In Magnetic Resonance Imaging (MRI), image acquisitions are often undersampled in the measurement domain to accelerate the scanning process, at the expense of image quality. However, image quality is a crucial factor that influences the accuracy of clinical diagnosis; hence, high-quality image reconstruction from undersampled measurements has been a key area of research. Recently, deep learning (DL) methods have emerged as the state-of-the-art for MRI reconstruction, typically involving deep neural networks to transform undersampled MRI images into high-quality MRI images through data-driven processes. Nevertheless, there is clear and significant room for improvement in undersampled DL MRI reconstruction to meet the high standards required for clinical diagnosis, in terms of eliminating aliasing artifacts and reducing image noise. In this paper, we introduce a self-supervised pretraining procedure using contrastive learning to improve the accuracy of undersampled DL MRI reconstruction. We use contrastive learning to transform the MRI image representations into a latent space that maximizes mutual information among different undersampled representations and optimizes the information content at the input of the downstream DL reconstruction models. Our experiments demonstrate improved reconstruction accuracy across a range of acceleration factors and datasets, both quantitatively and qualitatively. Furthermore, our extended experiments validate the proposed framework's robustness under adversarial conditions, such as measurement noise, different k-space sampling patterns, and pathological abnormalities, and also prove the transfer learning capabilities on MRI datasets with completely different anatomy. Additionally, we conducted experiments to visualize and analyze the properties of the proposed MRI contrastive learning latent space.

翻译：在磁共振成像（MRI）中，为加速扫描过程常对测量域进行欠采样，但这会以牺牲图像质量为代价。然而，图像质量是影响临床诊断准确性的关键因素，因此从欠采样测量中重建高质量图像一直是重点研究领域。近年来，深度学习（DL）方法已成为MRI重建的前沿技术，通常通过数据驱动过程利用深度神经网络将欠采样MRI图像转换为高质量MRI图像。然而，为满足临床诊断的高标准要求，当前欠采样DL MRI重建在消除混叠伪影和降低图像噪声方面仍有明显且显著的改进空间。本文提出一种基于对比学习的自监督预训练方法，旨在提升欠采样DL MRI重建的精度。我们利用对比学习将MRI图像表示转换到潜在空间，该空间能够最大化不同欠采样表示间的互信息，并优化下游DL重建模型输入端的信息含量。实验证明，该方法在一系列加速因子和数据集上均实现了定量与定性双重指标的重建精度提升。此外，扩展实验验证了所提框架在对抗性条件下的鲁棒性，包括测量噪声、不同k空间采样模式和病理异常等情况，同时证明了该方法在解剖结构完全不同的MRI数据集上具备迁移学习能力。另外，我们通过实验对所提MRI对比学习潜在空间的特性进行了可视化分析与探究。