New method for the analysis of multiple positron emission tomography dynamic datasets: an example applied to the estimation of the cerebral metabolic rate of oxygen.

Positron emission tomography (PET) provides the ability to extract useful quantitative information not available through other radiological techniques. In certain studies, the physiological parameters of interest cannot be determined from the data obtained from a single PET experiment alone. In this case, multiple experiments are required. At present, the methods used to analyse measurements acquired from multiple experiments often involve considering them separately during the modelling procedures. These methods of analysis may cause errors to be propagated through successive modelling procedures and do not fully utilise the information content provided by the PET measurements. A new method is presented, based on linear least squares for the analysis of PET dynamic data acquired from multiple experiments. This method simultaneously considers the complete set of measurements obtained and provides reliable parameter estimates. The efficient use of the information content provided by multiple experiments is considered and the propagation of errors is discussed. To facilitate our discussion, we apply this new method to the estimation of the cerebral metabolic rate of oxygen and the parameters of the oxygen utilisation model as a practical example. The results demonstrate a significant improvement in the reliability and estimation accuracy of the estimates for this new method. Furthermore, this method reduced the likelihood of errors being propagated. Therefore, the proposed method is suitable for the analysis of multiple PET dynamic datasets.