Keywords: AI Diffusion Models, AI/ML Image Reconstruction, Dynamic MRI, Diffusion model, Cardiac MRI
Motivation: Cardiac cine MRI reconstruction remains challenging due to the inherently high dimensionality and complexity of spatiotemporal information.
Goal(s): To develop a new diffusion model tailored for robust cardiac cine MRI reconstruction.
Approach: To the best of our knowledge, there is no prior work developing spatiotemporal diffusion model for cardiac cine MRI reconstruction. The learned spatiotemporal prior is agnostic to undersampling scenarios, allowing our method to flexibly adapt to changes in reconstructions without re-training.
Results: For both healthy cases and patients, our method consistently provides state-of-the-art performance in high acceleration reconstruction and shows robustness across various undersampling scenarios and acquisition protocols.
Impact: Given the amazing robustness and generalizability, we believe that our spatiotemporal diffusion model can be an important framework for cardiac cine MRI. Furthermore, this spatiotemporal diffusion approach can be extended to inverse problems in other medical modalities involving dynamic acquisitions.
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