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Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke

Abstract : The problem of final tissue outcome prediction of acute ischemic stroke is assessed from physically realistic simulated perfusion magnetic resonance images. Different types of simulations with a focus on the arterial input function are discussed. These simulated perfusion magnetic resonance images are fed to convolutional neural network to predict real patients. Performances close to the state-of-the-art performances are obtained with a patient specific approach. This approach consists in training a model only from simulated images tuned to the arterial input function of a tested real patient. This demonstrates the added value of physically realistic simulated images to predict the final infarct from perfusion.
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https://hal.univ-angers.fr/hal-02428568
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Soumis le : lundi 7 mars 2022 - 13:58:46
Dernière modification le : lundi 14 novembre 2022 - 02:46:05
Archivage à long terme le : : mercredi 8 juin 2022 - 19:34:54

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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale 4.0 International License

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Noelie Debs, Pejman Rasti, Léon Victor, Tae-Hee Cho, Carole Frindel, et al.. Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Computers in Biology and Medicine, 2019, 116, pp.103579. ⟨10.1016/j.compbiomed.2019.103579⟩. ⟨hal-02428568⟩

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