The inter-site variability of diffusion magnetic resonance imaging (dMRI) hinders the aggregation of dMRI data from multiple centers. This necessitates dMRI harmonization for removing non-biological site-effects. Recently, the emergence of high-resolution dMRI data across various connectome imaging studies allows the large-scale analysis of cortical micro-structure. Existing harmonization methods, however, perform poorly in the harmonization of dMRI data in cortical areas because they rely on image registration methods to factor out anatomical variations, which have known difficulty in aligning cortical folding patterns. To overcome this fundamental challenge in dMRI harmonization, we propose a framework of personalized dMRI harmonization on the cortical surface to improve the dMRI harmonization of gray matter by adaptively estimating the inter-site harmonization mappings. In our experiments, we demonstrate the effectiveness of the proposed method by applying it to harmonize dMRI across the Human Connectome Project (HCP) and the Lifespan Human Connectome Projects in Development (HCPD) studies and achieved much better performance in comparison with conventional methods based on image registration.
Keywords: Cortical Surface; Diffusion MRI; Harmonization.