Name
Title | ||
|---|---|---|
A numerical framework for studying the biomechanical behavior of abdominal aortic aneurysm |
Abstract | ||
|---|---|---|
Abdominal aortic aneurysm ( AAA) is known as a leading cause of death in the United States. AAA is an abnormal dilation of the aorta, which usually occurs below the renal arteries and causes an expansion at least 1.5 times its normal diameter. It has been shown that biomechanical parameters of the aortic tissue coupled with a set of specific geometric parameters characterizing the vessel expansion, affect the risk of aneurysm rupture. Here, we developed a numerical framework that incorporates both biomechanical and geometrical factors to study the behavior of abdominal aortic aneurysm. Our workflow enables the extraction of the aneurysm geometry from both clinical quality, as well as low-resolution MR images. We used a two-parameter, hyper-elastic, isotropic, incompressible material to model the vessel tissue. Our numerical model was tested using both synthetic and mouse data and we evaluated the effects of the geometrical and biomechanical properties on the developed peak wall stress. In addition, we performed several parameter sensitivity studies to investigate the effect of different factors affecting the AAA and its behavior and rupture. Lastly, relationships between different geometrical and biomechanical parameters and peak wall stress were determined. These studies help us better understand vessel tissue response to various loading, geometry and biomechanics conditions, and we plan to further correlate these findings with the pathophysiological conditions from a patient population diagnosed with abdominal aortic aneurysms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/12.2254528 | Proceedings of SPIE |
Keywords | Field | DocType |
Abdominal aortic aneurysm (AAA),biomedical imaging,image segmentation,finite element model,hyperelastic material properties,parametric studies,peak wall stress | Biomedical engineering,Computer vision,Population,Abdomen,Aneurysm,Aortic tissue,Artificial intelligence,Biomechanics,Abnormal dilation,Abdominal aortic aneurysm,Aorta,Physics | Conference |
Volume | ISSN | Citations |
10137 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Golnaz Jalalahmadi | 1 | 0 | 1.01 |
| Cristian A. Linte | 2 | 93 | 24.09 |
| Maria Helguera | 3 | 5 | 2.66 |