The rapid progress in image-to-image translation methods using deep neural networks has led to advancements in the generation of synthetic CT (sCT) in MR-only radiotherapy workflow. Replacement of CT with MR reduces unnecessary radiation exposure, financial cost and enables more accurate delineation of organs at risk. Previous generative adversarial networks (GANs) have been oriented towards MR to sCT generation. In this work, we have implemented multiple augmented cycle consistent GANs. The augmentation involves structural information constraint (StructCGAN), optical flow consistency constraint (FlowCGAN) and the combination of both the conditions (SFCGAN). The networks were trained and tested on a publicly available Gold Atlas project dataset, consisting of T2-weighted MR and CT volumes of 19 subjects from 3 different sites. The network was tested on 8 volumes acquired from the third site with a different scanner to assess the generalizability of the network on multicenter data. The results indicate that all the networks are robust to scanner variations. The best model, SFCGAN achieved an average ME of 0.9 5.9 HU, an average MAE of 40.4 4.7 HU and 57.2 1.4 dB PSNR outperforming previous research works. Moreover, the optical flow constraint between consecutive frames preserves the consistency across all views compared to 2D image-to-image translation methods. SFCGAN exploits the features of both StructCGAN and FlowCGAN by delivering structurally robust and 3D consistent sCT images. The research work serves as a benchmark for further research in MR-only radiotherapy.