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

作者：Huang Shiliang; Shen Qiang; Duong Timothy Q^*

来源：Journal of Cerebral Blood Flow and Metabolism, 2010, 30(9): 1661-1670.

DOI：10.1038/jcbfm.2010.56

• ANN
• DWI
• ischemic penumbra
• perfusion-diffusion mismatch
• predictive model
• PWI

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) CBF + ADC 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. Journal of Cerebral Blood Flow & Metabolism (2010) 30, 1661-1670; doi:10.1038/jcbfm.2010.56; published online 28 April 2010

• 出版日期2010-9