Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction

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.

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

Data-driven learning algorithm has been successfully applied to facilitate reconstruction of medical imaging. However, real-world data needed for supervised learning are typically unavailable or insufficient, especially in the field of magnetic resonance imaging (MRI). Synthetic training samples have provided a potential solution for such problem, while the challenge brought by various non-ideal situations were usually encountered especially under complex experimental conditions. In this study, a general framework, Model-based Synthetic Data-driven Learning (MOST-DL), was proposed to generate paring data for network training to achieve robust T2 mapping using overlapping-echo acquisition under severe head motion accompanied with inhomogeneous RF field. We decomposed this challenging task into parallel reconstruction and motion correction according to a forward model. The neural network was first trained in pure synthetic dataset and then evaluated with in vivo human brain. Experiments showed that MOST-DL method significantly reduces ghosting and motion artifacts in T2 maps in the presence of random and continuous subject movement. We believe that the proposed approach may open a door for solving similar problems with other MRI acquisition methods and can be extended to other areas of medical imaging.