High-resolution (HR) magnetic resonance imaging (MRI) is crucial for
enhancing diagnostic accuracy in clinical settings. Nonetheless, the inherent
limitation of MRI resolution restricts its widespread applicability. Deep
learning-based image super-resolution (SR) methods exhibit promise in improving
MRI resolution without additional cost. However, these methods frequently
require a substantial number of HR MRI images for training, which can be
challenging to acquire. In this paper, we propose an unpaired MRI SR approach
that employs self-supervised contrastive learning to enhance SR performance
with limited training data. Our approach leverages both authentic HR images and
synthetically generated SR images to construct positive and negative sample
pairs, thus facilitating the learning of discriminative features. 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 training data, thereby contributing to
the advancement of high-resolution MRI in clinical applications.

本研究提出了一种基于自监督对比学习的非配对 MRI 超分辨率方法，利用有限的训练数据提高超分辨率性能，结果显示即使在仅有有限数量的 HR 图像的情况下，峰值信噪比和结构相似性指数都得到了显著提高，强调了该方法在解决有限训练数据挑战上的潜力，以推进临床应用中的高分辨率 MRI。