Image reconstruction performance using mixed real and synthetic MR phase training data

Motivation: Prospectively developing MRI datasets with raw-kspace for MRI reconstruction is difficult, expensive and time-consuming. Generating synthetic k-space with synthetic phase as training data has been shown to work comparably for training MRI reconstruction models but its hard to access paired synthetic data. Goal(s): How does training data consisting of mixed real and synthetic k-space (including synthetic phase) affect image reconstruction performance. Approach: Five variational networks were trained with varying amounts of mixed real and synthetic training data. Image quality metrics were used to evaluate the quality of reconstructed images. Results: Adding small amounts of real training data helps increase reconstruction performance. Impact: The results suggest that small addition of real training data in addition to using mostly synthetic training data can help reconstruction performance. This could be useful clinically where synthetic data can augment models trained with small amounts of real data.