Multi Contrast Super Resolution

Multi-contrast super-resolution (SR) aims to enhance the resolution of low-resolution magnetic resonance imaging (MRI) scans by leveraging complementary information from multiple tissue contrasts. Current research focuses on deep learning models, including transformers and diffusion models, to achieve this, often incorporating techniques like implicit neural representations, compound attention mechanisms, and multi-scale feature matching to improve accuracy and robustness. These advancements are significant because higher-resolution multi-contrast MRI images can improve diagnostic accuracy and enable more precise quantitative analysis in various medical applications, particularly in neurology and ophthalmology.