  Dynamic magnetic resonance imaging (MRI) exhibits high correlations in
k-space and time. In order to accelerate the dynamic MR imaging and to exploit
k-t correlations from highly undersampled data, here we propose a novel deep
learning based approach for dynamic MR image reconstruction, termed k-t NEXT
(k-t NEtwork with X-f Transform). In particular, inspired by traditional
methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true
signals from aliased signals in x-f domain to exploit the spatio-temporal
redundancies. Building on that, the proposed method then learns to recover the
signals by alternating the reconstruction process between the x-f space and
image space in an iterative fashion. This enables the network to effectively
capture useful information and jointly exploit spatio-temporal correlations
from both complementary domains. Experiments conducted on highly undersampled
short-axis cardiac cine MRI scans demonstrate that our proposed method
outperforms the current state-of-the-art dynamic MR reconstruction approaches
both quantitatively and qualitatively.
