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Long scan time significantly hinders the widespread applications ofthree-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR)imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel methodbased on score-based diffusion models with self-supervised learning.Specifically, we first establish a mapping between the undersampled k-spacemeasurements and the MR images, utilizing a self-supervised Bayesianreconstruction network. Secondly, we develop a joint score-based diffusionmodel on 3D-MC-CMR images to capture their inherent distribution. The 3D-MC-CMRimages are finally reconstructed using the conditioned Langenvin Markov chainMonte Carlo sampling. This approach enables accurate reconstruction withoutfully sampled training data. Its performance was tested on the dataset acquiredby a 3D joint myocardial T1 and T1rho mapping sequence. The T1 and T1rho mapswere estimated via a dictionary matching method from the reconstructed images.Experimental results show that the proposed method outperforms traditionalcompressed sensing and existing self-supervised deep learning MRIreconstruction methods. It also achieves high quality T1 and T1rho parametricmaps close to the reference maps obtained by traditional mapping sequences,even at a high acceleration rate of 14.

Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging