Accurate error estimates are beneficial for many applications of emission tomography, e.g. kinetic modelling or SUV quantification with confidence levels. Due to the variety of parameters influencing the noise properties of PET images, the use of a single error model for all imaging situations and data processing set-ups leads to inaccurate error estimates. The present invention circumvents this problem by providing a database that includes a plurality of pre-determined noise models for different imaging situations. The most appropriate noise model can then be selected manually or automatically depending on the given imaging situation. Hence, the time-consuming procedure of extracting correct noise models, e.g. by utilizing a bootstrap method or by analysing repeated measurements, needs to be performed only once for each model and can be done by the vendor of the acquisition system, so that the clinician can instantly access the optimized error models from the database.