Abstract:
Stroke is the leading cause of death after heart disease and cancer and the number one
cause of long-term disability in America. About 80% of hemorrhagic stroke are produced
by the rupture of cerebral aneurysms. Surgical clipping and coil embolization are the
most common methods of treating these aneurysms. However, both these treatments have
some limitations for wide neck aneurysms. Recently, there has been an increased interest
in the use of stents as flow diverters. With an effective design, the blood flowing into
the aneurysm can be disrupted thereby promoting intra-aneurysmal thrombus formation
and thus preventing rupture. Modeling blood flow around these endovascular devices in
intracranial aneurysms is important for designing better devices and to personalize and
optimize endovascular stenting procedures in the treatment of these aneurysms. Numerous
studies have been conducted in the past using animal models, patient-specific in-vitro models
and idealized computational models. Nevertheless, all of them have significant limitations.
The main disadvantage of using animal models is that they fail to replicate the variable
anatomy of diseased human arteries. And the problem with in-vitro models is that they
are not suitable for large population studies. Patient-specific image-based numerical models
have demonstrated to be a fast, reliable and inexpensive way of simulating blood flow inside
these aneurysms. Moreover, studies using these models have the potential to replicate the
exact anatomy of specific patients in order to connect specific hemodynamic factors to clinical
events. Furthermore, large patient population study is also possible. A methodology was
previously developed in the CFD Lab, GMU, to conduct patient-specific studies but without
any endovascular devices. It includes image processing and segmentation algorithms,
unstructured 3D grid generation, finite element solver for Navier-Stokes equations, rheological
models and visualization techniques. However, the main difficulty in using these models
for endovascular stent simulations lies in the generation of acceptable computational grids
inside the blood vessels and around these devices. An adaptive embedded gridding technique
originally developed for fluid-structure interaction problems at the CFD lab, GMU,
tremendously simplifies this impediment. In this doctoral thesis the computational modeling
pipeline has been extended to model patient-specific hemodynamics of stented cerebral
aneurysms. The methodology was evaluated and demonstrated with a number of imagebased
models and different stent aneurysms and applied to the study of the effects of stents
on the flow in aneurysms and side arterial branches.