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Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics

Alberto De Luca1, Suheyla Cetin Karayumak2, Alexander Leemans3

  • 1Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands.

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Summary
This summary is machine-generated.

Rotation invariant spherical harmonics (RISH) harmonization effectively removes cross-site differences in multi-shell diffusion magnetic resonance imaging (dMRI) metrics like DKI and NODDI. This technique preserves longitudinal effects, enabling robust multicenter brain studies.

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Area of Science:

  • Neuroimaging
  • Diffusion Magnetic Resonance Imaging (dMRI)
  • Computational Neuroscience

Background:

  • Multi-shell dMRI enables advanced models like Diffusion Kurtosis Imaging (DKI) and Neurite Orientation Dispersion and Density Imaging (NODDI) for in-vivo brain studies.
  • Scanner and site variations in DKI and NODDI metrics hinder multicenter studies, limiting sample sizes and robust analysis.
  • Existing harmonization methods require repeated application for each dMRI metric.

Purpose of the Study:

  • To investigate the efficacy of Rotation Invariant Spherical Harmonics (RISH) features harmonization in mitigating cross-site differences for multi-shell dMRI data.
  • To assess whether RISH harmonization preserves longitudinal effects in DKI and NODDI metrics.
  • To evaluate the generalizability of RISH harmonization to advanced dMRI models beyond Diffusion Tensor Imaging (DTI).

Main Methods:

  • Acquisition of two-shell dMRI data from 46 subjects across 3 imaging sites with a longitudinal design (up to 3 time points).
  • Derivation of DKI and NODDI metrics before and after RISH features harmonization.
  • Comparison of metrics at whole-brain and voxel levels to evaluate cross-site differences and longitudinal effects.

Main Results:

  • RISH harmonization effectively removed statistically significant between-site differences in DKI metrics (FA, MD, MK) and NODDI metrics, particularly in white matter.
  • The RISH method maintained the magnitude and variance of longitudinal changes across all considered metrics.
  • Prominent between-site differences observed in frontal and posterior brain regions were successfully mitigated by RISH harmonization.

Conclusions:

  • RISH feature-based harmonization is a robust method for removing cross-site variability in multi-shell dMRI data for DKI and NODDI analyses.
  • This technique facilitates the implementation of large-scale, multicenter dMRI studies by ensuring data consistency across different scanners and sites.
  • RISH harmonization preserves the integrity of longitudinal data, allowing for reliable tracking of brain changes over time.