Abstract
Dynamic PET scan is capable of describing kinetics of the activity distribution of radiopharmaceuticals inside an object in vivo. Typically, by fitting the time-activity curve from dynamic PET images based on a given kinetic model, the corresponding estimated kinetic parameters can provide functional and physiological information. In this work, we consider three-compartmental model to describe the FDG metabolism in a living body. This model consists of 4 kinetic parameters which represent the rate constants of FDG transport and metabolism between plasma and tissues. Conventionally, Levenberg-Marquardt (LM) algorithm is used to estimate the parameters of nonlinear model. However, LM algorithm suffers some problems such as divergence or incapable estimation at high noise level. Based on the least square criteria and to ensure the convergence of kinetic parameter estimation, we suggested a weighted Levenberg-Marquardt (WLM) algorithm which introduces a relaxation parameter to prevent estimation procedure from divergence. The experimental results indicate that WLM can estimate kinetic parameters accurately as LM at low noise level, and still maintain estimation accuracy and stability at high noise level where LM algorithm becomes divergent. When applied to clinical images, WLM algorithm can also converge efficiently, while LM algorithm diverges once again.