Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction
This addresses motion artifacts in MRI for improved diagnosis, offering an unsupervised approach that overcomes limitations of supervised methods and strict corruption models, though it is incremental in applying diffusion models to this domain.
The paper tackles motion artifact reduction in MRI by proposing an annealed score-based diffusion model that trains only on motion-free images and uses forward and reverse diffusion with low-frequency data consistency, achieving superior performance over state-of-the-art methods in simulated and in vivo experiments.
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.