A Review of Generative Adversarial Networks Algorithms For Ultrasound Image Denoising

Speckle noise is a pervasive issue in ultrasound imaging, often leading to the degradation of image quality and hindering accurate interpretation. This challenge is particularly significant for novice sonographers, who may struggle with misdiagnoses due to the compromised clarity of images. Recent advancements in Generative Adversarial Networks (GANs) have shown promise in addressing this issue by effectively reducing speckle noise and enhancing image quality. This review paper explores the application of GANs in ultrasound imaging, focusing on their potential to improve the diagnostic accuracy of sonographers, especially those in training. By analyzing various GAN architectures and their performance in denoising tasks, we highlight the effectiveness of these models in producing clearer, more interpretable images. The review also examines the implications of improved image quality for the training of sonographers, emphasizing how enhanced visual data can accelerate skill development and boost diagnostic confidence among inexperienced practitioners. The findings suggest that integrating of GAN-based denoising techniques into ultrasound imaging workflows could play a critical role in advancing sonography education and improving healthcare outcomes in resource-limited settings.