  Improving the resolution of magnetic resonance (MR) image data is critical to
computer-aided diagnosis and brain function analysis. Higher resolution helps
to capture more detailed content, but typically induces to lower
signal-to-noise ratio and longer scanning time. To this end, MR image
super-resolution has become a widely-interested topic in recent times. Existing
works establish extensive deep models with the conventional architectures based
on convolutional neural networks (CNN). In this work, to further advance this
research field, we make an early effort to build a Transformer-based MR image
super-resolution framework, with careful designs on exploring valuable domain
prior knowledge. Specifically, we consider two-fold domain priors including the
high-frequency structure prior and the inter-modality context prior, and
establish a novel Transformer architecture, called Cross-modality
high-frequency Transformer (Cohf-T), to introduce such priors into
super-resolving the low-resolution (LR) MR images. Experiments on two datasets
indicate that Cohf-T achieves new state-of-the-art performance.
