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

Objective.Scattered coincidences are a major source of quantitative bias in positron emission tomography (PET) and must be compensated during reconstruction using an estimate of scattered coincidences per line-of-response and time-of-flight bin. Such estimates are typically obtained from simulators with simple cylindrical scanner models that omit detector physics. Incorporating detector sensitivities for scatter is challenging, as scattered coincidences have less constrained properties (e.g. incidence angles) than true coincidences.Approach.We integrated a 5D single-photon detection probability lookup table (photon energy, incidence angle, detector location) into the simulator logic. The resulting scatter sinogram is multiplied by a precomputed, lookup table-specific scatter sensitivity sinogram to yield the scatter estimate. Scatter was simulated with 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:Long, high-count MCGPU-PET simulations from a known activity distribution (reference).Same distribution with limited simulation time and counts.Same low-count data with joint estimation of activity and scatter during reconstruction.We also adapted the approach to test it on two acquisitions from a real Signa PET/MR.Main result.In scenario 1, scatter-compensated reconstructions achieved<1%global bias in all active regions relative to true-only reconstructions. In scenario 2, noisy scatter estimates caused strong positive bias, but Gaussian smoothing restored accuracy to scenario 1 levels. In scenario 3, joint estimation under low-count conditions maintained<1%global bias in nearly all regions. For real scans, the Monte Carlo-based scatter estimate was very similar to the vendor scatter estimate.Significance.Although demonstrated with a fast MC simulator, the proposed scatter sensitivity modeling could enhance existing single scatter simulators used clinically, which typically neglect detector physics. This proof-of-concept also supports the feasibility of scatter estimation for real scans using fast MC simulation, offering potentially greater accuracy and robustness to acquisition noise.