This study was undertaken to estimate metabolic tissue properties from dynamic breast F-18-FDG PET/CT image series and to display them as 3D parametric images. Each temporal PET series was obtained immediately after injection of 10 mCi of F-18-FDG and consisted of fifty 1- minute frames. Each consecutive frame was nonrigidly registered to the first frame using a finite element method (FEM) based model and fiducial skin markers. Nonlinear curve fitting of activity vs. time based on a realistic two-compartment model was performed for each voxel of the volume. Curve fitting was accomplished by application of the Levenburg-Marquardt algorithm (LMA) that minimized X2. We evaluated which parameters are most suitable to determine the spatial extent and malignancy in suspicious lesions. In addition, Patlak modeling was applied to the data. A mixture model was constructed and provided a classification system for the breast tissue. It produced unbiased estimation of the spatial extent of the lesions. We conclude that nonrigid registration followed by voxel-by-voxel based nonlinear fitting to a realistic two-compartment model yields better quality parametric images, as compared to unprocessed dynamic breast PET time series. By comparison with the mixture model, we established that the total cumulated activity and maximum activity parametric images provide the best delineation of suspicious breast tissue lesions and hyperactive subregions within the lesion that cannot be discerned in unprocessed images.