Paper
13 March 2017 A numerical framework for studying the biomechanical behavior of abdominal aortic aneurysm
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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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Golnaz Jalalahmadi, Cristian Linte, and María Helguera "A numerical framework for studying the biomechanical behavior of abdominal aortic aneurysm", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372A (13 March 2017); https://doi.org/10.1117/12.2254528
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Cited by 2 scholarly publications.
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KEYWORDS
3D modeling

Image segmentation

Data modeling

Tissues

Magnetic resonance imaging

Process modeling

Finite element methods

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