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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