Automated long axial field of view PET image processing and kinetic modelling with the TurBO toolbox

Abstract Purpose Long axial field of view (LAFOV) PET imaging requires extensive automation due to the large number of target tissues. Therefore, we introduce an open-source analysis pipeline (TurBO, Turku total-BOdy) for automated preprocessing and kinetic modelling of LAFOV [ 15 O]H 2 O and [ 18 F]FDG PET data. TurBO enables efficient, reproducible quantification of tissue perfusion and metabolism at regional- and voxel-levels through automated co-registration, motion correction, CT-based region of interest (ROI) segmentation, image-derived input function (IDIF) extraction, and region-specific kinetic modelling. Methods The pipeline was validated with Biograph Vision Quadra (Siemens Healthineers) LAFOV PET/CT data from 21 subjects scanned with [ 15 O]H 2 O and 16 subjects scanned with [ 18 F]FDG. Six CT-segmented ROIs (cortical brain gray matter, left iliopsoas muscle, right kidney cortex and medulla, pancreas, spleen and liver) were used to assess different levels of tissue perfusion and glucose metabolism. Results Model fits showed high quality with consistent estimates at regional and voxel-levels (R 2 > 0.83 for [ 15 O]H 2 O, R 2 > 0.99 for [ 18 F]FDG). Manual and automated IDIFs were in concordance (R 2 > 0.74 for [ 15 O]H 2 O, and R 2 > 0.78 for [ 18 F]FDG) with minimal bias (< 4% and < 10%, respectively). Manual and CT-segmented ROIs showed strong agreement (R 2 > 0.82 for [ 15 O]H 2 O and R 2 > 0.83 for [ 18 F]FDG). Motion correction had little impact on estimates (R 2 > 0.71 for [ 15 O]H 2 O and R 2 > 0.78 for [ 18 F]FDG) compared with uncorrected data. Conclusion The TurBO pipeline provides fully automated and reliable quantification for LAFOV PET data. It substantially reduces manual workload and enables standardized, reproducible assessment of inter-organ perfusion and metabolism.