Artificial neural network prediction of ischemic tissue fate in acute stroke imaging

Shiliang Huang, Qiang Shen, Timothy Q. Duong

Research output: Contribution to journalArticle

33 Scopus citations

Abstract

Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin-spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBFADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis.

Original languageEnglish (US)
Pages (from-to)1661-1670
Number of pages10
JournalJournal of Cerebral Blood Flow and Metabolism
Volume30
Issue number9
DOIs
StatePublished - Sep 1 2010

Keywords

  • ANN
  • DWI
  • PWI
  • ischemic penumbra
  • perfusiondiffusion mismatch
  • predictive model

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

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