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arXiv 提交日期: 2026-02-25
📄 Abstract - IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model

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 will be released upon acceptance.

顶级标签: medical computer vision model training
详细标签: medical imaging image harmonization mri normalizing flows multi-site studies 或 搜索:

IHF-Harmony:基于可逆分层流模型的多模态磁共振图像协调方法 / IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model


1️⃣ 一句话总结

这篇论文提出了一种名为IHF-Harmony的新方法,它利用可逆分层流模型,在不依赖配对数据的情况下,有效地将来自不同设备或协议的多模态磁共振图像进行标准化协调,在保持原始解剖结构不变的同时去除图像伪影,从而提升大规模多中心医学影像研究的可靠性。

源自 arXiv: 2602.21536