  Super-resolution (SR) plays a crucial role in improving the image quality of
magnetic resonance imaging (MRI). MRI produces multi-contrast images and can
provide a clear display of soft tissues. However, current super-resolution
methods only employ a single contrast, or use a simple multi-contrast fusion
mechanism, ignoring the rich relations among different contrasts, which are
valuable for improving SR. In this work, we propose a multi-stage integration
network (i.e., MINet) for multi-contrast MRI SR, which explicitly models the
dependencies between multi-contrast images at different stages to guide image
SR. In particular, our MINet first learns a hierarchical feature representation
from multiple convolutional stages for each of different-contrast image.
Subsequently, we introduce a multi-stage integration module to mine the
comprehensive relations between the representations of the multi-contrast
images. Specifically, the module matches each representation with all other
features, which are integrated in terms of their similarities to obtain an
enriched representation. Extensive experiments on fastMRI and real-world
clinical datasets demonstrate that 1) our MINet outperforms state-of-the-art
multi-contrast SR methods in terms of various metrics and 2) our multi-stage
integration module is able to excavate complex interactions among
multi-contrast features at different stages, leading to improved target-image
quality.
