  Magnetic Resonance (MR) Imaging and Computed Tomography (CT) are the primary
diagnostic imaging modalities quite frequently used for surgical planning and
analysis. A general problem with medical imaging is that the acquisition
process is quite expensive and time-consuming. Deep learning techniques like
generative adversarial networks (GANs) can help us to leverage the possibility
of an image to image translation between multiple imaging modalities, which in
turn helps in saving time and cost. These techniques will help to conduct
surgical planning under CT with the feedback of MRI information. While previous
studies have shown paired and unpaired image synthesis from MR to CT, image
synthesis from CT to MR still remains a challenge, since it involves the
addition of extra tissue information. In this manuscript, we have implemented
two different variations of Generative Adversarial Networks exploiting the
cycling consistency and structural similarity between both CT and MR image
modalities on a pelvis dataset, thus facilitating a bidirectional exchange of
content and style between these image modalities. The proposed GANs translate
the input medical images by different mechanisms, and hence generated images
not only appears realistic but also performs well across various comparison
metrics, and these images have also been cross verified with a radiologist. The
radiologist verification has shown that slight variations in generated MR and
CT images may not be exactly the same as their true counterpart but it can be
used for medical purposes.
