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Objective: Cardiac cine magnetic resonance imaging (MRI) is one of theimportant means to assess cardiac functions and vascular abnormalities.However, due to cardiac beat, blood flow, or the patient's involuntary movementduring the long acquisition, the reconstructed images are prone to motionartifacts that affect the clinical diagnosis. Therefore, accelerated cardiaccine MRI acquisition to achieve high-quality images is necessary for clinicalpractice. Approach: A novel end-to-end deep learning network is developed toimprove cardiac cine MRI reconstruction under free breathing conditions. 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) method with second-order bidirectional propagation is introduced toalign the adjacent cine MRI frames by maximizing spatial-temporal informationto alleviate motion artifacts. Finally, the Multi-Resolution Fusion (MRF)module is designed to correct the blur and artifacts generated from alignmentoperation and obtain the last high-quality reconstructed cardiac images. Mainresults: At an 8$\times$ acceleration rate, the numerical measurements on theACDC dataset are SSIM of 78.40%$\pm$4.57%, PSNR of 30.46$\pm$1.22 dB, and NMSEof 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM of87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18 dB, and NMSE of 0.0473$\pm$0.0072.Significance: The proposed method exhibits high-quality results with richerdetails and fewer artifacts for cardiac cine MRI reconstruction on differentaccelerations under free breathing conditions.

Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion