###### Abstract
The emergence of unsupervised methods can alleviate the lack of datasets that are labeled by experts. For unsupervised registration, the appropriate image similarity metrics need to be selected as the optimization objective. By training the DIRNet with a similarity metric, [PERSON] et al. [7] achieved the same precise performance as the traditional deformable image registration methods. [PERSON] et al. [8] designed a CNN-based DIR approach called VoxelMorph for the MRI brain datasets, which achieved rapid registration. However, these methods are mainly utilized for unimodal registration. The intensity distribution of the different modal images is uncertain and complex, making it difficult to use these methods directly for multimodal registration. The CNN was initially used to learn the mapping relationship for unsupervised registration. By using a pretrained CNN to directly extract features from a pair of images, [PERSON] et al. [9] achieved zero-shot registration on the brain MRI dataset. [PERSON] et al. [10] introduced the regularization term to constrain the anatomical structure deformation for PET-CT registration, in which the registration performance was improved. However, it is challenging for a CNN to map the features of the multimodal image to spatial relationships, as different modalities have significant gaps in appearance. Subsequently, several deformable image registration (DIR) frameworks based on the GAN are proposed, which show promising performances by providing additional regularization. [PERSON] et al. [11] presented the AIR framework, which utilized adversarial networks to distinguish good registration from poor registration. By connecting a registration network and a discrimination network, [PERSON] et al. [12] created a general registration framework that can achieve monomodal and multimodal image registration. [PERSON] et al. [13; 14] designed a cGAN-based [15] model that directly generated the warped image with the deformation field. Compared with the CNN, the GAN has an adversarial training process that makes the DVF more precise. Nonetheless, GANs also have some drawbacks, such as the long duration required for training and vanishing gradients. Another major use of the GAN is to bridge the enormous appearance gap of different modalities in translation-based multimodal registration. By employing the enhanced CycleGAN [16] to transform a CT image into an MRI-like image, [PERSON] et al. [17] estimated the final deformation field in a dual-stream method and achieved a better registration accuracy. However, due to the lack of structural consistency across views, the cycle-consistent loss may generate deformed images, which is unfavorable for the subsequent registration performance. In this paper, we design an unsupervised medical image registration method, DTR-GAN. The main idea is to simplify the complex multimodal image registration (images with an inconsistent grayscale relationship) to unimodal image registration (images with a consistent grayscale relationship) through the image-to-image translation algorithm. The main works are summarized below:  
(1) We design an unsupervised image registration framework, DTR-GAN, via a bidirectional image-to-image translation network for MRI-CT registration. By using complementary anatomical structure information from CT and MRI simultaneously, DTR-GAN can overcome the grayscale differences in multimodal images and achieve end-to-end registration. (2) We propose a multiscale encoder-decoder network to minimize loss of image detail during the image translation process, which can aggregate low-level and high-level features and ensure the generated coherent anatomical feature in the translation network. (3) We design a mixed similarity loss to penalize the discrepancy in appearance between the target image and the warped image. By extracting the features common to both the source and target images in the deep feature space, DTR-GAN can avoid inconsistent structural deformations during training and enhance registration accuracy.