  In clinical practice, well-aligned multi-modal images, such as Magnetic
Resonance (MR) and Computed Tomography (CT), together can provide complementary
information for image-guided therapies. Multi-modal image registration is
essential for the accurate alignment of these multi-modal images. However, it
remains a very challenging task due to complicated and unknown spatial
correspondence between different modalities. In this paper, we propose a novel
translation-based unsupervised deformable image registration approach to
convert the multi-modal registration problem to a mono-modal one. Specifically,
our approach incorporates a discriminator-free translation network to
facilitate the training of the registration network and a patchwise contrastive
loss to encourage the translation network to preserve object shapes.
Furthermore, we propose to replace an adversarial loss, that is widely used in
previous multi-modal image registration methods, with a pixel loss in order to
integrate the output of translation into the target modality. This leads to an
unsupervised method requiring no ground-truth deformation or pairs of aligned
images for training. We evaluate four variants of our approach on the public
Learn2Reg 2021 datasets \cite{hering2021learn2reg}. The experimental results
demonstrate that the proposed architecture achieves state-of-the-art
performance. Our code is available at https://github.com/heyblackC/DFMIR.
