Object independent scatter sensitivities for PET, applied to scatter estimation through fast Monte Carlo simulation

Abstract

Scattered coincidences introduce quantitative bias in positron emission tomography (PET) and must be compensated during reconstruction. Conventional scatter estimates typically rely on simplified cylindrical scanner models that omit detector physics. Incorporating detector sensitivities for scatter is challenging because scattered events exhibit less constrained properties, such as incidence angles, compared to true coincidences. We integrated a 5D single-photon detection probability lookup table (LUT) accounting for photon energy, incidence angle, and detector location into the simulator logic. The resulting scatter sinogram is scaled by a precomputed, LUT-specific scatter sensitivity sinogram. Scatter was simulated using MCGPU-PET, a fast Monte Carlo (MC) simulator with a simplified scanner model, and applied to phantom data from a simulated GE Signa PET/MR in GATE. We evaluated three scenarios: (1) high-count MC simulations from a known activity distribution; (2) limited-count simulations; and (3) joint estimation of activity and scatter under low-count conditions. The method was also tested on two real Signa PET/MR acquisitions. In scenario 1, scatter-compensated reconstructions achieved <1% global bias in all active regions. In scenario 2, noisy scatter estimates caused positive bias, but Gaussian smoothing restored accuracy to scenario 1 levels. In scenario 3, joint estimation maintained <1% bias in nearly all regions. For real scans, the MC-based scatter estimate closely matched the vendor-provided scatter estimate. This proof-of-concept demonstrates that scatter sensitivity modeling can enhance simulators by incorporating detector physics. It supports the feasibility of using fast MC simulations for real scans, offering improved accuracy and robustness to acquisition noise in clinical PET reconstruction.