2 research outputs found
Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion
Objective: Cardiac cine magnetic resonance imaging (MRI) is one of the
important means to assess cardiac functions and vascular abnormalities.
However, due to cardiac beat, blood flow, or the patient's involuntary movement
during the long acquisition, the reconstructed images are prone to motion
artifacts that affect the clinical diagnosis. Therefore, accelerated cardiac
cine MRI acquisition to achieve high-quality images is necessary for clinical
practice. Approach: A novel end-to-end deep learning network is developed to
improve 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 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. Main
results: At an 8 acceleration rate, the numerical measurements on the
ACDC dataset are SSIM of 78.40%4.57%, PSNR of 30.461.22 dB, and NMSE
of 0.04680.0075. On the ACMRI dataset, the results are SSIM of
87.65%4.20%, PSNR of 30.041.18 dB, and NMSE of 0.04730.0072.
Significance: The proposed method exhibits high-quality results with richer
details and fewer artifacts for cardiac cine MRI reconstruction on different
accelerations under free breathing conditions.Comment: 28 pages, 5 tables, 11 figure