Keywords: AI Diffusion Models, Analysis/Processing
Motivation: Cine cardiac MRI (CMR) 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 accelerate cine CMR acquisition while maintaining high image quality and temporal consistency based on diffusion model.
Approach: A diffusion model with fast inter-frame motion-guided sampling is constructed to achieve super-resolution of cine CMR while suppressing potential inter-frame variations of generated dynamic images.
Results: The proposed method outperforms both the state-of-the-art GAN-based super-resolution method, ESRGAN, and the cutting-edge fast diffusion super-resolution model, ResShift for recovering high-frequency details in cine CMR.
Impact: The proposed method yielding good-quality cine cardiac MR image series from low-resolution images enables accelerated cine cardiac MR acquisition, and could be potentially applied to achieve high spatial-temporal real-time cardiac MRI.
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