Harmonization of diffusion MRI data sets with adaptive dictionary learning.

ABSTRACT: Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability of the data. Our harmonization algorithm does not require paired training data sets, nor spatial registration or matching spatial resolution. Overcomplete dictionaries are learned iteratively from all data sets at

SUBMITTER: St-Jean S 

PROVIDER: S-EPMC7555079 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature