本文提出一種估算正子電腦斷層掃描之散射事件的新方法。此方法是採用序列子集均值最大化重建法(OSEM)以估計低頻的散射事件。我們分別使用蒙第卡羅(Monte Carlo)模擬，假體及臨床實驗來評估此方法。我們稱此方法為

　　　　　A new method to assess the scatter component in positron emission tomography (PET) based on estimating sthe low-frequency component correesponding to maximization (OSEM) reconstructions is proposed in this paper and evaluated using Monte Carlo simulation studies, experimental phantom measurements and clinical studies. The rationale of this method called Statistical Reconstruction-Based Scatter Correction (SRBSC) is that the image corresponding to scattered events in the projection data consists of almost low-frequency components of activity distribution and that the low-frequency ones in successive iterations of statistical reconstruction methods such as OSME. The second assumption is that the high-frequency components will be smeared, i.e. filtered by the scatter response kernels. A simple model has been devised to parameterize the scatter component using Monte Carlo simulations. The unscattered component estimated using SRBSC was compared to the true unscattered component as estimated by Monte Carlo simulations for simple phantom geometries and clinically realistic source distributions. Quantitative analysis was also performed on reconstructed images using simple metrics like the contrast, absolute concentration, recovery coefficient and signal-to-noise ratio. The SRBSC method tends to undercorrect for scatter in most regions of the 3D Hoffman brain phantom, but gives good activity recovery values which average within 1%. It was concluded that the proposed method improves image quality and the contrast compared to the case where no correction is applied and that an accurate modeling of the scatter component is essential for a proper scatter correction.