CS-MRI Reconstruction Using an Improved Generative Adversarial Network with Channel Attention Mechanism

Generative adversarial network (GAN) has emerged as one of the most prominent approaches for fast CS-MRI reconstruction. However, most deep-learning models achieve performance by increasing the depth and width of the networks, leading to prolonged reconstruction time and difficulty to train. We have developed an improved GAN-based model to achieve quality performance without increasing complexity by implementing the following: 1) dilated-residual structure with different dilation rates at different depth of the networks; 2) CAM to adjust the allocation of network resources; 3) multi-scale information fusion module to achieve feature fusion. Experiment data have confirmed the validity for the modules.