  Super-resolving the Magnetic Resonance (MR) image of a target contrast under
the guidance of the corresponding auxiliary contrast, which provides additional
anatomical information, is a new and effective solution for fast MR imaging.
However, current multi-contrast super-resolution (SR) methods tend to
concatenate different contrasts directly, ignoring their relationships in
different clues, e.g., in the high-intensity and low-intensity regions. In this
study, we propose a separable attention network (comprising high-intensity
priority attention and low-intensity separation attention), named SANet. Our
SANet could explore the areas of high-intensity and low-intensity regions in
the "forward" and "reverse" directions with the help of the auxiliary contrast,
while learning clearer anatomical structure and edge information for the SR of
a target-contrast MR image. SANet provides three appealing benefits: (1) It is
the first model to explore a separable attention mechanism that uses the
auxiliary contrast to predict the high-intensity and low-intensity regions
regions, diverting more attention to refining any uncertain details between
these regions and correcting the fine areas in the reconstructed results. (2) A
multi-stage integration module is proposed to learn the response of
multi-contrast fusion at multiple stages, get the dependency between the fused
representations, and boost their representation ability. (3) Extensive
experiments with various state-of-the-art multi-contrast SR methods on fastMRI
and clinical \textit{in vivo} datasets demonstrate the superiority of our
model.
