Prostate imaging at ultra-high fields is heavily affected by B1 field induced inhomogeneities. This not only results in unattractive images but it also might affect clinical diagnosis . To remedy this we developed a deep learning model that retrospectively corrects for the bias field. We applied this model to a clinical data set and demonstrated its performance in a qualitative manner. The results indicate that the model is able to drastically reduce the inhomogeneities in a variety of cases while the tissue contrast is generally maintained and the underlying anatomy has been successfully recovered.