Advanced MR and CT Imaging for Understanding Acute Stroke Evolution and Predictin

描述（由申请人提供）：

DESCRIPTION (provided by applicant): Ischemic stroke continues to be one of leading sources of mortality and morbidity in the U.S. For clinicians who treat acute stroke victims, it is challenging to reliably discriminate salvageable from irreversibly injured tissue as well as to predict which patients are most likely to respond favorably to reperfusion therapy and which patients should have treatment withheld to avoid severe complications. Similar to the diagnostic parameters needed for the workup of acute stroke patients, the specific aims of this project will focus on: (1) the detection of irreversibly damaged tissue (stroke core); (2) the identification of potentially salvageable tissue (at-risk tissue); (3) the detection of leakage from the vascular into the interstitial space and prediction f hemorrhage (hemorrhagic transformation); and (4) the involvement of specific brain regions and their respective functions when assessing stroke severity and predicting outcome. Note that the difference between stroke core and at-risk tissue is often referred to as penumbra. With these four aims in mind, the overarching aim of this project is to collect CT and MR imaging data over a period of 4 years from 60 acute stroke victims (whose scans are performed �30min apart) and to prospectively compare for the first time at such narrow time interval the ability of CT and MR to identify irreversibly damaged tissue, at-risk-tissue, and tissue that will most likely transform into (symptomatic) hemorrhage. This project will also assess each modality's ability to identify patients who will respond favorably to clot lysis/retrieval (a.k.a., reperfusion therapy) as well a those who will not benefit or who might actually sustain harm from the complications of reperfusion. Other project deliverables include improved MRI acquisition methods; optimized scan protocols for CT and MR in acute stroke; assessment of the role of iterative CT reconstructions in CT perfusion; new and refined outcome predictors; and a fully automated software that can be deployed at stroke centers nationwide (e.g., via NITRC). In summary, the project combines a sophisticated trial design with innovative imaging methods, medical image processing algorithms, and long-standing clinical and technical expertise in stroke imaging. This unique combination will produce invaluable clinical/imaging data from acute stroke patients; new clinical profiles and tools for patient triage; and a quantifiable assessment of the diagnostic strengths and weaknesses of advanced CT and MR methods as well as their clinical yield in acute stroke.