Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence
Motivation: Cardiac cine MRI requires multiple breath-holds to cover the left ventricle. Acquiring images of small matrix size effectively reduces acquisition time but causes a loss of spatial details.
Goal(s): To further research on the application of diffusion models in accelerating Cardiac cine MRI.
Approach: A diffusion model was constructed to achieve super-resolution of cardiac cine MRI to restore lost details in low-resolution images.
Results: The proposed diffusion model based super-resolution method can recover high-frequency details for cardiac cine MRI and outperformed the state-of-the-art generative adversarial super-resolution network.
Impact: The proposed method yielding good-quality cardiac cine images from low-resolution images helps to accelerate cardiac cine MRI and could be potentially applied to achieve high spatial-temporal real-time cardiac MRI.
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