  Use of synthetic data has provided a potential solution for addressing
unavailable or insufficient training samples in deep learning-based magnetic
resonance imaging (MRI). However, the challenge brought by domain gap between
synthetic and real data is usually encountered, especially under complex
experimental conditions. In this study, by combining Bloch simulation and
general MRI models, we propose a framework for addressing the lack of training
data in supervised learning scenarios, termed MOST-DL. A challenging
application is demonstrated to verify the proposed framework and achieve
motion-robust T2 mapping using single-shot overlapping-echo acquisition. We
decompose the process into two main steps: (1) calibrationless parallel
reconstruction for ultra-fast pulse sequence and (2) intra-shot motion
correction for T2 mapping. To bridge the domain gap, realistic textures from a
public database and various imperfection simulations were explored. The neural
network was first trained with pure synthetic data and then evaluated with in
vivo human brain. Both simulation and in vivo experiments show that the MOST-DL
method significantly reduces ghosting and motion artifacts in T2 maps in the
presence of unpredictable subject movement and has the potential to be applied
to motion-prone patients in the clinic.
