# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
## 3 Results  
### Comparison Experiments and Results  
The method proposed in this paper is compared with five existing methods that have shown good performance in the field of medical image-to-image translation research. Specifically:  
1. Pix2 pix [13] network adopts the U-Net architecture as the main framework of the generator.
2. CycleGAN [14] network shares the same generator architecture as pix2 pix, but it involves two generators and two discriminators for cyclic generation tasks.
3. SwinUnet [24] utilizes the Swin Transformer as the main framework for medical image segmentation tasks, adapted for application in this paper.
4. ResViT [26] builds upon the Vision Transformer architecture as the main generator framework.
5. pGAN [30] adopts ResNet as the main framework.  
For the comparative experiments, the original models' architectures and training parameters are used during the training process. All models are pre-trained for 20 epochs and trained for an additional 80 epochs on an NVIDIA GeForce RTX 3090. Structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mutual information (MI) are selected as quantitative evaluation metrics in this paper.  
Table 3 lists the comprehensive results of the AMU, Mount Sinai-P, Mount Sinai-S, UCDavis, and UWM sites, each containing non-brain tissue. To compare the results with only brain tissue, the non-brain tissue is removed from all results, as shown in Table 4,  
Figure 4: The overall processing flow of dMRI images augmentation. The figure provides a detailed description of the steps outlined in Section 2.4. During testing, the auxiliary generator no longer outputs results, as there is no need to further optimize the latent space through backpropagation.  
which displays the results after excluding non-brain tissue for the five sites. The overall results are consistent with Table 1, but there is a slight decrease. Subsequent experiments show results after excluding non-brain tissue. The specific results for the five datasets are shown in Table 5, and Figures 5 and 6. The CycleGAN method produces results closer to the source images on most datasets. Although this method employs a dual-generator and dual-discriminator structure, with each generative adversarial network serving separate tasks for generating target and source images, it is suitable for scenarios where paired images are not required in both domains. In contrast, both generative adversarial networks in our method are dedicated to generating low b-value images, resulting in better visual observations and evaluation metrics. The pGAN method fails to generate detail-rich images, as it uses ResNet as the basic generator architecture with a deeper network structure, but lacks the capability to retain encoder feature map information like U-Net. Our method utilizes the advantages of the U-Net architecture to capture features from different layers, thereby preserving detailed image information. Transformer-based ResViT and SwinUnet methods exhibit relatively generic performance due to the differences in global information from different sites in the macaque brain image dataset and the limited data samples. In contrast, our method, a fully convolutional neural network, maximizes the local generation capabilities of convolutional neural networks. The Pix2 pix method, a single generator adversarial network based on the U-Net generator architecture, performs well in generating global structural features but lacks detailed features. Our method addresses this limitation by using the auxiliary generative adversarial network to provide hidden space containing more detailed features, thus compensating for the shortcomings of the single generator adversarial network in capturing detailed features.