A Review of GAN-Synthesized Brain MR Image Applications

Abstract

Recent advancements in brain imaging technology have led to a rise in the use of magnetic resonance imaging (MRI) for clinical diagnosis. Deep learning (DL) techniques have emerged as a valuable tool for automatically detecting abnormalities in brain images without manual intervention. Meanwhile, generative adversarial networks (GANs) have shown promise in generating synthetic brain images for a variety of applications, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. This chapter conducts a comprehensive review of the literature on the use of GAN-synthesized images for diagnosing brain diseases, drawing on data from studies in the Web of Science and Scopus databases from the past decade. The review examines the various loss functions and software tools used in processing brain MRI images, as well as provides a comparative analysis of evaluation metrics for GAN-synthesized images to assist researchers in selecting the most appropriate metric for their specific needs.