Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, self-supervised learning, contrastive learning, accelerated reconstruction, fastMRIMost deep learning methods for MR image reconstruction heavily depend on supervised learning on fully sampled reference data, hence, lack generalizability on out-of-distribution inputs such as higher accelerations. To reduce the models’ dependency on fully sampled reference data and to leverage large cohorts of undersampled MR measurements, we propose a self-supervised framework that extracts contrastive features between different accelerations of a given scan and the rest of the scans in the dataset, which we then utilize for the downstream reconstruction task. Our quantitative and qualitative analysis demonstrates the superiority of the proposed framework for highly accelerated MR image reconstruction.
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