Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.

Journal: Stroke
Published Date: Feb 1, 2020

Abstract

Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

Authors

  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Qinyang Shou
    From the Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles (K.W., Q.S., S.J.M., H.K., D.J.J.W.).
  • Samantha J Ma
    From the Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles (K.W., Q.S., S.J.M., H.K., D.J.J.W.).
  • David Liebeskind
  • Xin J Qiao
    Department of Radiology (X.J.Q., N.S.), University of California, Los Angeles.
  • Jeffrey Saver
    Department of Neurology (D.L., J.S., F.S.), University of California, Los Angeles.
  • Noriko Salamon
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Hosung Kim
    Laboratory of Neuro Imaging, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA. Electronic address: hosung.kim@loni.usc.edu.
  • Yannan Yu
  • Yuan Xie
  • Greg Zaharchuk
    Stanford University, Stanford CA 94305, USA.
  • Fabien Scalzo
  • Danny J J Wang
    From the Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles (K.W., Q.S., S.J.M., H.K., D.J.J.W.).

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