In this work, a multistatic uniform diffraction tomography (MUDT) method, that was proposed by the authors as a new qualitative imaging method just recently, is combined with the quantitative Bayesian inversion framework. In this combined approach, MUDT is applied to find the location of the moisture and this localization is employed as a pre-knowledge for the Bayesian framework to estimate the moisture levels in a polymer foam. The proposed combined algorithm might become a major part of the development of a new kind of intelligent industrial microwave drying systems. The imaging algorithm is tested with simulated measurement data. The frequency band from 8 GHz to 12 GHz (X-band) is used for the MUDT algorithm whereas a single frequency of 8.2 GHz is assumed for the Bayesian framework. The first results demonstrate the ability of the developed combined algorithm for optimizing the computational load unlike seen in the quantitative inversion approaches.