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

TL;DR

This paper introduces a novel score-based diffusion model with self-supervised learning to accelerate 3D multi-contrast cardiac MRI, enabling high-quality reconstructions at high acceleration rates without fully sampled training data.

Contribution

It proposes a joint score-based diffusion model combined with a self-supervised Bayesian reconstruction network for faster 3D-MC-CMR imaging.

Findings

Outperforms traditional compressed sensing methods.

Achieves high-quality T1 and T1rho maps at acceleration rate of 14.

Reconstruction quality close to fully sampled data.

Abstract

Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion models with self-supervised learning. Specifically, 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 sampling. This approach enables accurate reconstruction without fully sampled training data. Its performance was tested on the dataset acquired by a 3D joint myocardial T1 and T1rho mapping sequence. The T1 and T1rho maps were estimated via a dictionary matching method from the reconstructed images. Experimental results show that the proposed method outperforms traditional compressed sensing and existing self-supervised deep learning MRI reconstruction methods. It also achieves high quality T1 and T1rho parametric maps close to the reference maps, even at a high acceleration rate of 14.