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

研究成果: Article査読

2 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号103579
ジャーナルComputers in Biology and Medicine
116
DOI
出版ステータスPublished - 2020 1
外部発表はい

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

フィンガープリント 「Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル