  Motion artifact reduction is one of the important research topics in MR
imaging, as the motion artifact degrades image quality and makes diagnosis
difficult. Recently, many deep learning approaches have been studied for motion
artifact reduction. Unfortunately, most existing models are trained in a
supervised manner, requiring paired motion-corrupted and motion-free images, or
are based on a strict motion-corruption model, which limits their use for
real-world situations. To address this issue, here we present an annealed
score-based diffusion model for MRI motion artifact reduction. Specifically, we
train a score-based model using only motion-free images, and then motion
artifacts are removed by applying forward and reverse diffusion processes
repeatedly to gradually impose a low-frequency data consistency. Experimental
results verify that the proposed method successfully reduces both simulated and
in vivo motion artifacts, outperforming the state-of-the-art deep learning
methods.
