Score-based Diffusion Models with Self-supervised Learning for Accelerated 3D Multi-contrast Cardiac MR Imaging

Motivation: Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. Goal(s): This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion models with self-supervised learning. Approach: We first establish a mapping between the undersampled k-space measurements and the MR images, utilizing a self-supervised Bayesian reconstruction network. Secondly, we develop a joint score-based diffusion model on 3D-MC-CMR images to capture their inherent distribution. The 3D-MC-CMR images are finally reconstructed using the conditioned Langenvin Markov chain Monte Carlo (MCMC) sampling. Results: This approach enables accurate reconstruction with high acceleration rates up to 14. Impact: The proposed method is trained in a self-supervised manner and therefore particularly suited for 3D CMR imaging that lacks fully sampled data.