Brix Gunnar, Zwick Stefan, Kiessling Fabian, Griebel Jürgen
Department of Medical and Occupational Radiation Protection, Federal Office for Radiation Protection, D-85762 Oberschleissheim, Germany.
Med Phys. 2009 Jul;36(7):2923-33. doi: 10.1118/1.3147145.
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.
本研究的目的是通过对在动态对比增强（DCE）成像实际条件下获得的浓度-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，并评估使用嵌套模型的多模型推断的信息论方法。利用轴向分布的多路径参考模型，采用文献报道的血浆流量、通透表面积乘积以及血管内和间质空间体积分数的典型值，模拟了具有实际噪声水平的组织曲线。随后，通过一个包含这些生理量作为拟合参数的双室模型，以及分别针对通透性受限和流量受限情况制定的仅具有三个和两个参数的两个简化模型，对模拟曲线进行分析。根据赤池信息准则（AIC）对竞争模型进行排序，平衡偏差与方差的权衡。为了利用来自所有三个模型的可用信息，使用赤池权重估计模型平均参数，该权重量化了支持每个模型的证据的相对强度。与完整模型相比，在组织曲线中关于血浆流量或毛细血管通透性的结构信息有限的情况下，简化模型产生了等效甚至更好的AIC值。多模型推断在一半的曲线中在很大程度上发挥了作用，并提高了估计的组织参数的精度。正如理论预期的那样，血浆流量存在系统性（但在很大程度上可校正）的高估，而其他三个生理组织参数可以以数值稳健且几乎无偏差的方式确定。在信息论范式下使用三个嵌套模型对有噪声的DCE数据进行药代动力学分析的所提出概念，为生理组织参数的无创量化提供了有前景的前景。
