Imaging Radiotracer Model Parameters in PET: A Mixture Analysis Approach

A variety of sophisticated radiotracer models are available for the quantitative interpretation of dynamic positron emission tomography (PET) data. Parameters in these models are used to define quantities, such as metabolic rate, blood volume and flow, etc., characterizing the functional physiological and/or biochemical status of tissue in vivo. We consider two methodologies for fitting radiotracer models on a pixel-wise basis to PET data. The first method does parameter optimization for each pixel considered as a separate region of interest. The second method also does pixel-wise analysis but incorporates an additive mixture representation to account for heterogeneity effects induced by instrumental and biological blurring. Several numerical and statistical techniques including cluster analysis, constrained nonlinear optimization, subsampling, and spatial filtering are used to implement the methods. A computer simulation experiment, modeling a standard [F-18] deoxyglucose imaging protocol using the UW-PET scanner, is conducted to evaluate the statistical performance of the parametric images obtained by the two methods. The results obtained by mixture analysis are found to have substantially improved mean square error performance characteristics. The total compute time for mixture analysis is on the order of 0.7 seconds per pixel on a 16 MIPS workstation. This results in a total compute time of about 1 hour per slice for a typical FDG brain study.