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Super-resolving the Magnetic Resonance (MR) image of a target contrast underthe guidance of the corresponding auxiliary contrast, which provides additionalanatomical information, is a new and effective solution for fast MR imaging.However, current multi-contrast super-resolution (SR) methods tend toconcatenate different contrasts directly, ignoring their relationships indifferent clues, e.g., in the high-intensity and low-intensity regions. In thisstudy, we propose a separable attention network (comprising high-intensitypriority attention and low-intensity separation attention), named SANet. OurSANet could explore the areas of high-intensity and low-intensity regions inthe "forward" and "reverse" directions with the help of the auxiliary contrast,while learning clearer anatomical structure and edge information for the SR ofa target-contrast MR image. SANet provides three appealing benefits: (1) It isthe first model to explore a separable attention mechanism that uses theauxiliary contrast to predict the high-intensity and low-intensity regionsregions, diverting more attention to refining any uncertain details betweenthese regions and correcting the fine areas in the reconstructed results. (2) Amulti-stage integration module is proposed to learn the response ofmulti-contrast fusion at multiple stages, get the dependency between the fusedrepresentations, and boost their representation ability. (3) Extensiveexperiments with various state-of-the-art multi-contrast SR methods on fastMRIand clinical \textit{in vivo} datasets demonstrate the superiority of ourmodel.

Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution