弥合合成到真实的差距：用于单次多参数映射重建的频率感知扰动与选择
Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction
AI总结：
针对医学成像中合成数据到真实数据的域差距问题，提出包含频率感知扰动与选择网络的FPS方法，经多类临床数据验证性能优异，且可推广至DTI等更多医学成像任务。
摘要：
以数据为中心的人工智能（AI）已显著推动医学成像发展，新兴方法利用合成数据解决数据稀缺问题，同时也带来了合成到真实的差距。无监督域适应（UDA）在真值稀缺的任务中展现出潜力，但其在重建任务中的应用仍有待深入探索。尽管多回波重叠分离（MOLED）实现了超快速多参数重建，拓展了其在多种临床场景中的应用，但其成像质量受限于域差距缓解不足、结构完整性难以维持以及映射准确性保障不足等问题。为解决这些问题，我们提出了频率感知扰动与选择（FPS）方法，包含Wasserstein距离调制的频率感知扰动（WDFP）和分层频率感知选择网络（HFSNet），后者整合了频率感知自适应选择（FAS）、紧凑型FAS（cFAS）以及特征感知架构集成（FAI）。具体而言，扰动在不确定性范围内激活域不变特征学习，而选择在扰动范围内优化最优解，构建了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真实临床病例上开展的大量实验，证明了FPS的优越性和临床适用性。此外，FPS还被应用于弥散张量成像（DTI），凸显了其通用性和更广泛医学应用的潜力。代码可在https://github.com/flyannie/FPS获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
