Stroke is the fifth leading cause of death in the United States. Subarachnoid hemorrhage (SAH) is a type of stroke often caused by the spontaneous rupture of a cerebral aneurysm. About 30% of the SAH patients develop delayed cerebral ischemia (DCI) a serious secondary complication with devastating impact. Cerebral vasospasm is one of the major precursors of DCI. Predicting the risk of vasospasm would enable better treatment and improved outcomes. Our overarching goal is to find a brain vasculature representation that can be used to find predictive image-based biomarkers. We propose a new methodology that leverages sparse dictionary learning and covariance-based features in order to encode the whole vessel structure in a vector of fixed size. Using 3D brain angiograms, we use this vasculature representation to train a logistic regression model to predict the occurrence of cerebral vasospasm with an area under the ROC curve of 0.93.
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