  Deep learning has achieved good success in cardiac magnetic resonance imaging
(MRI) reconstruction, in which convolutional neural networks (CNNs) learn a
mapping from the undersampled k-space to the fully sampled images. Although
these deep learning methods can improve the reconstruction quality compared
with iterative methods without requiring complex parameter selection or lengthy
reconstruction time, the following issues still need to be addressed: 1) all
these methods are based on big data and require a large amount of fully sampled
MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of
coil correlation on reconstruction in deep learning methods for dynamic MR
imaging has never been studied. In this paper, we propose an unsupervised deep
learning method for multi-coil cine MRI via a time-interleaved sampling
strategy. Specifically, a time-interleaved acquisition scheme is utilized to
build a set of fully encoded reference data by directly merging the k-space
data of adjacent time frames. Then these fully encoded data can be used to
train a parallel network for reconstructing images of each coil separately.
Finally, the images from each coil are combined via a CNN to implicitly explore
the correlations between coils. The comparisons with classic k-t FOCUSS, k-t
SLR, L+S and KLR methods on in vivo datasets show that our method can achieve
improved reconstruction results in an extremely short amount of time.
