Adaptive Enhanced Generative Adversarial Network for MRI Reconstruction

Extracting effective feature information is very important for deep learning methods. However, most deep learning-based CS-MRI methods use the standard convolutional layers with square kernels for feature extraction, which is difficult to further improve the accuracy of MRI reconstruction under limited computational resources. In this paper, we propose the enhanced asymmetric convolution blocks (EACBs) and the selective asymmetric kernel blocks (SAKBs) to effectively enhance the feature extraction ability of the network. Further, we propose a novel adaptive enhanced generative adversarial network, termed as AEGAN, for high-precision MRI reconstruction, where the proposed EACBs and SAKBs are embedded in the network architecture of AEGAN. In the AEGAN, the proposed EACBs are used to enhance the corresponding skeleton of the square kernel, and the SAKBs are used to efficiently capture useful feature information by adaptively adjusting the size of receptive fields. Therefore, the combination of EACBs and SAKBs can extract rich feature information more effectively and efficiently. In further experiments, it can be demonstrated that the proposed AEGAN exceeds the state-of-the-art GAN-based CS-MRI methods with fewer model parameters.