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Multi-Site Harmonization of Diffusion MRI Data via Method of Moments

Khoi Minh Huynh, Geng Chen, Ye Wu

    IEEE Transactions on Medical Imaging
    |January 25, 2019
    PubMed

    Insights

    This study introduces a new method to harmonize diffusion MRI data, reducing site-specific variations. This harmonization improves the reliability of brain imaging across different centers and scanners.

    Area of Science:

    • Neuroimaging
    • Biomedical Engineering
    • Medical Physics

    Background:

    • Diffusion MRI is crucial for studying brain microstructure non-invasively.
    • Multi-center studies enhance diffusion MRI data analysis but face challenges due to site-specific variations.
    • Scanner vendors, protocols, and reconstruction algorithms introduce data incompatibility across imaging centers.

    Purpose of the Study:

    • To introduce a model-free method for direct harmonization of diffusion MRI data.
    • To reduce site-specific variations in diffusion MRI data without fitting diffusion models.
    • To enable the definition of well-behaved, invertible, smooth, and injective mapping functions.

    Main Methods:

    • A model-free harmonization method based on the method of moments.
    • Direct harmonization of diffusion-attenuated signal, bypassing diffusion model fitting.
    • Utilizing mapping functions with defined properties like invertibility and smoothness.

    Main Results:

    • Reduced variations in diffusion scalars for traveling human phantoms: fractional anisotropy (FA) and mean diffusivity decreased from 1%-3% to <0.9%.
    • Generalized FA variations reduced from 1%-2.5% to 0.3%-1.2%.
    • Preserved individual differences and enhanced across-site consistency in tractography and white matter connectivity.

    Conclusions:

    • The proposed method effectively harmonizes diffusion MRI data, reducing site-specific variations.
    • This approach improves the reliability and consistency of multi-center diffusion MRI studies.
    • The method enhances the utility of diffusion MRI for detecting subtle neurodevelopmental, aging, and disease-related changes.

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