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, patients 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.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-06242002-114810 |
| Date | 30 August 2002 |
| Creators | Wang, Hong Jun |
| Contributors | David A. Vorp, Michael S. Sacks, Harvey S. Borovetz, Marshall W. Webster, Anne M. Robertson |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf, image/gif |
| Source | http://etd.library.pitt.edu:80/ETD/available/etd-06242002-114810/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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