A digital twin of the Biograph Vision Quadra long axial field of view PET/CT: Monte Carlo simulation and image reconstruction framework

Background: The high sensitivity and axial coverage of large axial field of view (LAFOV) PET scanners have an unmet potential for total-body PET research. Despite these technological advances, inherent challenges to PET scans such as patient motion persist. To provide simulation-derived ground truth information, we developed a digital replica of the Biograph Vision Quadra LAFOV PET/CT scanner closely mimicking real event processing and image reconstruction. Material and methods: The framework uses a GATE model in combination with vendor-specific software prototypes for event processing and image reconstruction (e7 tools, Siemens Healthineers). The framework was validated against experimental measurements following the NEMA NU-2 2018 standard. In addition, patient-like simulations were performed with the XCAT phantom, including respiratory motion and modeled lesions of 5, 10, 20 mm size, to assess the impact of motion artefacts on PET images using a motion-free reference. Results: The simulation framework demonstrated high accuracy in replicating scanner performance in terms of image quality, contrast recovery (37 mm sphere: 86.5% and 85.5%; 28 mm: 82.6% and 82.4%; 22 mm: 78.8% and 77.7%; 17 mm: 74.9% and 74.6%; 13 mm: 67.0% and 67.9%; 10 mm: 55.5% and 64.3%), image noise (CV of 7.5% and 7.7%) and sensitivity (174.6 cps/kBq and 175.3 cps/kBq) for the simulation and experimental data, respectively. High agreement was found for the spatial resolution with a difference of 0.4 ± 0.3 mm and the NECR aligned well with a maximum deviation of 9%, particularly in the clinical activity range below 10 kBq/mL. Motion induced artefacts resulted in a quantification error at lesion level between − 12.3% and − 45.1%. Conclusion: The experimentally validated digital twin of the Biograph Vision Quadra facilitates detailed studies of realistic patient scenarios while offering unprecedented opportunities for motion correction, dosimetry, AI training, and imaging protocol optimization.