Harmonization of diffusion MRI on healthy subjects using NeuroCombat and LongCombat: a B-Q MINDED brain intra- and inter-scanner study

Abstract

The structural integrity of brain white matter is commonly assessed using quantitative diffusion metric maps derived from diffusion MRI (dMRI) data. However, in multi-site, multi-scanner studies, variability across and within scanners presents challenges in ensuring consistent and comparable diffusion evaluations. This study assesses the effectiveness of ComBat-based harmonization algorithms in reducing intra- and inter-scanner variability in diffusion metrics such as FA, MD, AD, RD, MK, AK, and RK. Utilizing the B-Q MINDED dataset, which includes anatomical and dMRI data from 38 healthy adults scanned twice on two 3T MRI scanners (Siemens Healthineers PrismaFit and Siemens Healthineers Skyra) on the same day, we evaluated the NeuroCombat and LongCombat algorithms for harmonizing diffusion metrics. These harmonization methods effectively minimized both intra- and inter-scanner variability, highlighting their potential to improve consistency in multi-scanner diffusion analysis. Our findings suggest that NeuroCombat and LongCombat are recommended for harmonizing dMRI metric maps in clinical studies. Additionally, both algorithms applied in either ROI-based or voxel-wise configurations, significantly reduced variability, achieving levels comparable to scan-rescan variability intra-scanner. Nonetheless, the choice of harmonization algorithm and implementation should be tailored to the research question at hand. Moreover, the significant intra- and inter-subject variability on non-harmonized diffusion data demonstrated in this study reinforces the importance of harmonization strategies that address any sources of variability. By minimizing scanner-specific biases, the NeuroCombat and LongCombat harmonization algorithms enhance the reliability of diffusion biomarkers, enabling large-scale studies and more informed clinical decision-making in brain-related conditions.