# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
###### Abstract  
Currently, an increasing number of macaque brain MRI datasets are being made publicly accessible. Unlike human, publicly accessible macaque brain datasets suffer from data quality in diffusion magnetic resonance imaging (dMRI) data. Typically, dMRI data require a minimum ratio of 1:10 between low b-value (b \(<\) 10) volumes and high b-value (b \(>\) 300) volumes. However, the currently accessible macaque datasets do not meet this ratio. Due to site differences in macaque brain images, traditional human brain image-to-image translation models struggle to perform well on macaque brain images. Our work introduces a novel end-to-end primary-auxiliary dual generative adversarial network (PadGAN) for generating low b-value images. The auxiliary generator in the PadGAN is responsible for extracting the latent space features from peak information maps and transmitting them to the primary generator, enabling the primary generator to generate images with rich details. Experimental results demonstrate that PadGAN outperforms existing methods both qualitatively and quantitatively (mean SSIM increased by 0.1139). Diffusion probabilistic tractography using dMRI data augmented by our method yields superior results. medical image-to-image translation; generative adversarial networks; dMRI data augmentation; macaque brain image +
Footnote †: journal: Medical  
due to the use of early acquisition protocol configurations [6]. The reliability of computed results, such as DTI estimation, from analyses using data that do not meet the required ratio needs further confirmation. Therefore, it is necessary to generate and optimize low b-value volumes in macaque brain dMRI data. Medical image-to-image translation refers to the method of translation images from an input image modality to an output modality through a mapping relationship. This approach can be used to acquire additional data or complete missing data [7], and it can be applied to downstream tasks in medical image processing, such as image registration and segmentation [8; 9], as well as image classification [10]. Generative adversarial networks (GANs) are network models based on game theory, consisting of a generator and a discriminator [11]. The generator attempts to generate high-quality images to deceive the discriminator, while the discriminator distinguishes between real and generated images. Both sides become stronger in the adversarial process, resulting in the generator producing increasingly realistic images. With the emergence of GANs, the performance of medical image-to-image translation has been greatly improved. Initially, GANs were only used to generate images from random noise. With researchers attempting to use Transformer as the generator of GANs [12], Transformer is being applied in the field of medical image-to-image translation. The advent of pix2 pix and CycleGAN propelled the performance of GANs in image-to-image translation tasks [13; 14]. New methods harness the powerful generative capabilities of GANs to produce visually and objectively superior images. Some researchers have explored the application scenarios of CycleGAN in medical image-to-image translation [15; 16; 17], but more efforts have been devoted to improving CycleGAN for better application in unsupervised learning settings [18; 19; 20; 21; 22; 23]. Methods based on CycleGAN are unsupervised approaches, with the advantage of being able to perform mutual translation between two domains without requiring paired images. However, because CycleGAN serves two image translation tasks, its performance on the generation task in a single target domain is generally inferior compared with supervised methods. Compared with CycleGAN, methods based on Transformer are a supervised learning approach. Some researchers have employed Transformer for medical segmentation [24], MRI reconstruction [25], and medical image-to-image translation [8; 26; 27].