IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model

TL;DR

IHF-Harmony is a novel invertible hierarchy flow framework for multi-modality MRI harmonization that ensures high-fidelity, lossless translation across unpaired datasets, improving anatomical preservation and downstream task performance.

Contribution

The paper introduces IHF-Harmony, a unified invertible hierarchy flow model that effectively harmonizes multi-modality MRI data using unpaired datasets, overcoming scalability and dataset limitations.

Findings

Outperforms existing methods in anatomical fidelity.

Achieves high-quality harmonization across multiple MRI modalities.

Enhances downstream task performance with harmonized data.

Abstract

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.