Efficient structurally-strengthened generative adversarial network for MRI reconstruction

Compressed sensing based magnetic resonance imaging (CS-MRI) methods greatly shorten the scanning time while ensuring the quality of image reconstruction in an efficient way. Recently deep learning has been introduced into MRI reconstruction to further improve the image quality and shorten reconstruction time. In this paper, we propose an efficient structurally-strengthened Generative Adversarial Network, termed as ESSGAN, for reconstructing MR images from highly under-sampled k-space data. ESSGAN consists of a structurally-strengthened generator (termed as SG) and a discriminator. In SG, we introduce strengthened connections to enhance feature propagation and reuse between the concatenated strengthened convolutional autoencoders (termed as SCAEs), where each SCAE is a variant of a typical convolutional autoencoder. In addition, we creatively introduce the residual in residual blocks (termed as RIRBs) to SG. The RIRBs can effectively enhance the expression ability of the proposed SG. To further preserve more image details, we introduce an enhanced structural loss to SG. The experimental results demonstrate that ESSGAN can provide higher image quality with fewer model parameters than the state-of-the-art deep learning-based methods at different undersampling rates under different types of undersampling patterns.