Saturday, December 21, 2024
12/21/2024
SUMMARY The goal of this proposal is to investigate the dynamic changes in cerebrovascular morphology after acute ischemic stroke and reperfusion therapy and also use cerebrovascular morphological features to improve stroke outcome predictions. Stroke is among the leading causes of death and disability worldwide. Acute Ischemic stroke (AIS) constitutes approximately 90% of all strokes and is caused by a blockage or significant narrowing of the brain vessels. The resultant disruption in the blood flow patterns downstream of the occlusion could place the under-perfused regions of the brain at risk. Acute reperfusion therapies can restore the blood flow but vessel recanalization and improve outcomes when performed early after AIS. A well-connected collateral supply system, a redundant network of bypass vessels existing in the brain, is correlated with smaller final infarct size and more favorable outcomes. However, a detailed evaluation of the collateral supply remains challenging due to the small vessel size and network complexity. Timely diagnosis and treatment of AIS, as well as an accurate prediction of response to reperfusion therapies, risk of major complications, and long-term outcomes, are pivotal to patients, families, and providers to guide treatment pathways. However, accurate predictive models are lacking despite efforts to use a large number of clinical and imaging biomarkers. Brain vascular morphology and geometrical features have been shown to correlate with the development of cerebrovascular disorders. We recently developed an automatic algorithm that can extract the brain’s vascular morphologic and geometric features from the commonly used MR and CT angiograms in healthy subjects and AIS patients. We propose developing further and validating this algorithm to automatically extract complex cerebrovascular morphology in real-time. We also propose developing predictor models to accurately use the cerebrovascular morphologic features, collateral index, and other clinical and imaging information collected at admission to predict major complications and long-term outcomes after AIS.
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