Automated feature extraction across scanners for harmonization of diffusion MRI datasets

Abstract

Small variations in diffusion MRI metrics between subjects are ubiquitous due to differences in scanner hardware and are entangled in the genuine biological variability between subjects, including abnormality due to disease. 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 biological variability of the data. Results show that unpaired datasets from multiple scanners can be mapped to a scanner agnostic space while preserving genuine anatomical variability, reducing scanner effects and preserving simulated edema added to test datasets only.

Publication
ISMRM 2020

Reference:

Samuel St-Jean, Max A. Viergever and Alexander Leemans Automated feature extraction across scanners for harmonization of diffusion MRI datasets Proceedings of: ISMRM 2020

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