Structural and Practical Identifiability of Dual-input Kinetic Modeling in Dynamic PET of Liver Inflammation

Abstract

Dynamic 18F-FDG PET with tracer kinetic modeling has the potential to noninvasively evaluate human liver inflammation using the FDG blood-to-tissue transport rate K1. Accurate kinetic modeling of dynamic liver PET data and K1 quantification requires the knowledge of dual-blood input function from the hepatic artery and portal vein. While the arterial input function can be derived from the aortic region on dynamic PET images, it is difficult to extract the portal vein input function accurately from PET. The optimization-derived dual-input kinetic modeling approach has been proposed to overcome this problem by jointly estimating the portal vein input function and FDG tracer kinetics from time activity curve fitting. In this paper, we further characterize the model properties by analyzing the structural identifiability of the model parameters using the Laplace transform and practical identifiability using Monte Carlo simulation based on fourteen patient datasets. The theoretical analysis has indicated that all the kinetic parameters of the dual-input kinetic model are structurally identifiable, though subject to local solutions. The Monte Carlo simulation results have shown that FDG K1 can be estimated reliably in the whole-liver region of interest with reasonable bias, standard deviation, and high correlation between estimated and original values, indicating of practical identifiability of K1. The result has also demonstrated the correlation between K1 and histological liver inflammation scores is reliable. FDG K1 quantification by the optimization-derived dual-input kinetic model is promising for assessing liver inflammation.