Data-centric artificial intelligence (AI) has remarkably advanced medical imaging, with emerging methods using synthetic data to address data scarcity while introducing synthetic-to-real gaps. Unsupervised domain adaptation (UDA) shows promise in ground truth-scarce tasks, but its application in reconstruction remains underexplored. Although multiple overlapping-echo detachment (MOLED) achieves ultra-fast multi-parametric reconstruction, extending its application to various clinical scenarios, the quality suffers from deficiency in mitigating the domain gap, difficulty in maintaining structural integrity, and inadequacy in ensuring mapping accuracy. To resolve these issues, we proposed frequency-aware perturbation and selection (FPS), comprising Wasserstein distance-modulated frequency-aware perturbation (WDFP) and hierarchical frequency-aware selection network (HFSNet), which integrates frequency-aware adaptive selection (FAS), compact FAS (cFAS) and feature-aware architecture integration (FAI). Specifically, perturbation activates domain-invariant feature learning within uncertainty, while selection refines optimal solutions within perturbation, establishing a robust and closed-loop learning pathway. Extensive experiments on synthetic data, along with diverse real clinical cases from 5 healthy volunteers, 94 ischemic stroke patients, and 46 meningioma patients, demonstrate the superiority and clinical applicability of FPS. Furthermore, FPS is applied to diffusion tensor imaging (DTI), underscoring its versatility and potential for broader medical applications. The code is available at https://github.com/flyannie/FPS.

翻译：以数据为中心的人工智能显著推动了医学影像的发展，新兴方法利用合成数据应对数据稀缺问题，但同时也引入了合成与真实数据之间的差距。无监督域适应在真实标签稀缺的任务中展现出潜力，但其在重建任务中的应用仍待深入探索。尽管多重重叠回波分离技术实现了超快速的多参数重建，并将其应用扩展至多种临床场景，但其重建质量仍受限于域差距缓解不足、结构完整性保持困难以及映射精度保障不充分等问题。为解决这些问题，我们提出了频率感知扰动与选择方法，该方法由Wasserstein距离调制的频率感知扰动和分层频率感知选择网络构成，后者集成了频率感知自适应选择、紧凑型频率感知自适应选择以及特征感知架构集成模块。具体而言，扰动在不确定性范围内激活域不变特征学习，而选择则在扰动范围内优化最优解，从而建立了一条鲁棒的闭环学习路径。在合成数据以及来自5名健康志愿者、94名缺血性卒中患者和46名脑膜瘤患者的多样化真实临床病例上进行的大量实验，证明了该方法的优越性和临床适用性。此外，该方法被应用于扩散张量成像，进一步凸显了其多功能性及在更广泛医学应用中的潜力。代码发布于https://github.com/flyannie/FPS。