# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
## 4 Discussion  
In this work, we propose a method for dMRI brain image data augmentation using PadGAN to generate low b-value images. The introduction of peak information maps creates end-to-end conditions for extracting latent space features, allowing the auxiliary network to obtain latent space features through adversarial learning. On the basis of the U-Net network, a feature fusion module is added to the primary generator to merge latent space features and multi-scale information, thus generating images with rich details. Additionally, various generative adversarial network models are explored, and the strengths and weaknesses of each model are analyzed. PadGAN is creatively proposed and compared with comparative models in qualitative, quantitative, Xtract probabilistic tractography and DTI estimation to demonstrate its overall performance. Finally, ablation experiments are conducted on each module of PadGAN to demonstrate the importance of each part. Both generators in PadGAN adopt the encoder-decoder architecture based on U-Net, preserving multi-scale information through skip connections, and the introduction of latent space features enables PadGAN to learn fine-grained image features. As shown in Figures 5 and 6, unlike previous studies on human brain datasets where Transformer-based network models yield poor results, typically due to the large volume of data in human brain datasets resulting in different model parameters for each dataset, our approach uses a unified training strategy for the limited datasets of macaque brain images from each site. For datasets collected from each site, there are significant differences in acquisition parameters. Therefore, attention mechanisms are difficult to perform effectively for multi-site datasets. While ResNet can maintain model learning capability, even with deep network layers, it does not preserve multi-scale features like U-Net, resulting in deficiencies in detail generation. The pix2 pix method based on U-Net demonstrates good performance, but, as a single generator and discriminator method, it still lacks in generating image details. Although CycleGAN has two generative adversarial networks, these networks are tasked with mutual conversion between two modal data samples and do not leverage both networks to generate images in one target domain. The auxiliary network in PadGAN provides latent space information to the primary network to enhance the detail generation of the generated images, utilizing U-Net's skip connections to preserve multi-scale information, resulting in superior performance in image details. Unlike the typical computer vision image-to-image translation domain, the signal intensity values of MRI images have absolute significance and can be used for DTI estimations or neuroimaging studies. Common images in daily life are usually RGB images with a maximum pixel intensity value of 255, while the signal intensity value range of macaque brain images is typically in the range of thousands to tens of thousands. Therefore, when evaluating the quality of MRI image generation, we can go beyond quantitative metrics and qualitative observations. For medical MRI, some researchers conduct Turing tests with expert radiologists to assess the authenticity of generated images [32]. For macaque and human brain images used in research, we can further evaluate the quality of generated images by calculating neural tracing or DTI estimation results, which presents a novel validation approach. In future work, we can explore the generation of realistic images using multi-modal data. Although the macaque brain imaging dataset is limited, with few data within each site, many sites have at least two modalities of data. Utilizing network models that can effectively leverage multi-modal information may lead to the generation of higher quality images. Additionally, our method also has the potential for application in human brain imaging. Firstly, our method can be used for data augmentation of human brain dMRI images.