When a scanner is installed and begins to be used operationally, its actual performance may deviate somewhat from the predictions made at the design stage. Thus it is recommended that routine quality assurance (QA) measurements be used to provide an operational understanding of scanning properties. While QA data are primarily used to evaluate sensitivity and bias patterns, there is a possibility to also make use of such data sets for a more refined understanding of the 3-D scanning properties. Building on some recent work on analysis of the distributional characteristics of iteratively reconstructed PET data, we construct an auto-regression model for analysis of the 3-D spatial auto-covariance structure of iteratively reconstructed data, after normalization. Appropriate likelihood-based statistical techniques for estimation of the auto-regression model coefficients are described. The fitted model leads to a simple process for approximate simulation of scanner performance-one that is readily implemented in an R script. The analysis provides a practical mechanism for evaluating the operational error characteristics of iteratively reconstructed PET images. Simulation studies are used for validation. The approach is illustrated on QA data from an operational clinical scanner and numerical phantom data. We also demonstrate the potential for use of these techniques, as a form of model-based bootstrapping, to provide assessments of measurement uncertainties in variables derived from clinical FDG-PET scans. This is illustrated using data from a clinical scan in a lung cancer patient, after a 3-minute acquisition has been re-binned into three consecutive 1-minute time-frames. An uncertainty measure for the tumor SUVmax value is obtained. The methodology is seen to be practical and could be a useful support for quantitative decision making based on PET data.

中文翻译：

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3-D PET相关性的空间自回归分析及其在基于模型的数据模拟中的应用。

当安装扫描仪并开始投入使用时，其实际性能可能会与设计阶段的预测有所不同。因此，建议使用常规质量保证（QA）测量来提供对扫描属性的操作理解。虽然QA数据主要用于评估灵敏度和偏差模式，但也有可能也使用此类数据集来更精确地理解3-D扫描特性。在对迭代重建的PET数据的分布特征进行分析的一些最新工作的基础上，我们对归一化后的迭代重建数据的3-D空间自协方差结构进行分析，构建了一个自动回归模型。描述了用于估计自回归模型系数的基于似然的统计技术。拟合的模型导致了一个简单的过程，可以大致模拟扫描仪的性能，这很容易在R脚本中实现。该分析提供了一种实用的机制，用于评估迭代重建的PET图像的操作误差特征。仿真研究用于验证。在来自可操作的临床扫描仪的QA数据和数字体模数据上说明了该方法。我们还展示了使用这些技术作为基于模型的自举的一种形式的潜力，以提供对源自临床FDG-PET扫描的变量中测量不确定度的评估。使用来自肺癌患者的临床扫描数据可以说明这一点，经过3分钟的采集后，已重新组合为三个连续的1分钟时间范围。获得了肿瘤SUVmax值的不确定性度量。该方法被认为是实用的，并且可以为基于PET数据的定量决策提供有用的支持。