Automated Long Axial Field of View PET Image Processing and Kinetic Modelling with the TurBO Toolbox

  1. Jouni Tuisku
  2. Santeri Palonen
  3. Henri Kärpijoki
  4. Aino Latva-Rasku
  5. Nelli Tuomola
  6. Harri Harju
  7. Sergey V. Nesterov
  8. Vesa Oikonen
  9. Hidehiro Iida
  10. Jarmo Teuho
  11. Chunlei Han
  12. Tomi Karjalainen
  13. Anna K. Kirjavainen
  14. Johan Rajader
  15. Riku Klén
  16. Pirjo Nuutila
  17. Juhani Knuuti
  18. Lauri Nummenmaa
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Abstract

Long axial field of view (LAFOV) PET imaging requires a high level of automation and standardization, as the large number of target tissues increases the manual workload significantly. We introduce an automated analysis pipeline (TurBO, Turku total-BOdy) for preprocessing and kinetic modelling of LAFOV [15O]H2O and [18F]FDG PET data, enabling efficient and reproducible analysis of tissue perfusion and metabolism at regional and voxel-levels. The approach employs automated processing including co-registration, motion correction, automated CT segmentation for region of interest (ROI) delineation, image-derived input determination, and region-specific kinetic modelling of PET data.

Methods

We validated the analysis pipeline using Biograph Vision Quadra (Siemens Healthineers) LAFOV PET/CT scans from 21 subjects scanned with [15O]H2O and 16 subjects scanned with [18F]FDG using six segmented CT-based ROIs (cortical brain gray matter, left iliopsoas muscle, right kidney cortex and medulla, pancreas, spleen and liver) representing different levels of blood flow and glucose metabolism.

Results

Model fits showed good quality with consistent parameter estimates at both regional and voxel-levels (R² > 0.83 for [15O]H2O, R² > 0.99 for [18F]FDG). Estimates from manual and automated input functions were in concordance (R² > 0.74 for [15O]H2O, and R² > 0.78 for [18F]FDG) with minimal bias (<4% for [15O]H2O and <10% for [18F]FDG). Manually and automatically (CT-based) extracted ROI level data showed strong agreement (R² > 0.82 for [15O]H2O and R² > 0.83 for [18F]FDG), while motion correction had little impact on parameter estimates (R² > 0.71 for [15O]H2O and R² > 0.78 for [18F]FDG) compared with uncorrected data.

Conclusion

Our automated analysis pipeline provides reliable and reproducible parameter estimates across different regions, with an approximate processing time of 1-1.5 h per subject. This pipeline completely automates LAFOV PET analysis, reducing manual effort and enabling reproducible studies of inter-organ blood flow and metabolism, including brain-body interactions.

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