Kinetic parameter estimation from compartment models using a genetic algorithm.

Abstract

Kinetic parameters were estimated from a three-compartment fluorodeoxyglucose model with three rate constants using a genetic algorithm. The performance of the genetic algorithm was investigated by simulation studies, in which brain time-activity data (TAD) were generated using cited mean values of rate constants and the plasma TAD obtained from positron emission tomographic studies. The accuracy of kinetic parameter estimation using the genetic algorithm was compared with that using the non-linear least-squares (NLSQ) method. The margin of error in the parameters estimated using the genetic algorithm tended to be smaller than that obtained by the NLSQ method. Although not statistically significant at a noise level of 5% in the brain TAD, the difference between the two methods became significant for all parameters at a noise level of 15% or higher. Our results suggest that the genetic algorithm is a promising means of estimating kinetic parameters from compartment models, because it is more robust against statistical noise than the NLSQ method and it can be rendered highly parallel for processing.