Clinical-ComBAT: a diffusion-weighted MRI harmonization method for clinical applications

TL;DR

Clinical-ComBAT is a novel harmonization method for diffusion-weighted MRI data that addresses clinical variability, enabling better multi-site data integration and analysis in real-world settings.

Contribution

It introduces a non-linear, site-specific harmonization approach tailored for clinical environments, overcoming limitations of existing linear methods like ComBAT.

Findings

Improved alignment of diffusion metrics across sites.

Enhanced applicability for normative modeling in clinical data.

Effective on both simulated and real datasets.

Abstract

Diffusion-weighted magnetic resonance imaging (DW-MRI) derived scalar maps are effective for assessing neurodegenerative diseases and microstructural properties of white matter in large number of brain conditions. However, DW-MRI inherently limits the combination of data from multiple acquisition sites without harmonization to mitigate scanner-specific biases. While the widely used ComBAT method reduces site effects in research, its reliance on linear covariate relationships, homogeneous populations, fixed site numbers, and well populated sites constrains its clinical use. To overcome these limitations, we propose Clinical-ComBAT, a method designed for real-world clinical scenarios. Clinical-ComBAT harmonizes each site independently, enabling flexibility as new data and clinics are introduced. It incorporates a non-linear polynomial data model, site-specific harmonization referenced to a normative site, and variance priors adaptable to small cohorts. It further includes hyperparameter tuning and a goodness-of-fit metric for harmonization assessment. We demonstrate its effectiveness on simulated and real data, showing improved alignment of diffusion metrics and enhanced applicability for normative modeling.