Magnetic resonance imaging (MRI) is one of the most commonly applied tests in
neurology and neurosurgery. However, the utility of MRI is largely limited by
its long acquisition time, which might induce many problems including patient
discomfort and motion artifacts. Acquiring fewer k-space sampling is a
potential solution to reducing the total scanning time. However, it can lead to
severe aliasing reconstruction artifacts and thus affect the clinical
diagnosis. Nowadays, deep learning has provided new insights into the sparse
reconstruction of MRI. In this paper, we present a new approach to this problem
that iteratively fuses the information of k-space and MRI images using novel
dual Squeeze-Excitation Networks and Cross-Iteration Residual Connections. This
study included 720 clinical multi-coil brain MRI cases adopted from the
open-source deidentified fastMRI Dataset. 8-folder downsampling rate was
applied to generate the sparse k-space. Results showed that the average
reconstruction error over 120 testing cases by our proposed method was 2.28%,
which outperformed the existing image-domain prediction (6.03%, p<0.001),
k-space synthesis (6.12%, p<0.001), and dual-domain feature fusion (4.05%,
p<0.001).

本文使用双重收缩 - 激活网络和跨迭代残差连接，迭代融合 k 空间和 MRI 图像信息，实现 MRI 的稀疏重建，结果表明该方法的重建误差率平均仅为 2.28％，表现优于现有的图像域预测、k 空间合成和双域特征融合方法。