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

Noëlie Debs, Pejman Rasti, Léon Victor, Tae Hee Cho, Carole Frindel, David Rousseau

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number103579
JournalComputers in Biology and Medicine
Volume116
DOIs
Publication statusPublished - 2020 Jan
Externally publishedYes

Keywords

  • Arterial input function
  • Convolutional neural network
  • Lesion prediction
  • Perfusion magnetic resonance imaging
  • Simulation
  • Stroke

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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