  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.
