Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Generative Models
Motivation: Diffusion models have shown state-of-the-art performance in solving inverse problems, including MRI reconstruction. However, applicability to blind inverse problems, e.g. motion correction, is still limited.
Goal(s): To compare our method based on diffusion models to state-of-the-art reconstruction methods for reduction of retrospectively simulated and prospective motion artifacts in brain imaging.
Approach: We evaluated the mitigation of retrospective motion artifacts on fastMRI brain data (N=100, 1100 slices). Additionally, we evaluated the correction of prospective motion in healthy subjects (N=3). We compare our method to conventional and machine learning-based methods.
Results: Our method outperforms competing methods in both retrospective and prospective cases.
Impact: We demonstrate the value of our blind inverse problem framework based on diffusion models. Our method outperforms state-of-the-art methods for reconstruction with motion correction in both retrospectively and prospectively corrupted data.
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