  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). SSDiffRecon expresses a conditional
diffusion process as an unrolled architecture that interleaves cross-attention
transformers for reverse diffusion steps with data-consistency blocks for
physics-driven processing. Unlike recent diffusion methods for MRI
reconstruction, a self-supervision strategy is adopted to train SSDiffRecon
using only undersampled k-space data. Comprehensive experiments on public brain
MR datasets demonstrates the superiority of SSDiffRecon against
state-of-the-art supervised, and self-supervised baselines in terms of
reconstruction speed and quality. Implementation will be available at
https://github.com/yilmazkorkmaz1/SSDiffRecon.
