F. Scalzo, J. R. Alger, X. Hu, J. L. Saver, K. A. Dani, K. W. Muir, A. M. Demchuk, S. B. Coutts, M. Luby, S. Warach, D. S. Liebeskind, STIR VISTA Imaging Investigators



Publication year



Magnetic Resonance Imaging

Periodical Number






Author Address

Scalzo, F Univ Calif Los Angeles, Dept Neurol, Los Angeles, CA 90095 USA Univ Calif Los Angeles, Dept Neurol, Los Angeles, CA 90095 USA Univ Calif Los Angeles, Neurosurg Neural Syst & Dynam Lab NSDL, Los Angeles, CA USA Univ Glasgow, Inst Neurosci & Psychol, Glasgow G12 8QQ, Lanark, Scotland Univ Calgary, Hotchkiss Brain Inst, Dept Radiol, Calgary, AB, Canada Univ Calgary, Hotchkiss Brain Inst, Dept Clin Neurosci, Calgary, AB, Canada NIH, Sect Stroke Diagnost & Therapeut, Bethesda, MD 20892 USA

Full version

Permeability images derived from magnetic resonance (MR) perfusion images are sensitive to blood brain barrier derangement of the brain tissue and have been shown to correlate with subsequent development of hemorrhagic transformation (HT) in acute ischemic stroke. This paper presents a multi-center retrospective study that evaluates the predictive power in terms of HT of six permeability MRI measures including contrast slope (CS), final contrast (FC), maximum peak bolus concentration (MPB), peak bolus area (PB), relative recirculation (rR), and percentage recovery (%R). Dynamic T2*-weighted perfusion MR images were collected from 263 acute ischemic stroke patients from four medical centers. An essential aspect of this study is to exploit a classifier-based framework to automatically identify predictive patterns in the overall intensity distribution of the permeability maps. The model is based on normalized intensity histograms that are used as input features to the predictive model. Linear and nonlinear predictive models are evaluated using a cross-validation to measure generalization power on new patients and a comparative analysis is provided for the different types of parameters. Results demonstrate that perfusion imaging in acute ischemic stroke can predict HT with an average accuracy of more than 85% using a predictive model based on a nonlinear regression model. Results also indicate that the permeability feature based on the percentage of recovery performs significantly better than the other features. This novel model may be used to refine treatment decisions in acute stroke. (c) 2013 Elsevier Inc. All rights reserved.