The generation of parametric maps -i.e. maps that associate to each pixel of the image its kinetic parameters- is a crucial issue in Positron Emission Tomography. Unfortunately, the quality of such maps is sometimes poor, due to the high noise of data at pixel level. In this talk, different types of Bayesian methods, which improve the quality and reliability of parametric maps, will be described and their potentialities showed both on simulated and real data sets.