  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).
