Image-based computational hemodynamics methods and their application for the analysis of blood flow past endovascular devices

Abstract

Knowledge of the hemodynamic conditions in intracranial aneurysms before and after endovascular treatment is important to better understand the mechanisms responsible for aneurysm growth and rupture, and to optimize and personalize the therapies. Unfortunately, there are no reliable imaging techniques for in vivo quantification of blood flow patterns in cerebral aneurysms. Patient-specific, image-based computational models provide an attractive alternative since they can handle any vascular geometry and physiologic flow condition. However, computational modeling of the hemodynamics in cerebral aneurysms after their endovascular treatment is a challenging problem because of the high degree of geometric complexity required to represent and mesh the vascular anatomy and the endovascular devices simultaneously. This paper describes an image-based methodology for constructing patient-specific vascular computational fluid dynamics models and an adaptive grid embedding technique to simulate blood flows around endovascular devices. The methodology is illustrated with several examples ranging from idealized vascular models to patient-specific models of cerebral aneurysms after deployment of stents and coils. These techniques have the potential to be used to select the best therapeutic option for a particular individual and to optimize the design of endovascular devices on a patient-specific basis.