Search

The quality of MRI will affect doctor's judgment on patient's physical conditions,and the high-resolution MRI is more conducive to doctor to make an accurate diagnosis.Using computer technology to perform super-resolution reconstruction of MRI can obtain high-resolution MRI from existing low-resolution MRI.Based on the strong generation ability of the generative adversarial networks and the unsupervised learning characteristics of the generative adversarial networks,this paper studies the MRI super-resolution algorithm based on the generative adversarial networks.It designs a generative adversarial network model DNGAN that combines ResNet structure and DenseNet structure.In this network,the WGAN-GP theory is used as the adversarial loss to stabilize the training of the generative adversarial networks.In addition,the content loss function and the perceptual loss function are also used as the loss function of the network.At the same time,in order to make better use of the rich frequency domain information of MRI,the frequency domain information of MRI is added to the network as a frequency domain loss function.In order to prove the effectiveness of DNGAN,the MRI super-resolution experimental results of DNGAN are compared with that of SRGAN and bicubic interpolation method.Experimental results show that DNGAN model can effectively perform super-resolution reconstruction of MRI.