x4 Super-Resolution Analysis of Magnetic Resonance Imaging based on Generative Adversarial Network without Supervised Images

Magnetic resonance imaging (MRI) is widely used in clinical medical auxiliary diagnosis. In acquiring images by MRI machines, patients usually need to be exposed to harmful radiation. The radiation dose can be reduced by reducing the resolution of MRI images. This paper analyzes the super-resolution of low-resolution MRI images based on a deep learning algorithm to ensure the pixel quality of the MRI image required for medical diagnosis. It then reconstructs high-resolution MRI images as an alternative method to reduce radiation dose. This paper studies how to improve the resolution of low-dose MRI by 4 times through super-resolution analysis based on deep learning technology without other available information. This paper constructs a data set close to the natural low-high resolution image pair through degenerate kernel estimation and noise injection and constructs a two-layer generated countermeasure network based on the design ideas of ESRGAN, PatchGAN, and VGG-19. The test shows that our method is better than EDSR, RCAN, and ESRGAN in comparing non-reference image quality evaluation indexes.