Intracranial aneurysms are pathological dilations of the arteries in the brain, whose rupture is often fatal. Surgery and endovascular embolization are the two most common methods of treatment. Surgery involves opening a portion of the skull and placing metallic clips at the aneurysm neck thereby preventing blood flow into the aneurysm. In the case of embolization, a catheter is used to pack platinum coils in the aneurysm reducing the inflow and promoting thrombus formation. Due to its less invasive approach endovascular embolization is preferred over surgery. Nevertheless this approach also has some serious aftereffects. Coil compaction followed by the re-growth or the formation of the secondary aneurysm is the most common problem. The endovascular embolization approach also has a serious shortcoming that the coils alone cannot be used to block every type of aneurysm. Wide neck or fusiform aneurysms are coiled with the help of stents. Recent studies show that stent, which is a hollow cylindrical mesh, can be successfully used to limit the flow of blood into the aneurysm. However these studies have been conducted using idealized in-vitro and numerical models. Studies conducted using patient-specific models generated from medical images will provide a more realistic approach to computationally investigate the effects of stents on intra-aneurysmal flow patterns. However generation of computational grids inside the parent vessel and around these stents is a challenging task. In this paper an algorithm to map the stent to a patient-specific vascular model and an adaptive unstructured embedded gridding technique to model flow around stents are presented. Finally these techniques are demonstrated on patient-specific cases to prove their feasibility.

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