"Population" approach improves parameter estimation of kinetic models from dynamic PET data

Kinetic modeling is used to indirectly measure physiological parameters from dynamic positron emission tomography (PET) data. Usually, the unknown parameters of the model are estimated, in any given region of interest (ROI), by least squares (LS). However, when the signal-to-noise ratio (SNR) of PET data is too low, LS does not allow reliable parameter estimation. To overcome this problem, we study in this paper the applicability of approaches originally developed in the pharmacokinetic/pharmacodynamic literature and referred to as "population approaches". In particular, we consider the iterative two stage (ITS) method, which, given a set of M ROIs drawn on PET images of a given individual, estimates the unknown model parameters of each ROI by exploiting the information contained in all the M ROIs. After having revised the theory behind ITS, we assess its performance versus LS by using Monte Carlo simulations which allow us to evaluate the bias of the two methods in a variety of situations. Then, we compare the performance of LS and ITS in two case studies on [/sup 18/F]FDG kinetics in human skeletal muscle. Both simulated and real case studies results show that a population approach is of potential in modeling PET images since it allows to reliably estimate model parameters also in those ROIs where either a bad SNR or a poor sampling (e.g., infrequent scanning and/or short experiment duration) make the use of LS unsuccessful.