Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceObtaining paired, diverse and expert annotated medical data is extremely challenging especially for MRI reconstruction since raw data, including phase information is typically discarded from the scan leaving only the magnitude image for clinical assessment and diagnosis. Phase information contains valuable information about physiology, pathology and other tissue characteristics that are useful in developing more robust deep learning MRI reconstruction methods. In this work, we show that state of the art physics-based image reconstruction networks trained on synthetic raw MRI data consisting of synthetic phase and coil information perform comparably to image reconstruction networks trained on ground truth k-space data.
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