Abstract
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.