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Compressed sensing magnetic resonance imaging (CS-MRI) is a theoreticalframework that can accurately reconstruct images from undersampled k-space datawith a much lower sampling rate than the one set by the classicalNyquist-Shannon sampling theorem. Therefore, CS-MRI can efficiently accelerateacquisition time and relieve the psychological burden on patients whilemaintaining high imaging quality. The problems with traditional CS-MRIreconstruction are solved by iterative numerical solvers, which usually sufferfrom expensive computational cost and the lack of accurate handcrafted priori.In this paper, inspired by deep learning's (DL's) fast inference and excellentend-to-end performance, we propose a novel cascaded convolutional neuralnetwork called MD-Recon-Net to facilitate fast and accurate MRI reconstruction.Especially, different from existing DL-based methods, which operate on singledomain data or both domains in a certain order, our proposed MD-Recon-Netcontains two parallel and interactive branches that simultaneously perform onk-space and spatial-domain data, exploring the latent relationship betweenk-space and the spatial domain. The simulated experimental results show thatthe proposed method not only achieves competitive visual effects to severalstate-of-the-art methods, but also outperforms other DL-based methods in termsof model scale and computational cost.

MD-Recon-Net: A Parallel Dual-Domain Convolutional Neural Network for Compressed Sensing MRI