Deep learning prediction of stroke thrombus red blood cell content from multiparametric MRI.

Journal: Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
PMID: 36437762

Abstract

BACKGROUND AND PURPOSE: Thrombus red blood cell (RBC) content has been shown to be a significant factor influencing the efficacy of acute ischemic stroke treatment. In this study, our objective was to evaluate the ability of convolutional neural networks (CNNs) to predict ischemic stroke thrombus RBC content using multiparametric MR images.

Authors

  • Spencer D Christiansen
    Robarts Research Institute, Western University, London, Ontario, Canada.
  • Junmin Liu
    Robarts Research Institute, Western University, London, Ontario, Canada.
  • Maria Bres Bullrich
    Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
  • Manas Sharma
    The Digital Imaging Group of London, Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada.
  • Melfort Boulton
    Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
  • Sachin K Pandey
    Department of Medical Imaging, Western University, London, Ontario, Canada.
  • Luciano A Sposato
    Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
  • Maria Drangova
    Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.

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