  Cardiac cine magnetic resonance imaging (MRI) is one of the important means
to assess cardiac functions and vascular abnormalities. Mitigating artifacts
arising during image reconstruction and accelerating cardiac cine MRI
acquisition to obtain high-quality images is important. A novel end-to-end deep
learning network is developed to improve cardiac cine MRI reconstruction.
First, a U-Net is adopted to obtain the initial reconstructed images in
k-space. Further to remove the motion artifacts, the motion-guided deformable
alignment (MGDA) module with second-order bidirectional propagation is
introduced to align the adjacent cine MRI frames by maximizing spatial-temporal
information to alleviate motion artifacts. Finally, the multi-resolution fusion
(MRF) module is designed to correct the blur and artifacts generated from
alignment operation and obtain the last high-quality reconstructed cardiac
images. At an 8$\times$ acceleration rate, the numerical measurements on the
ACDC dataset are structural similarity index (SSIM) of 78.40%$\pm$.57%, peak
signal-to-noise ratio (PSNR) of 30.46$\pm$1.22dB, and normalized mean squared
error (NMSE) of 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM
of 87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18dB, and NMSE of 0.0473$\pm$0.0072.
The proposed method exhibits high-quality results with richer details and fewer
artifacts for cardiac cine MRI reconstruction on different accelerations.
