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

• URL: http://arxiv.org/abs/2109.01664v1
• Date: Fri, 3 Sep 2021 05:53:07 GMT
• Title: Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
• Authors: Chun-Mei Feng, Yunlu Yan, Chengliang Liu, Huazhu Fu, Yong Xu, Ling Shao
• Abstract summary: We propose a separable attention network (comprising a priority attention and background separation attention) named SANet. It can explore the foreground and background areas in the forward and reverse directions with the help of the auxiliary contrast. It is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the foreground and background regions.
• Score: 88.16655157395785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, \eg, in the foreground and background. In this paper, we propose a separable attention network (comprising a foreground priority attention and background separation attention), named SANet. Our method can explore the foreground and background areas in the forward and reverse directions with the help of the auxiliary contrast, enabling it to learn clearer anatomical structures 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 foreground and background 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 different stages, obtain the dependency between the fused features, and improve 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.

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