Super-resolution of magnetic resonance images using Generative Adversarial Networks.

Abstract

Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less measurements yields the potential to relax these inherent forfeits. Recent breakthroughs in the field of Machine Learning have shown high-resolution (HR) images could be recovered from low-resolution (LR) signals via super-resolution (SR). In particular, a novel class of neural networks named Generative Adversarial Networks (GAN) has manifested an alternative way of conceiving models capable of generating data. GANs can learn to infer details based on some prior information, subsequently recovering missing data. Accordingly, they manifest huge potential in MRI reconstruction and acceleration tasks. This paper conducts a review on GAN-based SR methods, exhibiting the immersive ability of GANs on upscaling MRIs by a scale factor of ×4 while at the same time maintaining trustworthy and high-frequency details. Despite quantitative results suggesting SRResCycGAN outperforms other popular deep learning methods in recovering ×4 downgraded images, qualitative results show Beby-GAN holds the best perceptual quality and proves GAN-based methods hold the capacity to reduce medical costs, distress patients and even enable new MRI applications where it is currently too slow or expensive.