Compartmental modelling is an established method of quantifying 18F-FDG uptake; however, only recently has it been applied to evaluate pulmonary inflammation. Implementation of compartmental models remains challenging in the lung, partly due to the low signal-to-noise ratio compared to other organs and the lack of standardisation. Good reproducibility is a key requirement of an imaging biomarker which has yet to be demonstrated in pulmonary compartmental models of 18F-FDG; in this paper, we address this unmet need. Retrospective subject data were obtained from the EVOLVE observational study: Ten COPD patients (age =66±9; 8M/2F), 10 α1ATD patients (age =63±8; 7M/3F) and 10 healthy volunteers (age =68±8; 9M/1F) never smokers. PET and CT images were co-registered, and whole lung regions were extracted from CT using an automated algorithm; the descending aorta was defined using a manually drawn region. Subsequent stages of the compartmental analysis were performed by two independent operators using (i) a MIAKATTM based pipeline and (ii) an in-house developed pipeline. We evaluated the metabolic rate constant of 18F-FDG (Kim) and the fractional blood volume (Vb); Bland-Altman plots were used to compare the results. Further, we adjusted the in-house pipeline to identify the salient features in the analysis which may help improve the standardisation of this technique in the lung. The initial agreement on a subject level was poor: Bland-Altman coefficients of reproducibility for Kim and Vb were 0.0031 and 0.047 respectively. However, the effect size between the groups (i.e. COPD, α1ATD and healthy subjects) was similar using either pipeline. We identified the key drivers of this difference using an incremental approach: ROI methodology, modelling of the IDIF and time delay estimation. Adjustment of these factors led to improved Bland-Altman coefficients of reproducibility of 0.0015 and 0.027 for Kim and Vb respectively. Despite similar methodology, differences in implementation can lead to disparate results in the outcome parameters. When reporting the outcomes of lung compartmental modelling, we recommend the inclusion of the details of ROI methodology, input function fitting and time delay estimation to improve reproducibility.

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¹⁸ F-FDG PET / CT隔室模型评估肺部炎症的可重复性

隔室建模是量化18F-FDG摄取的既定方法；然而，直到最近，它才被用于评估肺部炎症。隔室模型的实施在肺中仍然具有挑战性，部分原因是与其他器官相比信噪比低以及缺乏标准化。良好的再现性是成像生物标志物的关键要求，尚未在18F-FDG的肺室模型中得到证实。在本文中，我们解决了这一未满足的需求。回顾性受试者数据来自EVOLVE观察性研究：10例COPD患者（年龄= 66±9； 8M / 2F），10例α1ATD患者（年龄= 63±8； 7M / 3F）和10例健康志愿者（年龄= 68±8） ; 9M / 1F）永不吸烟。PET和CT图像被共同配准，并使用自动算法从CT提取整个肺区域；使用人工绘制的区域定义降主动脉。隔室分析的后续阶段是由两个独立的操作员使用（i）基于MIAKATTM的管道和（ii）内部开发的管道进行的。我们评估了18F-FDG的代谢速率常数（Kim）和血容量分数（Vb）。用布兰德-奥特曼图比较结果。此外，我们调整了内部管道，以识别分析中的显着特征，这可能有助于改善该技术在肺中的标准化。在主题水平上的初步共识很差：Kim和Vb的Bland-Altman再现性系数分别为0.0031和0.047。但是，使用任一管道，两组之间（即COPD，α1ATD和健康受试者）的效应大小相似。我们使用增量方法确定了造成这种差异的关键因素：ROI方法，IDIF建模和时间延迟估计。调整这些因素后，Kim和Vb的Bland-Altman重现性系数分别提高了0.0015和0.027。尽管采用了相似的方法，但实施方法上的差异可能导致结果参数的结果不一致。当报告肺室模型的结果时，我们建议包括ROI方法，输入函数拟合和时间延迟估计的细节，以提高可重复性。尽管采用了相似的方法，但实施方法上的差异可能导致结果参数的结果不一致。当报告肺室模型的结果时，我们建议包括ROI方法，输入函数拟合和时间延迟估计的细节，以提高可重复性。尽管采用了相似的方法，但实施方法上的差异可能导致结果参数的结果不一致。当报告肺室模型的结果时，我们建议包括ROI方法，输入函数拟合和时间延迟估计的细节，以提高可重复性。