Type of Document	Dissertation
Author	Wang, Hong Jun
URN	etd-06242002-114810
Title	Noninvasive Biomechanical Assessment of the Rupture Potential of Abdomial Aortic Aneurysm
Degree	Doctor of Philosophy
Program	Bioengineering
School	School of Engineering
Advisory Committee	Advisor Name Title David A. Vorp Committee Chair Anne M. Robertson Committee Member Harvey S. Borovetz Committee Member Marshall W. Webster Committee Member Michael S. Sacks Committee Member
Keywords	Cardiovascular Disease Microstructure Biomechanics Intraluminal Thrombus Abdominal Aortic Aneurysm 3D Reconstruction Hyperelastic Finite Element Method Multiple Linear Regression
Date of Defense	2002-07-17
Availability	restricted
Abstract Abdominal aortic aneurysm (AAA) is a localized dilation of the infrarenal aorta. Ruptured AAA has a mortality rate of 95% and is ranked as the 13th leading cause of death in the US. The ability to reliably evaluate the susceptibility of a particular AAA to rupture could vastly improve the clinical management of AAA patients. Currently, no such reliable evaluation technique exists. The purpose of this work was to develop a noninvasive technique to evaluate the rupture potential of individual AAA. To predict the wall strength distribution, experimentally determined wall strength data were used for construction of a mathematical model using multiple linear regression techniques. The developed model was then validated using data from a different group of specimens. The strength distributions for four different AAA were then generated using the validated model. The finite element method was used to estimate the wall stress distribution for all four AAA based on their realistic geometries (reconstructed from CT images) which included intraluminal thrombus (ILT). The measured systolic blood pressure was applied as the loading condition. Nonlinear hyperelastic constitutive models for AAA and ILT tissue were used, the latter being developed here based on uniaxial tensile testing data. For each patient, a local Rupture Potential Index (RPI) distribution was calculated as local (nodal) wall stress divided by local wall strength. The developed model contains four independent variable parameters: AAA size, patient’s age, family history, local ILT thickness, and normalized local AAA diameter (R Squared = 0.86, p = 0.001). The model predicted the actual (measured) strength very accurately (R Squared = 0.81 for model validation). The wall strength values predicted for the four AAA studied ranged from 130 to 306 N/(cm squared), whereas the measured wall strength values ranged from 39 to 324 N/(cm squared). The peak wall stress for the four AAA studied ranged from 19 to 37 N/(cm squared). The peak RPI values ranged from 0.15 to 0.55. This patient-specific, computer-based, noninvasive RPI estimation technique could become an import and reliable diagnostic tool for AAA patient management. However, further clinical studies are needed to validate this technique.
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