Self-supervised contrastive learning for MR image reconstruction of cardiac CINE on accelerated cohorts

Existing methods for deep-learning reconstruction require abundant fully-sampled images as labels, which is challenging or impractical in practice. To address this issue, we propose a self-supervised learning (SSL) approach that can be trained on subsampled data, avoiding the need for fully-sampled datasets. Specifically, we pre-train a feature extractor by contrastive learning as the first step. In the second step, the pre-trained feature extractor assists the self-supervised network during reconstruction by feature embedding. Results indicate that the proposed SSL method can effectively reconstruct cardiac CINE images without fully-sampled data. It outperforms existing SSL networks and shows comparable results to supervised learning.