Accurate Multi-contrast MRI Super-Resolution via a Dual Cross-Attention Transformer Network

Magnetic Resonance Imaging (MRI) is a critical imaging tool in clinical diagnosis, but obtaining high-resolution MRI images can be challenging due to hardware and scan time limitations. Recent studies have shown that using reference images from multi-contrast MRI data could improve super-resolution quality. However, the commonly employed strategies, e.g., channel concatenation or hard-attention based texture transfer, may not be optimal given the visual differences between multi-contrast MRI images. To address these limitations, we propose a new Dual Cross-Attention Multi-contrast Super Resolution (DCAMSR) framework. This approach introduces a dual cross-attention transformer architecture, where the features of the reference image and the up-sampled input image are extracted and promoted with both spatial and channel attention in multiple resolutions. Unlike existing hard-attention based methods where only the most correlated features are sought via the highly down-sampled reference images, the proposed architecture is more powerful to capture and fuse the shareable information between the multi-contrast images. Extensive experiments are conducted on fastMRI knee data at high field and more challenging brain data at low field, demonstrating that DCAMSR can substantially outperform the state-of-the-art single-image and multi-contrast MRI super-resolution methods, and even remains robust in a self-referenced manner. The code for DCAMSR is avaliable at https://github.com/Solor-pikachu/DCAMSR .