Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data. Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach. Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns. A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

中文翻译：

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使用 [18F]FDG PET 同时估计用于量化脑葡萄糖代谢的模型衍生输入函数

通过动态 [18F]FDG PET 定量大脑葡萄糖代谢率 (CMRGlu) 需要侵入性动脉取样。使用动脉输入函数 (AIF) 的替代方法包括同时估计 (SIME) 方法，该方法通过一系列指数对图像衍生输入函数 (IDIF) 进行建模，该指数的系数是通过拟合多个体积的时间活动曲线 (TAC) 获得的。出于兴趣。SIME 的局限性是假设输入函数可以通过一系列指数精确建模。或者，我们提出了一种基于两组织室模型的 SIME 方法，从全脑 TAC 中提取高信噪比 (SNR) 模型导出的输入函数 (MDIF)。本研究的目的是介绍 MDIF 方法及其在动物和人类数据分析中的实施。进行模拟以评估 MDIF 方法的准确性。进行动物实验以比较衍生的 MDIF 与测量的 AIF (n = 5)。使用神经健康志愿者 (n = 18) 的动态 [18F]FDG PET 数据，将 MDIF 方法与原始 SIME-IDIF 进行比较。最后，通过实施变分贝叶斯参数估计方法研究了提取参数图像的可行性。模拟表明 MDIF 可以从全脑 TAC 中准确提取。在动物实验中发现 MDIF 和测量的 AIF 之间具有良好的一致性。同样，来自人类数据的 MDIF 与 IDIF 曲线下面积比为 1.02 ± 0.08，导致灰质 CMRGlu 具有良好的一致性：MDIF 和 CMRGlu 为 24.5 ± 3.6 和 23.9 ± 3.2 mL/100 g/min分别为IDIF。MDIF 方法在表征 [18F]FDG 的第一遍方面被证明具有优越性。使用 MDIF 获得的分组参数图像显示了预期的空间模式。提出了模型驱动的 SIME 方法来导出高 SNR 输入函数。MDIF 和 AIF 在动物实验中的良好一致性证明了其潜力。此外，人体研究中获得的 CMRGlu 估计值与文献值一致。MDIF 方法比原始 SIME 方法需要更少的拟合参数，并且具有可以对任何输入函数的形状进行建模的优点。反过来，当与稳健的参数估计方法（例如变分贝叶斯方法）相结合时，MDIF 的高信噪比有可能促进体素参数的提取。