Cross-modality synthesis of medical images involves learning a robust latent representation that captures shared information across different imaging modalities. This task is complex due to the differences between modalities, which requires a model capable of understanding and transferring information across these domains. For instance, variations in magnetic field strength, coil configurations, and acquisition protocols can result in significant differences in image quality, resolution, and signal characteristics. To address this challenge, a novel cross-modality conditional generative model is proposed, which facilitates the synthesis of new modalities from existing ones. The model enforces a shared latent space by introducing priors designed to maximize the expected log-density in relation to the cross-modal variational representation.