive dysfunction in Alzheimer's disease

Summary Background Reduced [18F]Fluorodeoxyglucose ([18F]FDG)-PET uptake is a core imaging feature of Alzheimer's disease (AD). While tau load correlates with this metabolic signature, it remains unclear whether the spatial extent of tauopathy (SEOT) more accurately explains brain glucose hypometabolic patterns. Here, we compared SEOT versus tau load to determine their ability to predict brain hypometabolic signatures in AD. Methods We performed a cross-sectional study of amyloid-β positive participants from ADNI (n = 150) and an atypical AD subset from the McGill University Research Centre for Studies in Ageing (MCSA; n = 44). Participants underwent [18F]AV1451 or [18F]MK6240 tau-PET and [18F]FDG-PET. Tau load was indexed with regional SUVR, and SEOT with the proportion of abnormal voxels. Linear regressions related temporal and whole-cortex tau-PET load or SEOT to [18F]FDG-PET. We also compared the accuracy of tau-PET metrics for identifying AD-like hypometabolism. Spearman correlations assessed SEOT/tau load-FDG associations at regional and network levels. Partial Least Squares (PLS) regression investigated whether distributed tau load and SEOT predicted [18F]FDG-PET signatures. Structural equation modelling and hierarchical linear models assessed associations between tau metrics and cognition dependent and independent of [18F]FDG-PET. Findings Whole-cortex SEOT best predicted decreased signal in the [18F]FDG-PET AD-meta-ROI. SEOT also performed better in classifying AD-related brain hypometabolism. Across regions and networks, SEOT performed similarly or better than tau load in predicting metabolic dysfunction. Voxelwise analyses suggested complementary predictive value of SEOT and tau load, each capturing slightly distinct spatial associations with [18F]FDG-PET. PLS demonstrated partially non-redundant contributions from tau load and SEOT. Cortical SEOT showed the strongest predictive value for cognition. Interpretation SEOT provides complementary, independent, and often stronger predictive value than tau load for brain metabolism, particularly for network-level dysfunction. SEOT may improve diagnostic characterisation and prediction of cognitive impairment beyond [18F]FDG-PET. Funding TRIAD is supported by the Weston Brain Institute, Canadian Institutes of Health Research, Canadian Consortium of Neurodegeneration and Aging, Brain Canada Foundation, the Fonds de Recherche du Québec – Santé, and the Colin J Adair Charitable Foundation. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and the Canadian Institutes of Health Research.