用可逆层级流实现多模态MRI无配对统一谐波化
IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model
- 通过可逆层级流分步消除伪影特征，保证解剖结构不变形
- 在多模态数据上实现更高保真度，下游任务性能更优
- 适合大规模多中心医学影像研究，无需配对数据
回顾性MRI谐波化受限于跨模态扩展性差和依赖携带受试者数据集。为解决这些问题，我们提出IHF-Harmony，一种基于无配对数据的统一可逆层级流框架，用于多模态谐波化。通过将转换过程分解为可逆特征变换，IHF-Harmony确保双射映射与无损重建，防止解剖畸变。具体地，可逆层级流（IHF）采用分层减法耦合逐步去除与伪影相关的特征，而伪影感知归一化（AAN）利用解剖固定特征调制，精确传递目标特征。结合解剖与伪影一致性损失目标，IHF-Harmony实现了高保真谐波化并保留源图像解剖结构。在多个MRI模态上的实验表明，该方法在解剖保真度和下游任务性能上均优于现有方法，为大规模多中心成像研究提供了稳健的谐波化方案。代码已开源：https://github.com/Idea89560041/IHF-Harmony。
原文摘要 · Abstract (English)
Retrospective MRI harmonization is limited by poor scalability across modalities and reliance on traveling subject datasets. To address these challenges, we introduce IHF-Harmony, a unified invertible hierarchy flow framework for multi-modality harmonization using unpaired data. By decomposing the translation process into reversible feature transformations, IHF-Harmony guarantees bijective mapping and lossless reconstruction to prevent anatomical distortion. Specifically, an invertible hierarchy flow (IHF) performs hierarchical subtractive coupling to progressively remove artefact-related features, while an artefact-aware normalization (AAN) employs anatomy-fixed feature modulation to accurately transfer target characteristics. Combined with anatomy and artefact consistency loss objectives, IHF-Harmony achieves high-fidelity harmonization that retains source anatomy. Experiments across multiple MRI modalities demonstrate that IHF-Harmony outperforms existing methods in both anatomical fidelity and downstream task performance, facilitating robust harmonization for large-scale multi-site imaging studies. Code is available at https://github.com/Idea89560041/IHF-Harmony.
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