Motivation: Multi-site MRI data often exhibit non-biological variations due to differences in scanner vendors, field strength, and protocols, hindering downstream image analysis. Goal(s): We aim to develop a novel framework for brain MRI harmonization that does not require paired training data from traveling subjects, and can harmonize multiple MR sequences (T1, T2, T2-FLAIR). Approach: Our framework first employs a class-conditioned diffusion model as a coarse harmonizer to unify multi-site MRIs into a common domain, eliminating site-specific image style characteristics. In the second stage, we fine-tune this model to translate the coarsely harmonized MRIs into a specified target style, guided by a style extractor. Impact: By eliminating non-biological imaging variations from various acquisition sites, our framework allows researchers to utilize multi-site data more effectively, facilitating more robust and generalizable analysis. This will enable large-scale multisite longitudinal studies and increase usable data to improve statistical power.
Wu et al. (Tue,) studied this question.
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