Effective super-resolution reconstruction for multiprotocol low-field MRI

Due to the high cost of high-field MRI equipment, low-field MRI systems are still widely used in small and medium-sized hospitals. Compared to high-field MRI, images acquired from low-field MRI often suffer from lower resolution and lower signal-to-noise ratios. And the analysis of clinical data reveals that noise levels can vary significantly across different low-field MRI protocols. In this study, we propose an effective super-resolution reconstruction model based on generative adversarial networks (GAN). The proposed model can implicitly differentiate between various sequence types, allowing it to adapt to different scan protocols during reconstruction process. To further enhance image detail, a one-to-many supervision strategy is employed during the training process, utilizing similar patches within a single image. Additionally, the number of basic blocks in the model is reduced through knowledge distillation to meet the speed requirements for clinical use. The experimental results on actual 0.35T low-field MR images suggest that the proposed method holds substantial potential for clinical application.