  We consider an MRI reconstruction problem with input of k-space data at a
very low undersampled rate. This can practically benefit patient due to reduced
time of MRI scan, but it is also challenging since quality of reconstruction
may be compromised. Currently, deep learning based methods dominate MRI
reconstruction over traditional approaches such as Compressed Sensing, but they
rarely show satisfactory performance in the case of low undersampled k-space
data. One explanation is that these methods treat channel-wise features
equally, which results in degraded representation ability of the neural
network. To solve this problem, we propose a new model called MRI Cascaded
Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a
variant of U-net with Channel-wise Attention (UCA) module, (ii) a long skip
connection and (iii) a combined loss. Our model is able to attend to salient
information by filtering irrelevant features and also concentrate on
high-frequency information by enforcing low-frequency information bypassed to
the final output. We conduct both quantitative evaluation and qualitative
analysis of our method on a cardiac dataset. The experiment shows that our
method achieves very promising results in terms of three common metrics on the
MRI reconstruction with low undersampled k-space data.
