Kinetic parameter estimation at the individual voxel level has the powerful ability to represent both spatial distributions and quantitative physiological parameters of interest. In practice, parametric images are commonly quantified by computing mean values for specified ROIs. Nonetheless, the mean operator vastly oversimplifies the available spatial uptake information. It may be hypothesized that a given tracer will exhibit increasingly differential or heterogeneous uptake due to disease: subsequently, we have implemented and explored a comprehensive texture and shape analysis framework wherein extensive information is generated from parametric PET images, through: (1) 3D moment invariants analysis, (2) intensity histogram analysis, (3) gray-level spatial-dependence (GLSD) analysis, and (4) neighborhood gray tone difference (NGTD) analysis. In the present work, we applied this approach to imaging of 11C-DPA-713, a novel PET ligand with high binding to the translocator protein (TSPO), a marker of neuroinflammation. In particular, for tracers such as DPA with relatively wide-spread uptake, where a reliable reference tissue does not exist, quantification of heterogeneity may provide additional valuable information. Our center has been the first to perform DPA PET studies in humans, and is gathering a large collection of PET studies; e.g. subjects with systemic lupus erythematosus (SLE), traumatic brain injury (TBI; former NFL players), along with young and elderly controls. Our preliminary analysis has revealed that, compared to conventional mean ROI analysis, a number of metrics in the proposed framework yield enhanced discrimination (as measured using AUC) between patients vs. controls in a range of ROIs, in TBI as well as SLE vs. controls, consistent with increased heterogeneity of uptake with disease, though in the case of SLE this can be, at least partly, attributed to changes in ROI shapes. Overall, the proposed framework has the potential to bring about a new quantification paradigm in parametric imaging.