Theoretical modeling for monitoring the growth of fusiform abdominal aortic aneurysms using dielectric elastomer capacitive sensors

Lü, C.F. and Du, Y.K. (2016) Theoretical modeling for monitoring the growth of fusiform abdominal aortic aneurysms using dielectric elastomer capacitive sensors. International Journal of Applied Mechanics, 8(7), 1640010. (doi: 10.1142/s175882511640010x)

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Abstract

To evaluate the rupture risk of abdominal aortic aneurysms, traditional medical imaging techniques, e.g., ultrasonography, CT scanning, or magnetic resonance imaging, form the regular clinic treatments for detecting the growth but can only provide the diameter of the aneurysm. This has been proved to be inadequate since a considerable portion of rupture has been missed by the critical rupture size that is established according to clinical statistics. We proposed to mount a dielectric elastomer capacitive sensor around an early-stage abdominal aneurysm and using the continuously varying capacitance for retrieving both the wall stress and morphology of the aneurysm in a real-time manner. A theoretical mechanics model based on the growth theory is developed to predict the correlation between the capacitance and the stress level as well as the geometrical parameters of the aneurysm. Numerical calculations agree well qualitatively with the clinical statistics of both rupture size and rupture stress of the aneurysm. The results suggest that the rupture risk of an aneurysm in a patient may be assessed more reliably by multiple indicators of stress and morphology during the growth process. A range of capacitance may also be recommended for elective repair based on the proposed theoretical model.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Du, Mr Yangkun
Authors: Lü, C.F., and Du, Y.K.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Mathematics
Journal Name:International Journal of Applied Mechanics
Publisher:World Scientific Publishing
ISSN:1758-8251
ISSN (Online):1758-826X
Published Online:20 December 2016

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