? Our research aims to predict tissue outcome in acute human ischemic stroke using MRI and link tissue outcome to clinical outcome. Early prediction of tissue and clinical outcome can aid in the clinical management of acute stroke as well as assist in the development of novel therapeutics. MRI, which provides both anatomic and physiologic information, may best be able to provide markers of prognosis. However, there remain a number of unresolved issues in stroke MRI due to the high variance in the correlation between imaging and outcome, namely the interpretation of the diffusion/perfusion (DWI/PWI) mismatch (particularly as a marker for tissue salvageability) and the best way to measure cerebral perfusion. Previously, we demonstrated that DWI/PWI can be used to predict tissue outcome on a voxel-by-voxel basis with >65% sensitivity and >80% specificity through the creation of 'risk maps' depicting probabilistic outcome. We developed these risk maps using retrospective stroke MRI data to generate coefficients by which to optimally weigh individual DWI/PWI parameters. Here, we propose to extend this work by refining our predictive models of tissue outcome, translating tissue outcome into clinical outcome, improving the input to our predictive models through better PWI calculation methods and applying our models clinically. Specifically, we will: (1) Test our hypothesis that the variance in our tissue outcome predictions will be reduced by accounting for stroke mechanism and clinical variables (age, gender, time to MRI). Additionally, we will test an atlas-based approach to translate a prediction of tissue outcome to one of clinical outcome. (2) Develop and test PWI calculation methods that we hypothesize will overcome the problems of delay of the contrast bolus inherent in the standard technique. We will also evaluate a novel MRI marker derived from the 'noise' in PWI data that turns out to be not random. Since disruption of perfusion is the key event in stroke, improvements in PWl should directly impact tissue outcome prediction. (3) Identify tissue at risk of infarction and predict its clinical impact in prospective pilot data from routinely-treated patients. We will then pilot the evaluation of therapeutics by comparing, in the same patient on a per-voxel basis, the predicted infarct and clinical outcome from pre-treatment data with the actual infarct and clinical outcome on post-treatment follow-up images, We hypothesize such an approach could markedly reduce the number of patients required to determine efficacy. ? ?

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Diagnostic Radiology Study Section (RNM)
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Conwit, Robin
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Massachusetts General Hospital
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