Purpose: We propose a novel contrastive learning latent space representation for MRI datasets with partially acquired scans. We show that this latent space can be utilized for accelerated MR image reconstruction. Theory and Methods: Our novel framework, referred to as COLADA (stands for Contrastive Learning for highly accelerated MR image reconstruction), maximizes the mutual information between differently accelerated images of an MRI scan by using self-supervised contrastive learning. In other words, it attempts to "pull" the latent representations of the same scan together and "push" the latent representations of other scans away. The generated MRI latent space is subsequently utilized for MR image reconstruction and the performance was assessed in comparison to several baseline deep learning reconstruction methods. Furthermore, the quality of the proposed latent space representation was analyzed using Alignment and Uniformity. Results: COLADA comprehensively outperformed other reconstruction methods with robustness to variations in undersampling patterns, pathological abnormalities, and noise in k-space during inference. COLADA proved the high quality of reconstruction on unseen data with minimal fine-tuning. The analysis of representation quality suggests that the contrastive features produced by COLADA are optimally distributed in latent space. Conclusion: To the best of our knowledge, this is the first attempt to utilize contrastive learning on differently accelerated images for MR image reconstruction. The proposed latent space representation has practical usage due to a large number of existing partially sampled datasets. This implies the possibility of exploring self-supervised contrastive learning further to enhance the latent space of MRI for image reconstruction.

翻译：目的：我们提出一种新颖的对比学习潜空间表示方法，用于处理部分采集的MRI数据集。研究表明，该潜空间可用于加速磁共振图像重建。理论与方法：我们提出的新框架称为COLADA（高度加速磁共振图像重建的对比学习），通过自监督对比学习最大化同一MRI扫描中不同加速图像的互信息。换言之，它试图将同一扫描的潜表示“拉近”，同时将其他扫描的潜表示“推远”。生成的MRI潜空间随后用于磁共振图像重建，其性能与几种基线深度学习重建方法进行了比较和评估。此外，利用对齐性和均匀性分析了所提出的潜空间表示的质量。结果：COLADA全面优于其他重建方法，在推理过程中对欠采样模式变化、病理异常和k空间噪声具有鲁棒性。COLADA证明了在未知数据上仅需极少的微调即可实现高质量重建。表示质量分析表明，COLADA产生的对比特征在潜空间中呈最优分布。结论：据我们所知，这是首次尝试将对比学习应用于不同加速图像的磁共振图像重建。由于存在大量已有的部分采样数据集，所提出的潜空间表示方法具有实用价值。这意味着有望进一步探索自监督对比学习，以增强MRI图像重建的潜空间表征。