Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence
Motivation: MR-based “real-time” imaging of dynamic processes, as the beating heart, often depends on fast (undersampled) scans, which are subsequently reconstructed by algorithms exploiting prior knowledge. Spatio-temporal models describing the data in suboptimal manner can thereby lead to residual artifacts.
Goal(s): A high-quality model to regularize the reconstruction of real-time cardiac MRI based on undersampled spiral data acquisitions.
Approach: A video diffusion model was trained using cine videos in magnitude reconstruction and subsequently applied as a prior in a plug-and-play FISTA approach.
Results: Reconstructions of undersampled real-time frames with higher image quality than a low rank plus sparse approach.
Impact: We show the potential of probabilistic video diffusion models as a promising prior in iterative reconstructions of undersampled dynamic MR data. In our example, the approach enabled high quality real-time cardiac functional MRI in patients with arrhythmia.
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