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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
Contributeur : David Rousseau <>
Soumis le : lundi 6 janvier 2020 - 09:42:57
Dernière modification le : jeudi 19 mars 2020 - 17:16:33

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Noelie Debs, Pejman Rasti, Victor Leong, 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, Elsevier, 2019, 116, pp.103579. ⟨10.1016/j.compbiomed.2019.103579⟩. ⟨hal-02428568⟩

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