Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution

• URL: http://arxiv.org/abs/2307.02148v3
• Date: Sat, 16 Sep 2023 18:16:11 GMT
• Title: Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution
• Authors: Wenxuan Chen, Sirui Wu, Shuai Wang, Zhongsen Li, Jia Yang, Huifeng Yao, Xiaolei Song
• Abstract summary: Multi-contrast super-resolution of MRI can achieve better results than single-image super-resolution. We propose a novel network architecture with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI SR. CANM-Net outperforms state-of-the-art approaches in both retrospective and prospective experiments.
• Score: 7.197850827700436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissue from different perspectives and has many clinical applications. By utilizing the complementary information among different modalities, multi-contrast super-resolution (SR) of MRI can achieve better results than single-image super-resolution. However, existing methods of multi-contrast MRI SR have the following shortcomings that may limit their performance: First, existing methods either simply concatenate the reference and degraded features or exploit global feature-matching between them, which are unsuitable for multi-contrast MRI SR. Second, although many recent methods employ transformers to capture long-range dependencies in the spatial dimension, they neglect that self-attention in the channel dimension is also important for low-level vision tasks. To address these shortcomings, we proposed a novel network architecture with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI SR: The compound self-attention mechanism effectively captures the dependencies in both spatial and channel dimension; the neighborhood-based feature-matching modules are exploited to match degraded features and adjacent reference features and then fuse them to obtain the high-quality images. We conduct experiments of SR tasks on the IXI, fastMRI, and real-world scanning datasets. The CANM-Net outperforms state-of-the-art approaches in both retrospective and prospective experiments. Moreover, the robustness study in our work shows that the CANM-Net still achieves good performance when the reference and degraded images are imperfectly registered, proving good potential in clinical applications.

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