Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Data Harmonization, Bloch equation
Motivation: The MR scanner effect in a multi-site dataset can affect bias in statistical analysis or reduce generality in deep neural networks.
Goal(s): We aim to suggest a MR physics-informed harmonization framework (PhyCHarm) that generates consistent quantitative maps and harmonized T1w images.
Approach: We introduce a Quantitative Maps Generator and a Harmonization Network to be trained with a constraint loss based on a signal equation.
Results: PhyCHarm shows the highest evaluation scores in both networks and consistent segmentation accuracy in the downstream task (FSL FAST GM and WM segmentation).
Impact: PhyCHarm works based on the Bloch equation. PhyCHarm enables us to reduce scanner effects efficiently in the dataset before conducting test/retest, longitudinal, or multi-site studies. It can be helpful to ensure deep neural networks' generality.
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