  Recently, diffusion models (DM) have been applied in magnetic resonance
imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive
performance, especially with regard to detailed reconstruction. However, the
current DM-based SR reconstruction methods still face the following issues: (1)
They require a large number of iterations to reconstruct the final image, which
is inefficient and consumes a significant amount of computational resources.
(2) The results reconstructed by these methods are often misaligned with the
real high-resolution images, leading to remarkable distortion in the
reconstructed MR images. To address the aforementioned issues, we propose an
efficient diffusion model for multi-contrast MRI SR, named as DiffMSR.
Specifically, we apply DM in a highly compact low-dimensional latent space to
generate prior knowledge with high-frequency detail information. The highly
compact latent space ensures that DM requires only a few simple iterations to
produce accurate prior knowledge. In addition, we design the Prior-Guide Large
Window Transformer (PLWformer) as the decoder for DM, which can extend the
receptive field while fully utilizing the prior knowledge generated by DM to
ensure that the reconstructed MR image remains undistorted. Extensive
experiments on public and clinical datasets demonstrate that our DiffMSR
outperforms state-of-the-art methods.
