A deep convolutional network for cranial pseudo-CT generation was developed with the consideration of prior knowledge involved in radiotherapetic imaging workflow. This prior knowledge has been scarcely studied along with deep learning. It could greatly reduce the complexity of image data handled by the network. Examined on 14 sets of DIXON-MR and CT images, the proposed model achieved low generalization gap and offered accurate results regardless of the amount of training data. It achieved an average of 89.77±29.32HU mean-absolute-difference in two-fold cross-validation. It is experimentally shown that the proposed method is well-suited for generating clinical pseudo-CT for radiotherapeutic applications.