## 4 Results
### Results on the Learn2 Reg and CHAOS Datasets  
To validate the performance of DTR-GAN, we compare it with Affine [37], VoxelMorph [8], CR-GAN, SbR [25], RoT [23], IMSE [28], and DFR [26] on the Learn2 Reg and CHAOS datasets. Affine represents the registration framework using the Elastix toolbox. VoxelMorph is a CNN-based registration network. CR-GAN is a VoxelMorph registration network based on the CycleGAN translation network with PatchNCE loss. SbR is a 2D synthesis-based registration network using contrastive learning to enforce geometric consistency. RoT is a multimodal registration network based on the geometric-preserving translation network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Modality** & **Size** & **Train/Validation/Test** & **Resize** \\ \hline Learn2 Reg & CT, MRI & 192 \(\times\)160 & 10/2/4 & ✓ \\ CHAOS & CT, MRI & -* & 14/2/4 & ✓ \\ \hline \hline \end{tabular}  
* - represents that there are many different sizes and we cannot list them all.  
\end{table} 
Table 1: A concise description of the two datasets utilized in our method. Figure 4: Instances from the Learn2 Reg and CHAOS datasets. IMSE is a self-supervised multimodal image registration network with a multimodal spatial evaluator to evaluate spatial differences. DFR is the translation-based multimodal registration network that removes the discriminator in translation work. **Results on the Lean2 Reg dataset. ** Table 2 illustrates average DSC scores and ASD scores on the Lean2 Reg dataset. Compared with DFR, the DSC scores of DTR-GAN are improved by 2.35% and 2.08% in MRI-CT registration and CT-MRI registration, respectively. In addition, compared with DFR, the ASD values of DTR-GAN are decreased by 0.33 mm and 0.12 mm, respectively. In addition, among the above methods, DTR-GAN achieves the best results for the DSC and ASD. Bidirectional translation networks TP and TR generate promising translated images in DTR-GAN, thereby improving the registration performance. Specifically, MFF aggregates multiscale information in the translation network DT-GAN, allowing DTR-GAN to better capture local and global features in images. By designing a mixed similarity loss, the anatomical details in the two modalities can be aligned well. Figure 5 shows the registration results of some methods on the Learn2 Reg dataset. Figure 5A,E show warped images, Figure 5B,F show deformation fields, and Figure 5C,D,G,H show checkerboard grids and overlapping images, respectively, which are visualizations of image alignment effects. Figure 5A,E show the warped images in DTR-GAN are closer to the target image. The first in column (A) denotes that the cycle-consistency loss would lead to shape distortion in CR-GAN. We can observe that DTR-GAN with similarity loss is more effective for appearance preservation in image registration. Figure 5D,H show the effects of overlaying the target image with the warped image, in which the target image is set to blue, the warped image is set to red, and the two images are superimposed together in purple. The purple in the image is more evident, indicating that the warped image and target image are better aligned. As shown in Figure 5, DTR-GAN shows an optimum performance. **Results on the CHAOS dataset. ** Table 3 shows the experimental results on the CHAOS dataset. Compared with DFR, the DSC scores of DTR-GAN increased by 1.98% and 1.51% on average in MRI-CT registration and CT-MRI registration, respectively. In addition, the ASD scores of DTR-GAN are decreased by 0.28 mm and 0.26 mm on average. Figure 6 illustrates the registration results of different methods. Figure 6A,E show warped images, Figure 6B,F show deformation fields, and Figure 6C,D,G,H depict checkerboard grids and overlapping images, respectively, which are visualizations of image alignment effects. As shown in Figure 6, we can observe that the warped image in DTR-GAN is closer to the target image. Figure 6C,G show that the alignment of DTR-GAN is more optimum than the other approaches.