Cine Cardiac MRI Motion Correction using Denoising Diffusion Probabilistic Models

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.