Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation for 3D PET

In positron emission tomography (PET) imaging, scattered gamma photons typically account for more than 30% of total detected coincidence counts. Single scatter simulation (SSS) method is widely used for estimating scatter contribution in PET image reconstruction. Monte Carlo (MC) techniques are more accurate but computationally expensive. When using SSS, the modeled scatter contribution is typically scaled to match the measured data. Typically tail fitting is used for this scaling. However, when the available tail part is either too small or too noisy, the tail fitting technique may lead to artifacts in the reconstructed images. In this study, a hybrid method that combines SSS and MC is presented. The method uses SSS to approximate the shape of scatter contribution, and scales the SSS result by a scaling factor derived from a low-count MC simulation. Effectiveness of the method is evaluated with phantom and patient studies. Images reconstructed using SSS with tail-fitting scaling (SSS-TFS) and SSS with Monte Carlo scaling (SSS-MCS) are compared. Results show that SSS-MCS significantly reduces or eliminates the artifacts that present in SSS-TFS images. For smaller objects, both SSS-MCS and SSS-TFS produce artifact-free images. The MC simulation takes less than a second on a computer with 8 CPU cores to achieve less than 1% of scatter scaling factor variation. A hybrid scatter correction method that combines SSS and Monte Carlo simulation is developed for PET reconstruction. The method demonstrates a significant improvement in scatter correction accuracy. The added computational cost is negligible.