Variational Diffusion Models for Motion Correction: Comprehensive Evaluation

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.