Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI

Motivation: Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring. Goal(s): To improve image sharpness and motion delineation for cine MRI under high undersampling rates. Approach: A combined non-generative reconstruction and diffusion enhancement model along with a novel paired sampling strategy was developed. Results: The proposed combined method provided sharper tissue boundaries and clearer motion than the original reconstruction in experts’ evaluation on clinical data. The innovative paired sampling strategy substantially reduced artificial noises in the generative results. Impact: The approach has the potential to improve reconstruction quality in highly accelerated cardiac cine imaging. The novel paired sampling for diffusion generation may be applied to other conditional tasks to reduce the artificial noises stemming from noisy training data.