  Magnetic resonance imaging (MRI) is crucial for enhancing diagnostic accuracy
in clinical settings. However, the inherent long scan time of MRI restricts its
widespread applicability. Deep learning-based image super-resolution (SR)
methods exhibit promise in improving MRI resolution without additional cost.
Due to lacking of aligned high-resolution (HR) and low-resolution (LR) MRI
image pairs, unsupervised approaches are widely adopted for SR reconstruction
with unpaired MRI images. However, these methods still require a substantial
number of HR MRI images for training, which can be difficult to acquire. To
this end, we propose an unpaired MRI SR approach that employs contrastive
learning to enhance SR performance with limited HR training data. Empirical
results presented in this study underscore significant enhancements in the peak
signal-to-noise ratio and structural similarity index, even when a paucity of
HR images is available. These findings accentuate the potential of our approach
in addressing the challenge of limited HR training data, thereby contributing
to the advancement of MRI in clinical applications.
