  Magnetic Resonance Imaging (MRI) is a leading diagnostic modality for a wide
range of exams, where multiple contrast images are often acquired for
characterizing different tissues. However, acquiring high-resolution MRI
typically extends scan time, which can introduce motion artifacts.
Super-resolution of MRI therefore emerges as a promising approach to mitigate
these challenges. Earlier studies have investigated the use of multiple
contrasts for MRI super-resolution (MCSR), whereas majority of them did not
fully exploit the rich contrast-invariant structural information. To fully
utilize such crucial prior knowledge of multi-contrast MRI, in this work, we
propose a novel structure-guided MCSR (SGSR) framework based on a new
spatio-frequency co-query attention (CQA) mechanism. Specifically, CQA performs
attention on features of multiple contrasts with a shared structural query,
which is particularly designed to extract, fuse, and refine the common
structures from different contrasts. We further propose a novel
frequency-domain CQA module in addition to the spatial domain, to enable more
fine-grained structural refinement. Extensive experiments on fastMRI knee data
and low-field brain MRI show that SGSR outperforms state-of-the-art MCSR
methods with statistical significance.
