The purpose of this study is to evaluate the identifiability of physiological tissue parameters by pharmacokinetic modeling of concentration-time curves derived under conditions that are realistic for dynamic-contrast-enhanced (DCE) imaging and to assess the information-theoretic approach of multimodel inference using nested models. Tissue curves with a realistic noise level were simulated by means of an axially distributed multipath reference model using typical values reported in literature on plasma flow, permeability-surface area product, and volume fractions of the intravascular and interstitial space. The simulated curves were subsequently analyzed by a two-compartment model containing these physiological quantities as fit parameters as well as by two reduced models with only three and two parameters formulated for the case of a permeability-limited and a flow-limited scenario, respectively. The competing models were ranked according to Akaike's information criterion (AIC), balancing the bias versus variance trade-off. To utilize the information available from all three models, model-averaged parameters were estimated using Akaike weights that quantify the relative strength of evidence in favor of each model. As compared to the full model, the reduced models yielded equivalent or even superior AIC values for scenarios where the structural information in the tissue curves on either the plasma flow or the capillary permeability was limited. Multimodel inference took effect to a considerable extent in half of the curves and improved the precision of the estimated tissue parameters. As theoretically expected, the plasma flow was subject to a systematic (but largely correctable) overestimation, whereas the other three physiological tissue parameters could be determined in a numerically robust and almost unbiased manner. The presented concept of pharmacokinetic analysis of noisy DCE data using three nested models under an information-theoretic paradigm offers promising prospects for the noninvasive quantification of physiological tissue parameters.

译文

:这项研究的目的是通过对在动态对比增强(DCE)成像现实的条件下得出的浓度-时间曲线进行药代动力学建模,评估生理组织参数的可识别性,并评估多模型推理的信息理论方法使用嵌套模型。借助轴向分布的多径参考模型,使用文献中报道的典型值(血浆流量,通透性表面积积以及血管内和间隙空间的体积分数),模拟具有实际噪声水平的组织曲线。随后通过包含这些生理量作为拟合参数的两室模型以及通过分别针对渗透率受限和流量受限的情况分别制定了三个参数和两个参数的两个简化模型来分析模拟曲线。根据Akaike的信息标准(AIC)对竞争模型进行排名,以平衡偏差与差异权衡。为了利用可从所有三个模型获得的信息,使用Akaike权重估计模型平均参数,该权重量化了支持每种模型的证据的相对强度。与完整模型相比,对于组织结构中有关血浆流量或毛细血管通透性的结构信息受到限制的情况,简化模型产生的等效或什至更好的AIC值。多模型推断在很大程度上影响了一半的曲线,并提高了估计的组织参数的精度。如理论上所预期的,血浆流量受到系统的(但在很大程度上可校正)过高估计,而其他三个生理组织参数可以以数值上鲁棒且几乎无偏的方式确定。提出的在信息理论范式下使用三个嵌套模型的嘈杂DCE数据的药代动力学分析概念为生理组织参数的非侵入式量化提供了有希望的前景。

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