Annals of Biomedical Engineering

, Volume 40, Issue 10, pp 2177–2187

Regional Prediction of Tissue Fate in Acute Ischemic Stroke

  • Fabien Scalzo
  • Qing Hao
  • Jeffry R. Alger
  • Xiao Hu
  • David S. Liebeskind
Article

DOI: 10.1007/s10439-012-0591-7

Cite this article as:
Scalzo, F., Hao, Q., Alger, J.R. et al. Ann Biomed Eng (2012) 40: 2177. doi:10.1007/s10439-012-0591-7

Abstract

Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model of tissue fate that combines regional imaging features available after onset. A key component is the use of cuboids randomly sampled during the learning process. Models trained with time-to-maximum feature (Tmax) computed from perfusion weighted images (PWI) are compared to the ones obtained from the apparent diffusion coefficient (ADC). The prediction task is formalized as a regression problem where the inputs are the local cuboids extracted from Tmax or ADC images at onset, and the output is the segmented FLAIR intensity of the tissue 4 days after intervention. Experiments on 25 acute stroke patients demonstrate the effectiveness of the proposed approach in predicting tissue fate. Results on our dataset show the superiority of the regional model vs. a single-voxel-based approach, indicate that PWI regional models outperform ADC models, and demonstrates that a nonlinear regression model significantly improves the results in comparison to a linear model.

Keywords

Brain ischemiaAcute stroke diagnosticPredictionStrokeRegionalPerfusion weighted imagesLesion growth

Copyright information

© Biomedical Engineering Society 2012

Authors and Affiliations

  • Fabien Scalzo
    • 1
  • Qing Hao
    • 1
  • Jeffry R. Alger
    • 1
  • Xiao Hu
    • 1
  • David S. Liebeskind
    • 1
  1. 1.Department of Neurology & Neurosurgery, Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA