论文|Maosong Ran, Wenjun Xia, Yongqiang Huang, Zexin Lu, Peng Bao, Yan Liu, Huaiqiang Sun, Jiliu Zhou, and Yi Zhang*：MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI

本文（MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI）原载IEEE Transactions on Radiation and Plasma Medical Sciences，由四川大学计算机学院张意教授等科研人员创作，系四川大学“智慧法治”超前部署学科系列学术成果。后续会持续分享四川大学“智慧法治”超前部署学科系列学术成果，欢迎大家阅读。

Abstract:Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that can accurately reconstruct images from undersampled k-space data with a much lower sampling rate than the one set by the classical Nyquist- Shannon sampling theorem. Therefore, CS-MRI can efficiently accelerate acquisition time and relieve the psychological burden on patients while maintaining high imaging quality. The problems with traditional CS-MRI reconstruction are solved by iterative numerical solvers, which usually suffer from expensive computational cost and the lack of accurate handcrafted priori. In this article, inspired by deep learning's (DL's) fast inference and excellent end-to-end performance, we propose a novel cascaded convolutional neural network called MRI dual-domain reconstruction network (MD-Recon-Net) to facilitate fast and accurate magnetic resonance imaging reconstruction. Especially, different from existing DL-based methods, which operate on single domain data or both domains in a certain order, our proposed MD-Recon-Net contains two parallel and interactive branches that simultaneously perform on k-space and spatial-domain data, exploring the latent relationship between k-space and the spatial domain. The simulated experimental results show that the proposed method not only achieves competitive visual effects to several state-of-the-art methods but also outperforms other DL-based methods in terms of model scale and computational cost.

Maosong Ran, Wenjun Xia, Yongqiang Huang, Zexin Lu, Peng Bao, Yan Liu, Huaiqiang Sun, Jiliu Zhou, and Yi Zhang*. MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI. IEEE Transactions on Radiation and Plasma Medical Sciences, pp. 120-135, vol. 5, no. 1, 2021.（论文下载）