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.

翻译：高分辨率磁共振成像（MRI）在临床环境中对提升诊断准确性至关重要。然而，MRI分辨率的内在局限性限制了其广泛应用。基于深度学习的图像超分辨率（SR）方法在无需额外成本的情况下有望改善MRI分辨率。然而，这些方法通常需要大量高分辨率MRI图像进行训练，而这些图像的获取可能具有挑战性。本文提出一种非配对MRI超分辨率方法，通过自监督对比学习在有限训练数据下提升SR性能。该方法利用真实高分辨率图像与合成生成的SR图像构建正负样本对，从而促进判别性特征的学习。本研究呈现的实验结果表明，即便在可用的高分辨率图像稀少的情况下，峰值信噪比与结构相似性指数仍获得显著提升。这些发现凸显了该方法在应对训练数据有限挑战方面的潜力，进而推动高分辨率MRI在临床应用中的发展。