Reconstructing Reality: Robust High-Frequency Recovery for MRI via Latent Diffusion Models

Multi-contrast magnetic resonance imaging MRI is a widely used analytical tool for characterizing tissue contrast in neurological disorders. Although conventional MRI techniques provide rich contrast information in the diagnosis of neurological diseases, their limited spatial resolution often hinders the precise identification of subtle pathological regions. Therefore, super-resolution SR reconstruction of MRI images holds significant importance in the field of medical imaging. Traditional end-to-end deep neural network approaches tend to learn the average of multiple possible reconstruction outcomes, resulting in overly smoothed generated images that lack high-frequency details. In recent years, generative models have demonstrated remarkable capabilities in SR tasks by synthesizing more realistic high-frequency information, thereby substantially mitigating the aforementioned issue. However, generative models generally exhibit considerable randomness, making it challenging to ensure the stability and consistency of the results. To address this, we propose a novel MRI SR method that integrates the strengths of both generative and discriminative models. Specifically, we employ a latent diffusion model LDT to capture the high-frequency information in real images and utilize the low-frequency information from low-resolution LR images as conditional input for an autoencoder to generate high-resolution HR images. Quantitative experimental results demonstrate that our method outperforms existing state-of-the-art MRI SR approaches across multiple metrics while maintaining a more lightweight architecture. Furthermore, visualization results further validate the superiority of our method in reconstructing high-frequency details.