# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
###### Abstract
However, Transformer requires a large amount of data, but publicly accessible samples of macaque dMRI data are limited, making it challenging to fully leverage the advantages of Transformer [28]. The pix2 pix-based method is also a widely used supervised learning approach for medical image-to-image translation. The Synb0-DisCo method applies the pix2 pix technique to correct distorted b0 images [29]. pGAN and Ea-GAN, respectively, enhance the image detail capability by improving the loss function and considering edge information [30; 31]. MedGAN [32] employs a cascaded U-Net as its generator for various medical image translation tasks. As pix2 pix-based methods are designed for the generation task of a single target domain with paired image data, they often exhibit higher generation accuracy in medical image-to-image translation tasks. However, since such methods typically rely on a single generative adversarial network, they lack in detail learning. Furthermore, all these methods share a common issue. Currently, most studies on modality translation of brain MRI images are based on human brain GRAY color space, with the aim of providing visually interpretable images [21; 26; 31; 33]. Medical imaging signal intensity values have absolute significance [8], and are required for probabilistic tractography calculations, rather than the typical GRAY color range of bitmap images. Therefore, the images generated by the aforementioned methods cannot meet the requirements of computational neuroscience research. In this work, we introduce the concept of peak information maps and propose a novel end-to-end primary-auxiliary dual GAN network (PadGAN), which can extract latent space features from peak information maps to translate high-quality low b-value images. The generated low b-value images can be used for augmenting dMRI image data, improving the quality of dMRI images. The results show that PadGAN outperforms existing methodsin qualitative observations and quantitative metrics, and the effectiveness of each module is validated through ablation experiments. Finally, we use the Xtract toolbox [34] in FSL6.0 (FMRIB Software Library) tools [35] to perform probabilistic tractography and use FSL tools to conduct DTI estimation on dMRI data augmented. The Xtract calculation results of dMRI data augmented using our method are more satisfactory. In summary, the specific contributions of this paper are as follows:  
1. We introduce the concept of peak information maps and design a corresponding method for calculating peak information maps. 2. We propose a novel end-to-end primary-auxiliary dual GAN network to translate high b-value images to low b-value images. In this network, the auxiliary generator extracts latent space features from peak information maps and transfers these features to the primary generator. The primary network integrates the latent space features and multi-scale features to generate low b-value images. 3. Through DTI estimation and Xtract probabilistic tractography experiments, we validate the effectiveness of generating low b-value images for augmenting original dMRI data, providing new validation approaches for quality assessment in brain science research and offering optimized dMRI data for brain science studies.