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Personalized dMRI Harmonization on Cortical Surface.

Yihao Xia1,2, Yonggang Shi1,2

  • 1Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA.

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|March 19, 2024
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Summary

Diffusion MRI harmonization is crucial for multi-center studies. A novel surface-based framework improves harmonization of cortical micro-structure data, outperforming traditional methods.

Keywords:
Cortical SurfaceDiffusion MRIHarmonization

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Inter-site variability in diffusion MRI (dMRI) data impedes multi-center aggregation.
  • Harmonization is essential to remove non-biological site-effects in dMRI.
  • High-resolution dMRI enables large-scale analysis of cortical micro-structure.

Purpose of the Study:

  • To develop a novel framework for personalized dMRI harmonization on the cortical surface.
  • To improve dMRI harmonization in gray matter by adaptively estimating inter-site mappings.
  • To overcome limitations of existing registration-based harmonization methods in cortical areas.

Main Methods:

  • Proposed a framework for personalized dMRI harmonization on the cortical surface.
  • Employed adaptive estimation of inter-site harmonization mappings.
  • Applied the method to harmonize dMRI data across the Human Connectome Project (HCP) and HCP-Development (HCPD) studies.

Main Results:

  • The proposed method demonstrated superior performance in harmonizing dMRI data.
  • Achieved significantly better harmonization compared to conventional registration-based methods.
  • Effectively addressed challenges in aligning cortical folding patterns for harmonization.

Conclusions:

  • The personalized, surface-based harmonization framework effectively improves dMRI harmonization in cortical gray matter.
  • This approach offers a significant advancement over existing methods, particularly for large-scale connectome studies.
  • Enables more robust aggregation and analysis of multi-center dMRI datasets.