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Use of synthetic data has provided a potential solution for addressingunavailable or insufficient training samples in deep learning-based magneticresonance imaging (MRI). However, the challenge brought by domain gap betweensynthetic and real data is usually encountered, especially under complexexperimental conditions. In this study, by combining Bloch simulation andgeneral MRI models, we propose a framework for addressing the lack of trainingdata in supervised learning scenarios, termed MOST-DL. A challengingapplication is demonstrated to verify the proposed framework and achievemotion-robust T2 mapping using single-shot overlapping-echo acquisition. Wedecompose the process into two main steps: (1) calibrationless parallelreconstruction for ultra-fast pulse sequence and (2) intra-shot motioncorrection for T2 mapping. To bridge the domain gap, realistic textures from apublic database and various imperfection simulations were explored. The neuralnetwork was first trained with pure synthetic data and then evaluated with invivo human brain. Both simulation and in vivo experiments show that the MOST-DLmethod significantly reduces ghosting and motion artifacts in T2 maps in thepresence of unpredictable subject movement and has the potential to be appliedto motion-prone patients in the clinic.

Model-based Synthetic Data-driven Learning (MOST-DL): Application in  Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo  Acquisition