Zhang Chi, Yang Qizhi, Fan Linyu, Yu Shaocong, Sun Liyan, Cai Congbo, Ding Xinghao
IEEE Trans Med Imaging. 2023 Nov 28;PP. doi: 10.1109/TMI.2023.3335212.
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 (T) 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 T 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.
使用基于物理的建模生成合成数据为快速定量磁共振成像（qMRI）的深度学习方法中有限或缺乏的真实世界训练样本提供了解决方案。然而，合成数据分布与真实世界数据不同，特别是在复杂成像条件下，导致域之间存在差距以及在实际场景中泛化性能有限。最近，提出了一种单次qMRI方法，即多重重叠回波分离成像（MOLED），借助训练好的网络以毫秒级顺序量化组织横向弛豫时间（T）。先前的工作利用基于布洛赫的模拟器生成用于网络训练的合成数据，这使得合成场景与真实世界场景之间存在域差距，并导致泛化有限。在本研究中，我们从域适应的角度提出了一种通过MOLED的T映射方法，该方法无需真实标签训练即可获得准确的映射性能，同时降低了序列研究的成本。实验表明，我们的方法在磁共振解剖结构恢复方面表现出色。
