Guangyuan Li1, Chengyan Wang2, Weibo Chen3, and Jun Lyu1
1School of Computer and Control Engineering, Yantai University, Yantai, China, 2Human Phenome Institute, Fudan University, Shanghai, China, 3Philips Healthcare, Shanghai, China
1School of Computer and Control Engineering, Yantai University, Yantai, China, 2Human Phenome Institute, Fudan University, Shanghai, China, 3Philips Healthcare, Shanghai, China
In order to solve the reconstruction effect of CS-MRI under highly under-sampling，we proposed a modified GAN architecture for accelerating CS-MRI reconstruction, namely RSCA-GAN，and added spatial and channel-wise attention in Generative Adversarial Networks.
Fig.1.(a) Framework of the proposed method.The generator of the network is connected with two residual autoencoder U-net. The discriminator is composed of 6 layers. (b) Composition of KS-Block.
Fig.2.The architecture of Residual SCAU-Net.The encode block is indicated by green, and the decode block is indicated by blue. The 4D tensor is used as input, and using the 2D convolution with filter_size of 3x3 and Stride of 2. The number of feature maps is defined as feature_num=64. The residual block is represented by orange, which is used to increase the depth of the network. SCA Block is indicated by yellow composed of spatial attention and channel attention.