Self-Supervised MRI Reconstruction with Unrolled Diffusion Models

Understand

Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality.

• Promising deep learning methods have recently been proposed to reconstruct accelerated MRI scans.
• However, existing methods still suffer from various limitations regarding image fidelity, contextual sensitivity, and reliance on fully-sampled acquisitions for model training.
• To comprehensively address these limitations, we propose a novel self-supervised deep reconstruction model, named Self-Supervised Diffusion Reconstruction (SSDiffRecon).