Using a Video Diffusion Model-prior for reconstructing undersampled dynamic MR-data – An application to real-time cardiac MRI

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.