A method for kinetic analysis of dynamic positron emission tomography (PET) data by linear programming that allows identification of the components of a measured PET signal without predefining a compartmental model has recently been proposed by Cunningham and co-workers. The method identifies a small subset of functions from a large input set of feasible functions that best fits the time course of total radioactivity measured by PET. To investigate in detail the properties of this technique, we applied it to PET studies with [18F]fluorodeoxyglucose, a tracer with well-characterized kinetic properties. We examined dynamically acquired data over various time intervals in many brain regions and found that the number of components identified by the method is stable and consistent with the presence of kinetic heterogeneity in every region. We optimized the method for determination of regional rates of glucose utilization; calculated rates were found to be somewhat dependent upon the treatment of noise in the measured tissue data and upon the time interval in which the data were collected. The application of a numerical filter to remove noise in the data resulted in values for regional cerebral glucose utilization that were stable with time and consistent with rates determined by the other established techniques. Based on the results of the current study, we expect that the spectral analysis technique will prove to be a highly flexible tool for kinetic analysis of other tracer compounds; it is capable of producing low-variance, time-stable estimates of physiological parameters when optimized for time interval of application, input spectrum of components, and processing of noise in the data.