MRI based diffusion and perfusion predictive model to estimate stroke evolution

Rose, Stephen E., Chalk, Jonathan B., Griffin, Mark P., Janke, Andrew L., Chen, Fang, Mclachlan, Geoffrey J., Peel, David, Zelaya, Fernando O., Markus, Hugh S., Jones, Derek K., Simmons, Andrew, O'Sullivan, Michael, Jarosz, Jo M., Strugnell, Wendy and Doddrell, David M. (2001) MRI based diffusion and perfusion predictive model to estimate stroke evolution. Magnetic Resonance Imaging, 19 8: 1043-1053. doi:10.1016/S0730-725X(01)00435-0

Author Rose, Stephen E.
Chalk, Jonathan B.
Griffin, Mark P.
Janke, Andrew L.
Chen, Fang
Mclachlan, Geoffrey J.
Peel, David
Zelaya, Fernando O.
Markus, Hugh S.
Jones, Derek K.
Simmons, Andrew
O'Sullivan, Michael
Jarosz, Jo M.
Strugnell, Wendy
Doddrell, David M.
Title MRI based diffusion and perfusion predictive model to estimate stroke evolution
Journal name Magnetic Resonance Imaging   Check publisher's open access policy
ISSN 0730-725X
Publication date 2001-10
Sub-type Article (original research)
DOI 10.1016/S0730-725X(01)00435-0
Volume 19
Issue 8
Start page 1043
End page 1053
Total pages 11
Editor J. Gore
Place of publication United States
Publisher Elsevier Science
Collection year 2001
Language eng
Subject C1
230204 Applied Statistics
780101 Mathematical sciences
Abstract In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/-0.19 and 1.20 +/-0.36. For recovered tissue the mean values were 0.99 +/-0.25 and 1.87 +/-0.71, respectively. There was a significant difference between these two regions for both measures (P<0.003 and p<0.001, respectively). Mean absolute measures of CBF (ml/100g/min) and CBV (ml/100g) for the total infarcted territory were 33.9 +/-9.7 and 4.2 +/-1.9. For recovered MTT tissue, the mean values were 41.5 +/-7.2 and 5.3 +/-1.2, respectively. A significant difference was also found for these regions (p<0.009 and p<0.036, respectively). The mean measures of sensitivity, specificity, positive and negative predictive values for modeling infarct evolution for the validation patient data were 0.72 +/-0.05, 0.97 +/-0.02, 0.68 +/-0.07 and 0.97 +/-0.02. We propose that this automated strategy may allow possible guided therapeutic intervention to stroke patients and evaluation of efficacy of novel stroke compounds in clinical drug trials. (C) 2001 Elsevier Science Inc. All rights reserved.
Keyword Radiology, Nuclear Medicine & Medical Imaging
Acute Stroke
Magnetic Resonance Imaging
Diffusion And Perfusion
Cerebral Blood-flow
High-resolution Measurement
Imaging Bolus Tracking
Ischemic Stroke
Weighted Mri
Hyperacute Stroke
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
School of Medicine Publications
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 23 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 31 times in Scopus Article | Citations
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Created: Tue, 14 Aug 2007, 16:03:54 EST