Division of Imaging Sciences and Biomedical Engineering, The Rayne Institute, St. Thomas' Hospital, King's College London, London, SE1 7EH, UK.
Ann Nucl Med. 2011 Nov;25(9):643-9. doi: 10.1007/s12149-011-0514-y. Epub 2011 Jul 14.
In 3D PET, scatter of the gamma photons is one of the most significant physical factors which degrades not only image quality but also quantification. The currently most used scatter estimation method is the analytic single scatter simulation (SSS) which usually accommodates for multiple scattering by scaling the single scatter estimation. However, it has not been clear yet how accurate this approximation is for cases where multiple scatter is significant, raising the question: "How important is correction for multiple scattered photons, and how accurately do we need to simulate all scattered events by appropriate scaling?" This study answers these questions and evaluates the accuracy of SSS implementation in the open-source library STIR.
Different scatter orders approximations are evaluated including different levels of scattering and different scaling approaches using Monte Carlo (i.e. SimSET) data. SimSET simulations of a large anthropomorphic phantom were reconstructed with iterative reconstruction algorithms. Images reconstructed with 3D filtered back-projection reprojection algorithm have been compared quantitatively in order to clarify the errors due to different scatter order approximations.
Quantification in regions has improved by scatter correction. For example, in the heart the ideal value was 3, whereas before scatter correction the standard uptake value (SUV) was 4.0, after single scatter correction was 3.3 and after single and double scatter correction was 3.0. After correction by scaling single scatter with tail-fit, the SUV was 3.1, whereas with total-fit it was 3.0. Similarly, for the SSS correction methodology implemented in STIR using tail-fit the heart SUV was 3.1 whereas using total-fit it was 3.0.
The results demonstrate that correction for double scatter improves image contrast and therefore it is required for the accurate estimation of activity distribution in PET imaging. However, it has been also shown that scaling the single scatter distribution is a reasonable approximation to compensate for total scatter. Finally, scatter correction with STIR has shown excellent agreement with Monte Carlo simulations.
在 3D PET 中，伽马光子的散射是降低图像质量和定量准确性的最重要物理因素之一。目前最常用的散射估计方法是解析单散射模拟（SSS），该方法通常通过对单散射估计进行缩放来适应多次散射。然而，对于多次散射显著的情况，这种近似的准确性还不清楚，这就提出了一个问题：“校正多次散射光子有多重要，我们需要通过适当的缩放来精确模拟所有散射事件？”本研究回答了这些问题，并评估了开源库 STIR 中 SSS 实现的准确性。
使用蒙特卡罗（即 SimSET）数据评估不同散射阶近似，包括不同散射级和不同缩放方法。使用迭代重建算法对大型人体模型的 SimSET 模拟进行重建。为了阐明不同散射阶近似引起的误差，对使用 3D 滤波后向投影重投影算法重建的图像进行了定量比较。
散射校正提高了区域内的定量准确性。例如，在心脏中，理想值为 3，而在散射校正之前，标准摄取值（SUV）为 4.0，在单散射校正后为 3.3，在单散射和双散射校正后为 3.0。在用尾部拟合对单散射进行缩放校正后，SUV 为 3.1，而用总拟合时 SUV 为 3.0。同样，对于 STIR 中使用尾部拟合的 SSS 校正方法，心脏 SUV 为 3.1，而使用总拟合时 SUV 为 3.0。
结果表明，校正双散射可以提高图像对比度，因此对于准确估计 PET 成像中的活性分布是必需的。然而，也已经表明，对单散射分布进行缩放是补偿总散射的合理近似。最后，STIR 的散射校正与蒙特卡罗模拟显示出极好的一致性。
