EXPRESS: Blood-free full quantification of dynamic TSPO PET imaging with [11C]PBR28

BACKGROUND: Quantitative dynamic PET imaging requires metabolite-corrected arterial input functions, traditionally obtained through invasive arterial blood sampling, limiting broader adoption of quantitative kinetic modelling. Achieving generalisability across datasets, scanners, and imaging centres remains a major challenge for data-driven alternatives. METHODS: We developed ASTRA, a deep-learning-based framework for arterial input function estimation from dynamic brain TSPO PET. ASTRA was developed and validated using arterial measurements in one dataset (n=90), then transfer-learned and evaluated against arterial measurements at a second centre (n=52). Building upon this, ASTRA was applied to an independent clinical cohort (n=32) from the second site but without arterial reference data, where quantitative analyses had therefore not been performed, to assess whether it could reproduce previously reported semi-quantitative cross-sectional group differences. Robustness to altered tracer kinetics was evaluated in an out-of-distribution pharmacological blockade cohort (n=5). RESULTS: ASTRA-derived quantitative estimates agreed with arterial measurements (VT: CCC=0.658-0.805), with higher uncertainty associated with greater error. In the clinical cohort, ASTRA reproduced SUVR-based group differences using DVR (p<0.001), except in the pituitary gland (p=0.12). Finally, ASTRA yielded receptor occupancy estimates (0.92±0.17) closely matching arterial values (0.94±0.12) in the blocking cohort. CONCLUSION: These findings demonstrate the feasibility of multi-site, non-invasive quantitative TSPO PET.