Magnetic Resonance Imaging Super Resolution

Magnetic Resonance Imaging (MRI) super-resolution aims to enhance the resolution of MRI scans without increasing scan time, improving diagnostic accuracy and reducing artifacts. Current research heavily utilizes deep learning, focusing on advanced architectures like transformers and diffusion models, often incorporating multi-contrast information for improved reconstruction accuracy. These methods leverage various techniques, including attention mechanisms, implicit neural representations, and contrastive learning, to achieve superior performance compared to traditional approaches. The resulting improvements in image quality have significant implications for clinical diagnosis and various downstream medical image analysis tasks.