Thiele Frank, Buchert Ralph
Philips Research Europe, Aachen, Germany.
Nucl Med Commun. 2008 Feb;29(2):179-88. doi: 10.1097/MNM.0b013e3282f28138.
Pharmacokinetic modelling of dynamic PET data has become an important tool to analyse in-vivo studies in humans and animals. Estimation of the model parameters often requires non-linear regression of an objective function such as weighted least squares. Since the noise properties of the data are not known exactly in practice, several weighting schemes have been proposed. The objective of this study was to evaluate the impact of commonly used weights on neuroreceptor quantification with the simplified reference tissue model (SRTM).
We compared the following weights: uniform, Poisson statistics-based ideal and noisy weights, iterative weighting, and a noise-free approximation of Poisson weights. Ten thousand time-activity curves (TACs) were simulated for several noise levels and the three neuroreceptor PET ligands C-(+)McN5652, C-DASB, and C-raclopride. Each TAC was fitted using weighted non-linear regression of the SRTM. We assessed bias and variation of the parameter estimates as well as quality of fit and parameter distributions.
Results differed substantially between ligands and between model parameters. Best parameter estimates were obtained with the noise-free approximation of Poisson weights. The often-used noisy Poisson weights performed worst for all ligands. Uniform weighting gave acceptable parameter estimates for most setups.
'Choice of weights' is important in pharmacokinetic neuroreceptor quantification with the SRTM. Weights estimated directly from noisy data should be avoided as they can severely degrade parameter estimation and the statistical power of a study. If the noise characteristic of the data is unknown, uniform weighting is recommended.
动态正电子发射断层扫描（PET）数据的药代动力学建模已成为分析人类和动物体内研究的重要工具。模型参数的估计通常需要对目标函数进行非线性回归，如加权最小二乘法。由于在实际中数据的噪声特性并不确切知晓，因此提出了几种加权方案。本研究的目的是评估常用权重对简化参考组织模型（SRTM）神经受体定量的影响。
我们比较了以下几种权重：均匀权重、基于泊松统计的理想权重和噪声权重、迭代权重以及泊松权重的无噪声近似。针对几种噪声水平以及三种神经受体PET配体C-(+)McN5652、C-DASB和C-雷氯必利模拟了一万条时间-活度曲线（TAC）。每条TAC都使用SRTM的加权非线性回归进行拟合。我们评估了参数估计的偏差和变异性以及拟合质量和参数分布。
不同配体之间以及模型参数之间的结果差异很大。使用泊松权重的无噪声近似获得了最佳参数估计。对于所有配体，常用的噪声泊松权重表现最差。在大多数设置下，均匀加权给出了可接受的参数估计。
在使用SRTM进行药代动力学神经受体定量时，“权重的选择”很重要。应避免直接从有噪声的数据估计权重，因为它们会严重降低参数估计和研究的统计效力。如果数据的噪声特征未知，建议使用均匀加权。
