Evaluation of objective functions for estimation of kinetic parameters
Evaluation of objective functions for estimation of kinetic parameters
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DOI：
10.1118/1.2135907
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发表时间：
2006-02-01
期刊：
影响因子：
3.8
通讯作者：
Christian, BT
中科院分区：
文献类型：
--
作者：
Muzic, RF;Christian, BT
人们对定量分析体内图像数据越来越感兴趣，因为这有助于客观比较和测量效果。在这方面，人们越来越多地转向药代动力学模型和估计这样的模型的参数。在这项工作中，几个参数估计方法进行了比较的背景下，最常见的药代动力学模型中使用的正电子发射断层扫描成像来描述葡萄糖代谢和受体-配体相互作用的示踪剂浓度。模拟数据是在5个不同的噪声水平中的每一个下用1000个实现生成的。使用七种迭代非线性估计方法对每个实现进行动力学参数估计：普通最小二乘（OLS）、加权最小二乘（WLS）、惩罚加权最小二乘（PWLS）、迭代重新加权最小二乘（IRLS）和扩展最小二乘变化（ELSO、ELS 1、ELS 3）。此外，还使用了广义线性最小二乘法（GLLS）。对于相对无噪声的数据，迭代非线性估计方法通常产生低偏差、高精度的参数估计，而对于GLLS，偏差更为突出。在更高、更真实的噪声水平下，可以看到估计方法之间的更大区别，ELS和IRLS方法通常比其他方法实现更好的精度。在高噪声水平下，WLS、GLLS和PWLS对某些动力学参数产生了具有较大偏倚（> 200%）的参数估计值。一般来说，有更有利的估计方法比常用的WLS。根据模型输出确定权重值的方法-IRLS，ELS 0，ELS 1和ELS 3-通常比直接根据实验数据确定权重值的方法性能更好。(c)2006年美国医学物理学家协会。
There is growing interest in quantitatively analyzing in vivo image data, as this facilitates objective comparisons and measurement of effect. In this regard, people increasingly turn to pharmacokinetic models and estimation of parameters of such models. In this work several parameter estimation methodologies were compared within the context of the most common pharmacokinetic model used in positron emission tomography imaging to describe glucose metabolism and receptor-ligand interactions at tracer concentrations. Simulated data were generated with 1000 realizations at each of 5 different noise levels. Estimates of the kinetic parameters were made for each realization using seven iterative, nonlinear estimation methodologies: ordinary least squares (OLS), weighted least squares (WLS), penalized weighted least squares (PWLS), iteratively reweighted least squares (IRLS), and variations of extended least squares (ELSO, ELS1, ELS3). Additionally, generalized linear least squares (GLLS) was also used. With relatively noise-free data, the iterative nonlinear estimation methods generally produced low-bias, high-precision parameter estimates, whereas with GLLS the bias was more prominent. Greater distinction between the estimation methods was seen at the higher, more realistic noise levels, with ELS and IRLS methods generally achieving better precision than the other methods. At the high noise levels WLS, GLLS, and PWLS yielded parameter estimates with large bias (> 200%) for some kinetic parameters. In general, there are more favorable estimator methodologies than the frequently employed WLS. Methods that determine values of weights based on model output-IRLS, ELS0, ELS1 and ELS3-generally perform better than methods that determine values of weights based directly on the experimental data. (c) 2006 American Association of Physicists in Medicine.