Contrastive Learning MRI Reconstruction
Mevan Ekanayake Zhifeng Chen Gary Egan Mehrtash Harandi Zhaolin Chen
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.