While compressed sensing (CS) enables highly-accelerated cardiac MRI acquisitions, its lengthy image reconstruction may limit clinical translation. Deep learning (DL) is capable reconstructing undersampled images with clinically acceptable reconstruction times. The purpose of this study was to build, train, and validate a deep learning framework for rapidly reconstructing highly-accelerated cardiac MR images, where CS reconstructed images are used as reference.
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