Imaging radiotracer model parameters in PET: a mixture analysis approach

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Abstract
Two methodologies for fitting radiotracer models on a pixel-wise basis to PET data are considered. 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 (FDG) 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 computation time for mixture analysis is on the order of 0.7 s/pixel on a 16 MIPS workstation. This results in a total computation time of about 1 h per slice for a typical FDG brain study.