Keywords: AI Diffusion Models, Motion Correction
Motivation: Cine cardiac MRI is used to evaluate cardiac functions and vascular abnormalities.However, MRI requires a long scan time, which inevitably induces motion artifacts.
Goal(s): Develop a cine cardiac MR image motion correction technique to reduce both the scan time and motion artifacts.
Approach: We trained a diffusion-based model with simulated data from a public ACDC dataset to reduce the cine cardiac MRI motion artifacts.
Results: The proposed method was compared with GAN and U-Net methods in removing motion artifacts. It produced results that closely approach the ground-truth, achieving the highest SSIM and PSNR scores among all the evaluated methods.
Impact: Our method demonstrates improvements in motion compensation compared with GAN and U-Net.
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