Statistical tensor regression models for intracranial aneurysm growth prediction

Project Summary/Abstract Cerebral aneurysms occur in about 6% of the population and have a very high morbidity and mortality rate if they rupture. Fortunately, most unruptured intracranial aneurysms (UIAs) rarely cause symptoms and do not require an invasive treatment that may itself causes severe cerebrovascular disorders. However, it is very difficult to predict which UIAs will rupture. Recent evaluations of the hemodynamic features of UIAs, using 4D Flow MRI (4DF), have shown promising results that suggest specific hemodynamic variables may have a great impact on aneurysm growth or rupture. However, the clinical applicability of these hemodynamic variables in predicting UIA growth has not yet been realized due to the lack of robust methods for gathering them, and also for describing their relationship to UIA growth. To fill in the gaps, the proposed research aims to develop a comprehensive statistical and computational framework to predict: (a) the growth of UIAs at the 24th month (b) their growth trajectory over a five-year period. Our goal is to develop a statistical framework to improve the UIA growth prediction that, in turn, will improve the UIA rupture risk assessment. Toward achieving this goal, we will develop a unique tensor regression machine learning framework that will (1) enhance 4DF resolution (2) predict the UIA growth at the 24th month and (3) predict the longitudinal UIA growth trajectory. Successful completion of the proposed research will provide a comprehensive computational system that can assist physicians when deciding whether a patient with UIA needs treatment, or follow-up imaging, as well as the time interval for the surveillance imaging.