Towards the reduction of dynamic image data in position emission tomography studies

In this study, we propose a method and investigate the reduction of dynamic image data with positron emission tomography (PET). The method is based upon the use of sampling schedules with a reduced number of scanning intervals and the use of an integral model in the cost function of nonlinear regression. The application of this method is illustrated by the problem of estimating the metabolic rate of glucose with the [18F]2-fluoro-2-deoxyglucose (FDG) model. Computer simulations were performed using various sampling schedules with scanning intervals of different lengths. The results were compared in terms of the accuracy and precision of the estimated parameters. It has been found that the use of sampling schedules with a reduced number of scanning intervals in conjunction with the integral model is very effective. The number of images in dynamic PET FDG studies can be reduced by a factor of 4.5 without losing the accuracy and precision of the parameter estimates.