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 OH 2 O and 18 FFDG 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 OH 2 O and 16 subjects scanned with 18 FFDG. 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 OH 2 O, R 2 > 0.99 for 18 FFDG). Manual and automated IDIFs were in concordance (R 2 > 0.74 for 15 OH 2 O, and R 2 > 0.78 for 18 FFDG) with minimal bias ( 0.82 for 15 OH 2 O and R 2 > 0.83 for 18 FFDG). Motion correction had little impact on estimates (R 2 > 0.71 for 15 OH 2 O and R 2 > 0.78 for 18 FFDG) 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.
Tuisku et al. (Sat,) studied this question.