Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Self-Supervised Learning, Image Reconstruction, HeartExisting 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.
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