Bayesian methods are commonly used to regularise Maximum Likelihood (ML) based Emission Tomography reconstruction algorithms. The prior models employed are either dependent upon some assumed form of activity distribution, on an assumed magnitude of the activity across well defined regions of interest, or both. Assuming approximate levels of the activity levels requires firstly a delineation of the tissued regions, and secondly sensible starting estimates for the level of activity present in each of these compartments. Errors in either of these steps yields an incorrect prior model and a reconstruction solution that reflects this. In order to compensate for this possible weakness in the Bayesian reconstruction process, the following work proposes developing priors based on the emission data itself, constrained according to a more realistic forward model of the activity distribution. The influence of inaccuracies in the prior estimation of compartment activity levels is subsequently removed, the so-called resolution mis-match is avoided, and uncertainties in the segmentation are compensated for. As comparative tests described in this short paper show, bias toward the prior - an oft cited criticism of Bayesian methods - may yet be warranted.