Keywords: AI/ML Image Reconstruction, Heart
Motivation: The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processing time poses a challenge.
Goal(s): To develop a clinically feasible diffusion-model-based reconstruction pipeline to improve the image quality of cine MRI.
Approach: A multi-in-multi-out diffusion enhancement model together with fast inference strategies were developed to be used in conjunction with a reconstruction model.
Results: The diffusion reconstruction reduced spatial and temporal blurring in prospectively undersampled clinical data, as validated by experts’ inspection. The 1.5s/video processing time enabled the approach to be applied in clinical scenarios.
Impact: The proposed diffusion reconstruction pipeline provides a practical solution to cardiac cine reconstruction with enhanced quality for clinical usage. This pipeline may be transfered to the clinical application of other diffusion-based methods.
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