Unsupervised learning for MRI cross-scanner harmonization

Harmonization is necessary for large-scale multi-site neuroimaging studies to reduce the variations due to factors such as image acquisition, imaging devices, and acquisition protocols. This so-called scanner effect significantly impacts multivariate analysis and the development of computational predictive models using MRI. Our approach utilized an unsupervised learning based model to build a mapping between MR data acquired from two different scanners. Results illustrate the potential of unsupervised deep learning algorithms to harmonize MRI data, as well as to improve downstream tasks by applying the harmonization.