Arterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic 18F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (CP*(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimated CP*(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling. The Feng 18F-FDG plasma concentration model was applied to estimate AIF parameters (n = 23). AIF normalization used either AUC(0–60 min) or CP*(0), estimated from an exponential fit. CP*(0) is also described as the ratio of the injected dose (ID) to initial distribution volume (iDV). iDV was modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15–45, 30–60, 45–75, 60–90 min) (PBIFAUC) and estimated CP*(0) (PBIFiDV). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and Patlak Ki values. The IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC and Ki comparison, 30–60 min was the most accurate time window for PBIFAUC; later time windows for scaling underestimated Ki (− 6 ± 8 to − 13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30–60), and PBIFiDV were 0.91, 0.94, and 0.90, respectively. The bias of Ki was − 9 ± 10%, − 1 ± 8%, and 3 ± 9%, respectively. Both PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.

中文翻译：

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全身动态 18F-FDG PET Patlak 成像的基于人群的输入函数评估


动脉血采样是获得动脉输入函数 (AIF) 以量化全身 (WB) 动态 18F-FDG PET 成像的金标准方法。然而，该过程是侵入性的并且通常不适用于临床环境。作为替代方案，我们使用两种标准化方法将 AIF 与基于人群的输入函数 (PBIF) 进行比较：曲线下面积 (AUC) 和外推初始血浆浓度 (CP*(0))。为了缩放 PBIF，我们测试了两种方法：(1) 图像导出输入函数 (IDIF) 的 AUC 和 (2) 估计 CP*(0)。本研究的目的是通过与金标准动脉血采样进行比较，验证 IDIF 和 PBIF 用于 FDG 肿瘤学 WB PET 研究。 Feng 18F-FDG 血浆浓度模型用于估计 AIF 参数 (n = 23)。 AIF 标准化使用 AUC（0–60 分钟）或 CP*(0)，根据指数拟合估计。 CP*(0) 也被描述为注射剂量 (ID) 与初始分布体积 (iDV) 的比率。 iDV 使用受试者身高和体重进行建模，系数在 23 名受试者中进行估计。在 12 名肿瘤患者中，我们计算了 IDIF（来自主动脉）和 PBIF，并根据 4 个时间窗口（15-45、30-60、45-75、60-90 分钟）的 IDIF AUC 进行缩放 (PBIFAUC) 并估计CP*(0) (PBIFiDV)。使用 AUC 值和 Patlak Ki 值将 IDIF 和 PBIF 与金标准 AIF 进行比较。 IDIF 早期低估了 AIF，后来又高估了它。因此，根据 AUC 和 Ki 的比较，30-60 分钟是 PBIFAUC 最准确的时间窗口；稍后缩放的时间窗口低估了 Ki（− 6 ± 8 至 − 13 ± 9%）。 AIF 和 IDIF、PBIFAUC(30-60) 和 PBIFiDV 之间的 AUC 相关性分别为 0.91、0.94 和 0.90。 Ki的偏差分别为−9±10%、−1±8%和3±9%。两种 PBIF 缩放方法都提供了良好的平均性能和适度的变化。通过改进 IDIF 方法并使用重测数据评估 PBIF，可以获得改进的性能。