Dr. Juan R. Cebral of the Computational and Data Sciences Department has been studying blood flow patterns in cerebral aneurysms in collaboration with Dr. Christopher Putman from the Interventional Neuroradiology Unit of Inova Fairfax Hospital. Three Ph.D. students in the Computational and Data Sciences Department are involved in this research and make extensive use of the GMU supercomputing resources: Marcelo Castro, Fernando Mut and Sunil Appanaboyina.

The research focuses on the use of image-based patient-specific computational models of cerebral aneurysms to better understand the disease progression in order to improve patient evaluation and treatment. "Patient-specific" models face the daunting task of creating an individualized model for each patient based on real-time imaging. This research makes heavy use of computational resources since it requires running a large number of cases for correlation of hemodynamic characteristics and clinical events, as well as to characterize the sensitivity of the computational models to different modeling and physiologic parameters. Typical simulations involving unstructured grids between 2 and 3 million tetrahedral elements are run for two cardiac cycles on 16 processors of the SGI Altix system of GMU in approximately 10 to 20 hours. An even more powerful system would be highly valuable in reducing this time.

Cerebral Aneurysms

Cerebrovascular diseases are leading causes of morbidity and mortality in the Western World. Stroke is the third cause of death after cancer and heart disease, and is the single leading cause of nursing home admission, and one of the fastest growing health care costs. Strokes can either be ischemic (due to lack of blood flow or oxygen to the brain) or hemorrhagic (bleeding into the brain). Ischemic strokes are caused by an interruption of blood flow to a portion of the brain typically due to blockages of blood vessels. The most common source of ischemic strokes is carotid artery atherosclerosis. Hemorrhagic strokes are most commonly due to the rupture of a cerebral aneurysm. Cerebral aneurysms are pathological dilatations of the cerebral arteries commonly found at arterial bifurcations of the so called circle of Willis at the base of the brain.

Improvements of neuroradiological techniques have resulted in more frequent detection of unruptured aneurysms. Because prognosis of subarachnoid hemorrhage is still poor, preventive treatment is increasingly considered. However, the best patient care would be to treat only those patients who are likely to rupture. This requires a better understanding of the process of aneurysm formation, progression, and rupture. These mechanisms are not well understood but previous studies have identified the major factors involved in these processes: a) arterial hemodynamics, b) arterial wall biomechanics, c) mechanobiology, and d) intracranial environment. These studies included observations made in in vitro models, idealized computational models and from clinical experience. Each of these approaches has had significant limitations in connecting the hemodynamic factors studied to clinical events. Thus, these mechanisms have not been studied using the in vivo hemodynamics of cerebral aneurysms.
Hemodynamics is thought to play a critical role in the progression and rupture of intracranial aneurysms. However, no reliable methods exist for measuring intra-aneurysmal blood flow patterns in vivo. In contrast, computational models are attractive for studying the intra-aneurysmal flow patterns because, when combined with medical imaging methods, they can realistically represent the in vivo patient-specific hemodynamic conditions.

The objective of the research is to better understand the mechanisms of aneurysm formation, growth and rupture in order to improve patient evaluations (risk of rupture assessment) and their endovascular treatment using coils and stents.

Hemodynamics and Rupture

The research team has conducted a pilot clinical study of the association between intraaneurysmal hemodynamic characteristics derived from patient-specific computational fluid dynamic models and the clinical event of cerebral aneurysm rupture. For this purpose, over a hundred patient-specific models of cerebral aneurysms were constructed from 3D rotational angiography images. Computational fluid dynamics simulations were performed under pulsatile flow conditions measured on a normal subject. The aneurysms were classified into different categories depending on the complexity and stability of the flow pattern, the location and size of the flow impingement region, and the size of the inflow jet. These hemodynamic features were then analyzed for associations with history of rupture. In this work, a large variety of flow patterns was observed. Seventy-two percent of ruptured aneurysms had complex or unstable flow patterns, 80% had small impingement regions and 76% had small jet sizes. Conversely, unruptured aneurysms accounted for 73%, 82% and 75% of aneurysms with simple stable flow patterns, large impingement regions and large jet sizes, respectively. In this work, a simple flow characterization system was proposed and interesting trends in the association between hemodynamic features and aneurysmal rupture were found. Simple stable patterns, large impingement regions, and jet sizes were more commonly seen with unruptured aneurysms. In contrast, ruptured aneurysms were more likely to have disturbed flow patterns, small impingement regions and thin jets.

Comparison of Hemodynamic Models and Conventional Angiography

Validation is essential before using computational models for clinical studies of aneurysm hemodynamics. Although it is possible to use analytic solutions, in vitro models and/or animal studies to validate the computational models, it is important to demonstrate that CFD models accurately represent the in vivo hemodynamic conditions. Validation of patient-specific computational models is problematic because there is no gold standard for measuring flow patterns in vivo. Recently, Cebral and Putnam’s group has shown that flow structures observed with high frame-rate biplane angiography agree with observations made on computational models and virtual angiograms in a number of patients.

Modeling of Endovascular Interventions

Cerebral aneurysms are typically treated surgically by placing a metallic clip across the neck that isolates the aneurysm from the arterial circulation. However, surgery is no longer first line therapy for most aneurysms. Minimally invasive endovascular management is most suited to patients with high surgical risks. Endovascular treatment relies on blocking or limiting blood flow into the aneurysms by inserting coils into the aneurysm sac or stents across the neck. The choice of optimal endovascular therapy for a particular patient (especially for difficult cases) requires knowledge about the relationship between hemodynamics in the presence of endovascular devices and the procedure outcomes. Cebral and collaborators are developing mesh embedding techniques for patient-specific computational modeling blood flows after endovascular procedures.