Clinical-ComBAT: a diffusion-weighted MRI harmonization method for clinical applications
This addresses scanner-specific biases in clinical MRI data for neurodegenerative disease assessment, but it is incremental as it builds on the ComBAT method.
The authors tackled the problem of harmonizing diffusion-weighted MRI data across multiple clinical sites, which is limited by existing methods' assumptions, and proposed Clinical-ComBAT, showing improved alignment of diffusion metrics and enhanced applicability for normative modeling.
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.