Diffusion Model-Based MRI Super-Resolution Synthesis

In modern medical imaging, although there have been advances in the application of super-resolution technology to MRI in recent years, current applications still cannot meet practical needs. For example, for MRI images under specific pathological or physiological conditions, the existing super-resolution technology still lacks effectiveness in processing noise and restoring details. And when processing images with complex organizational structures, such as white matter fiber bundles in the brain, existing super-resolution techniques often fail to accurately restore image details, resulting in structural distortion. To address these deficiencies, we propose in this study an advanced super-resolution (SR) reconstruction framework tailored specifically for magnetic resonance imaging (MRI). Our approach makes use of the Denoising Diffusion Probabilistic Model (DDPM) and CrossAttention, an advanced technique known for its ability to maintain data accuracy while making the most of available conditions, leading to high-quality image restoration. By incorporating sophisticated priors and innovative network architecture, our method significantly outperforms traditional SR techniques, particularly in preserving fine anatomical details and enhancing overall image quality. The proposed framework undergoes rigorous validation through extensive experiments on diverse MRI datasets, demonstrating its robustness and effectiveness in various scenarios. Furthermore, we provide a comprehensive analysis of the performance metrics, including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), Normalized Mean Squared Error (NMSE), and Universal Quality Index (UQI), to underscore the superiority of our DDPM-based approach. This work not only contributes to advancing the state-of-the-art in MRI SR but also paves the way for broader applications in medical imaging and related fields.