  Current deep learning reconstruction for accelerated cardiac cine MRI suffers
from spatial and temporal blurring. We aim to improve image sharpness and
motion delineation for cine MRI under high undersampling rates. A
spatiotemporal diffusion enhancement model conditional on an existing deep
learning reconstruction along with a novel paired sampling strategy was
developed. The diffusion model 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.
