Masked U-net: Bridging the gap between real and synthetic data for mapping reconstruction in multiple overlapping-echo detachment imaging

Motivation: The synthetic training data used for mapping reconstruction of deep learning can simulate the required features, but it is difficult to fully simulate the unnecessary characteristics existing in real-world data. Goal(s): This work aims to enable the model to extract the essential mapping relationships for mapping reconstruction and eliminate the interference of non-ideal factors in real data. Approach: We propose a mask pre-training method called Masked U-net that allows the model to learn appropriate inductive biases on quantitative images. Results: The proposed method can better extract relevant features and reduce the interference of irrelevant factors in real data. Impact: The proposed method bridges the gap between the real data and synthetic data, improves the quality of deep learning reconstruction driven by synthetic training data, and achieves important application in T2/T2* mapping reconstruction of multiple overlapping-echo detachment (MOLED) imaging.