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Abstract #5185

Deep-learning and feature selection for fast, quantitative and specific CEST imaging

Cecile Maguin1 and Julien Flament1
1Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-roses, France

Synopsis

Keywords: Machine Learning/Artificial Intelligence, CEST & MT, Feature selectionWe propose an artificial neural network combined with a feature selection scheme for fast, quantitative CEST imaging, designed for specificity. Our NN was evaluated on glucose phantoms and glutamate/glucose mixed phantoms and goes beyond performances of classical fittings approaches.

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Keywords