###### Abstract  
Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical images. Since images with different modalities exhibit different features, it remains a challenge to find their exact correspondence. Most of the current methods based on image-to-image translation cannot fully leverage the available information, which will affect the subsequent registration performance. To solve the problem, we develop an unsupervised multimodal image registration method named DTR-GAN. Firstly, we design a multimodal registration framework via a bidirectional translation network to transform the multimodal image registration into a unimodal registration, which can effectively use the complementary information of different modalities. Then, to enhance the quality of the transformed images in the translation network, we design a multiscale encoder-decoder network that effectively captures both local and global features in images. Finally, we propose a mixed similarity loss to encourage the warped image to be closer to the target image in deep features. We extensively evaluate methods for MRI-CT image registration tasks of the abdominal cavity with advanced unsupervised multimodal image registration approaches. The results indicate that DTR-GAN obtains a competitive performance compared to other methods in MRI-CT registration. Compared with DFR, DTR-GAN has not only obtained performance improvements of 2.35% and 2.08% in the dice similarity coefficient (DSC) of MRI-CT registration and CT-MRI registration on the Learn2 Reg dataset but has also decreased the average symmetric surface distance (ASD) by 0.33 mm and 0.12 mm on the Learn2 Reg dataset. multimodal image registration; image-to-image translation; unsupervised; deep learning 4.0.0000-0001-990-0001-990-0001-9900-9001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-99001-9001-99001-99001-99001-99001-990 Compared with unimodal registration, the main challenge for multimodal image registration is the large differences in grayscale and texture between the different modalities, as well as the difficulty in finding the optimal transformation parameters. As abdominal images contain deformable organs and tissue caused by respiration and other physiological processes, abdomen registration is a challenging problem. The multimodal registration of the abdomen requires consideration of deformation, organ shape variation, and intensity discrepancy between imaging modes. For example, in the case of the liver, the grayscale values of CT and MRI images differ significantly, making it difficult to measure the similarity between the two modalities. Due to the presence of lower tissue contrast in CT images and the lack of obvious structured information in MRI, the image synthesis model from the CT to MRI modality has stronger nonlinearity. With the development of deep learning-based methods, significant results have been obtained in medical image registration. Deep learning-based methods are mainly classified as supervised and unsupervised. Supervised registration requires the ground truth to guide more promising registration, whereby the dissimilarity between the deformation field and the ground truth is minimized during training. [PERSON] et al. [5] proposed DVNet for CT-US registration, which used a patch-based CNN approach to estimate displacement vectors (DVs), but it had not been demonstrated on clinical CT and US data. [PERSON] et al. [6] proposed BIRNet to achieve dual supervision registration, which used a dual-guided fully CNN network and employed gap filling to achieve promising registration. By enhancing the effectiveness of the label, the performance of the supervised method has been improved. However, supervised registration demands a great deal of accurately labeled data that are time-consuming and difficult to acquire.