A review on generative based methods for MRI reconstruction

Abstract Magnetic resonance imaging (MRI) is one of the most important methods for clinical diagnosis. However, the main drawback of MRI is the long imaging time, which will cause the moving artifact by patient movements. With the rapid development of the computing power of computer, deep learning is widely used in computer vision, natural language processing, visual recognition and so on. Meanwhile, a large number of reconstruction methods based on deep learning have also emerged. Recently, many generative models have been proposed to solve the perception quality problem that existed in fast MRI images. In this paper, we manage to survey the motivations and reconstruction strategies of generative-based methods published in journals and conferences over the past five years. First, the background and theoretical basis of MRI reconstruction are introduced. Secondly, the application of generative-based methods in MRI reconstruction field is comprehensively summarized and analyzed, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE) and VAE-GAN. Then the advantages and disadvantages of the existing generative-based MRI reconstruction methods are discussed. Finally, several publicly available MR image datasets and evaluation metrics are presented, which can provide a reference for researchers and practitioners working in related domains. The conclusions and challenges are also given.