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.

翻译：多对比度磁共振成像从不同角度反映人体组织信息，具有广泛的临床应用价值。通过利用不同模态间的互补信息，多对比度MRI超分辨率相较于单图像超分辨率能够取得更优效果。然而，现有方法存在以下可能限制其性能的缺陷：第一，现有方法要么简单拼接参考特征与退化特征，要么利用两者间的全局特征匹配，这些方式并不适用于多对比度MRI超分辨率任务；第二，尽管近期许多方法采用Transformer捕捉空间维度的长程依赖关系，但忽视了通道维度的自注意力对低级视觉任务同样重要。针对上述问题，我们提出了一种基于复合注意力与邻域匹配的新型网络架构(CANM-Net)用于多对比度MRI超分辨率：复合自注意力机制有效捕捉了空间维度和通道维度的依赖关系；基于邻域的特征匹配模块用于匹配退化特征与相邻参考特征，继而融合生成高质量图像。我们在IXI、fastMRI及真实扫描数据集上开展了超分辨率任务实验。在回顾性实验和前瞻性实验中，CANM-Net均优于现有最先进方法。此外，鲁棒性研究表明，当参考图像与退化图像配准不完美时，CANM-Net仍能取得良好性能，展现出良好的临床应用潜力。