This study demonstrates the potential of using deep learning for generating raw data to augment raw datasets of limited size for use in deep-learning-based MR image reconstruction. Using an adversarial autoencoder architecture, variability in 10 T1-weighted raw datasets from the fastMRI database was learned and recombined into new, random raw data. We trained a deep-learning-based Compressed Sensing reconstruction using both conventional approaches with real raw data and compared the results to training with generated raw data. The reconstruction from generated raw data showed improved reconstruction results and better generalization to scans with slightly different echo times.
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