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Multi-contrast MRI Reconstruction via Multi-scale Patched-based Cross-contrast Channel Selection

In this paper, we tackle the under-sampled MRI reconstruction problem with auxiliary contrasts. Instead of adopting a naive fusion scheme before feeding the multi-contrast features into the networks, we propose a more interpretable early fusion approach. By utilizing the inherent multi-contrast nature of MR image feature maps, we propose a Cross-contrast CHannel Selection (CCHS) module to explicitly explore dependency between channels for better cross-contrast fusion in reconstruction of the target one. Furthermore, the CCHS module is extended into a more adaptive version by introducing a multi-scale patch-based learning strategy. Quantitative and qualitative experimental results on the fastMRI dataset demonstrate the effectiveness of our approach.