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Abstract #1769

Improving Deep Learning MRI Super-Resolution for Quantitative Susceptibility Mapping

Antoine Moevus1,2, Mathieu Dehaes2,3,4, and Benjamin De Leener1,2,4
1Departement of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada, 2Institute of Biomedical Engineering, University of Montreal, Montréal, QC, Canada, 3Department of Radiology, Radio-oncology, and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada, 4Research Center, Ste-Justine Hospital University Centre, Montreal, QC, Canada

In this preliminary work, we are exploring the application of deep learning (DL) super-resolution techniques to improve quantitative susceptibility maps (QSM). We trained a light deep learning neural network on the QSM data from the AHEAD dataset. We studied different variants of the mean squared error (MSE) as loss functions and two different training strategies : cyclic learning rate and an adaptive learning rate. We found that the cyclic learning rate yielded better results in general if correctly optimized with the learning rate finder algorithm.

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