The use of DRLs has been extensively documented in the literature as a tool for protocol optimization across different x-ray imaging modalities in different countries. It recognizes the importance of developing and validating methods capable of correlating DRL quantities (CTDI, DLP and SSDE) with the technical parameters employed in CT studies. Such correlations must be supported by robust statistical methodologies in order to ensure the adoption of adequate optimization decisions. The aim of this work was to apply the Generalized Additive Model (GAM) statistical analysis in adult non-contrast chest and abdomen-pelvis CT typical values. These patient cohorts were statistically evaluated to identify correlations with key-parameters associated to the demographic patient information and machine dependent data, taking into account patients’ effective diameters, and body mass indexes (BMI). GAM was implemented considering each anatomical region in order to correlate the log-transformed DRL quantities (DRLq's) as outcomes given different key predictors related to image acquisition and patient characteristics. A total of 956 CT patient data were collected in this retrospective single-center study. Demographic variables demonstrate that age is not or it is just weakly-correlated to the DRLq's resulting from chest procedures, but it is strongly correlated when considering abdomen-pelvis examinations. Gender is correlated to the DRLq's for chest examinations adopting as a key predictor but it is only correlated with DLP adopting the BMI as a key predictor. The level of accuracy provided by the GAM was adequate for interpreting the large fluctuations of the DRLq's, technical parameters and demographic data observed for the studied patient cohorts. Our results reflect the importance of a comprehensive statistical evaluation of typical values. The domain of this technique is important to different CT imaging chain stakeholders and its application can be a key tool for decision-making process to effective optimization strategies.

中文翻译：

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成人胸腹盆腔CT检查典型值统计分析

文献中广泛记录了 DRL 的使用，作为不同国家不同 X 射线成像模式的协议优化工具。它认识到开发和验证能够将 DRL 数量（CTDI、DLP 和 SSDE）与 CT 研究中使用的技术参数相关联的方法的重要性。这种相关性必须得到强大的统计方法的支持，以确保采用适当的优化决策。这项工作的目的是将广义加性模型 (GAM) 统计分析应用于成人非对比胸部和腹部骨盆 CT 典型值。对这些患者队列进行统计评估，以确定与患者人口统计信息和机器相关数据相关的关键参数的相关性，同时考虑患者的有效直径和体重指数（BMI）。 GAM 的实施考虑了每个解剖区域，以便将对数转换的 DRL 量 (DRLq) 与给定与图像采集和患者特征相关的不同关键预测因子的结果相关联。这项回顾性单中心研究共收集了 956 例 CT 患者数据。人口统计变量表明，年龄与胸部手术产生的 DRLq 没有或只是弱相关，但在考虑腹部骨盆检查时，它是强相关的。性别与采用 BMI 作为关键预测因子的胸部检查 DRLq 相关，但仅与采用 BMI 作为关键预测因子的 DLP 相关。 GAM 提供的准确度足以解释所研究患者群体观察到的 DRLq、技术参数和人口统计数据的大幅波动。我们的结果反映了对典型值进行全面统计评估的重要性。该技术的领域对于不同的 CT 成像链利益相关者都很重要，其应用可以成为有效优化策略的决策过程的关键工具。