The paper "Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution" addresses the challenges of current diffusion model (DM)-based methods in MRI super-resolution (SR) reconstruction, particularly the inefficiency and distortion issues. To tackle these problems, the authors propose an efficient diffusion model named DiffMSR, which integrates DM with a Prior-Guide Large Window Transformer (PLWformer). The key contributions of the paper are: 1. **DiffMSR**: An efficient diffusion model for multi-contrast MRI SR that generates prior knowledge in a highly compact low-dimensional latent space, reducing the number of iterations and computational resources. 2. **PLWformer**: A decoder designed to extend the receptive field while utilizing the prior knowledge generated by DM, ensuring undistorted and artifact-free reconstructed images. 3. **Two-Stage Training**: The training process is divided into two stages: Prior Extraction (PE) and DM training. PE compresses the HR image into a compact latent space, while the DM learns to generate prior knowledge from Gaussian noise. The PLWformer is then trained to utilize this prior knowledge effectively. The paper demonstrates the effectiveness of DiffMSR through extensive experiments on public and clinical datasets, showing superior performance compared to state-of-the-art methods in terms of reconstruction metrics (PSNR and SSIM) and computational efficiency. The authors also conduct ablation studies to validate the contributions of each component of the proposed method.The paper "Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution" addresses the challenges of current diffusion model (DM)-based methods in MRI super-resolution (SR) reconstruction, particularly the inefficiency and distortion issues. To tackle these problems, the authors propose an efficient diffusion model named DiffMSR, which integrates DM with a Prior-Guide Large Window Transformer (PLWformer). The key contributions of the paper are: 1. **DiffMSR**: An efficient diffusion model for multi-contrast MRI SR that generates prior knowledge in a highly compact low-dimensional latent space, reducing the number of iterations and computational resources. 2. **PLWformer**: A decoder designed to extend the receptive field while utilizing the prior knowledge generated by DM, ensuring undistorted and artifact-free reconstructed images. 3. **Two-Stage Training**: The training process is divided into two stages: Prior Extraction (PE) and DM training. PE compresses the HR image into a compact latent space, while the DM learns to generate prior knowledge from Gaussian noise. The PLWformer is then trained to utilize this prior knowledge effectively. The paper demonstrates the effectiveness of DiffMSR through extensive experiments on public and clinical datasets, showing superior performance compared to state-of-the-art methods in terms of reconstruction metrics (PSNR and SSIM) and computational efficiency. The authors also conduct ablation studies to validate the contributions of each component of the proposed method.