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

Pengli Zhu, Yitao Zhu, Haowen Pang, Anqi Qiu

TL;DR

IHF-Harmony, a unified invertible hierarchy flow framework for multi-modality harmonization using unpaired data, achieves high-fidelity harmonization that retains source anatomy by decomposing the translation process into reversible feature transformations.

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 will be released upon acceptance.

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

TL;DR

IHF-Harmony, a unified invertible hierarchy flow framework for multi-modality harmonization using unpaired data, achieves high-fidelity harmonization that retains source anatomy by decomposing the translation process into reversible feature transformations.

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 will be released upon acceptance.
Paper Structure (14 sections, 5 equations, 4 figures, 1 table)

This paper contains 14 sections, 5 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Overall Architecture
  4. Squeeze Operation
  5. Artefact-Aware Normalization
  6. Invertible Hierarchy Flow (IHF)
  7. Loss Functions
  8. Implementation
  9. Experimental Results
  10. Removal of Vendor Variance with Image Fidelity Preservation
  11. Structural MRI Harmonization and Histogram Consistency
  12. Diffusion MRI Harmonization and Model Fitting Consistency
  13. Ablation Studies
  14. Conclusion

Figures (4)

  • Figure 1: Overview of IHF-Harmony.a, IHF-Harmony works in an invertible manner. The blue arrows indicate the forward pass for feature extraction, while the red arrows denote the backward pass for image reconstruction. b, invertible hierarchy flow (IHF) performs hierarchical channel-wise subtraction to enable learnable spatial feature transformation and multi-scale fusion. c, artefact-aware normalization (AAN) aligns source feature statistics to the target template using anatomy-guided affine parameters.
  • Figure 2: Multi-modality MRI harmonization across vendors. Representative results for a, T1-weighted images, b, T2-weighted images, and c--d, diffusion-weighted images. The leftmost column displays original unharmonized images, followed by results harmonized to GE, Siemens, and Philips scanners, respectively.
  • Figure 3: Numerical evaluation of harmonization outcomes in structural MRI.a--b, visual inspection and histogram distribution comparison for multi-site harmonization. c, quantitative comparison of different harmonization methods. Multiple metrics were used for evaluation (*p$<$ 0.0001, two-sided $t$-test) and mean values are marked by bullseye symbols and standard errors by error bars.
  • Figure 4: Validation of diffusion MRI harmonization via parameter fitting consistency. The left and right panels show parameter maps used for qualitative and quantitative analysis of scanner- and channel-specific harmonization results, respectively. (B/S: scanner; 20/64ch: channel)