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Tissue Fate Prediction in Acute Ischemic Stroke Using Cuboid Models

  • Fabien Scalzo
  • Qing Hao
  • Jeffrey R. Alger
  • Xiao Hu
  • David S. Liebeskind
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6454)

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 that combines tissue information available immediately after onset, measured using fluid attenuated inversion recovery (FLAIR), with multi-modal perfusion features (Tmax, MTT, and TTP) to infer the likely outcome of the tissue. A key component is the use of randomly extracted, overlapping, cuboids (i.e. rectangular volumes) whose size is automatically determined during learning. The prediction problem is formalized into a nonlinear spectral regression framework where the inputs are the local, multi-modal cuboids extracted from FLAIR and perfusion images at onset, and where the output is the local FLAIR intensity of the tissue 4 days after intervention. Experiments on 7 stroke patients demonstrate the effectiveness of our approach in predicting tissue fate and its superiority to linear models that are conventionally used.

Keywords

Acute Ischemic Stroke Cerebral Blood Volume Mean Transit Time Flair Image Perfusion Weight Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fabien Scalzo
    • 1
    • 2
  • Qing Hao
    • 1
  • Jeffrey R. Alger
    • 1
  • Xiao Hu
    • 2
  • David S. Liebeskind
    • 1
  1. 1.Dept. of NeurologyUniversity of CaliforniaLos AngelesUSA
  2. 2.Dept. of Neurosurgery, NSDLUniversity of CaliforniaLos AngelesUSA

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