Toward Better Generalization Using Synthetic Data: A Domain Adaptation Framework for T₂ Mapping via Multiple...

The generation of synthetic data using physics-based modeling provides a solution to limited or lacking real-world training samples in deep learning methods for rapid quantitative magnetic resonance imaging (qMRI). However, synthetic data distribution differs from real-world data, especially under complex imaging conditions, resulting in gaps between domains and limited generalization performance in real scenarios. Recently, a single-shot qMRI method, multiple overlapping-echo detachment imaging (MOLED), was proposed, quantifying tissue transverse relaxation time ( $\text {T}_{{2}}$ ) in the order of milliseconds with the help of a trained network. Previous works leveraged a Bloch-based simulator to generate synthetic data for network training, which leaves the domain gap between synthetic and real-world scenarios and results in limited generalization. In this study, we proposed a $\text {T}_{{2}}$ mapping method via MOLED from the perspective of domain adaptation, which obtained accurate mapping performance without real-label training and reduced the cost of sequence research at the same time. Experiments demonstrate that our method outshined in the restoration of MR anatomical structures.