  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.
