Computational analysis for non-invasive detection of stenosis in peripheral arteries

Date

2019-08-01

Author

Salman, Huseyin Enes
Yazıcıoğlu, Yiğit

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Atherosclerosis usually affects the entire cardiovascular system, including peripheral blood vessels. Peripheral arterial stenosis may indicate possible serious vascular disorders related to more vital organs. If peripheral arterial stenosis can be discerned at an early stage, it can serve as a warning sign to take precautions, such as using more invasive diagnostic techniques or adopting a healthier life style. In this study, peripheral regions, such as the thigh, upper arm, and neck are modelled considering stenosis of their major arteries. Stenosis generates a fluctuating pressure field on the arterial wall, which leads to vibration on the skin's surface. This stenosis-induced pressure field is modelled as a harmonic load and applied to the inner surface of the arterial structure. The vibration response on bare skin is computationally determined using the superposition of modal responses. Realistic geometries and hyperelastic material properties are used in modelling the layers of skin, fat, muscle, and bones. The results indicate that stenosis severities higher than 70% lead to a considerable increase in vibration-response amplitudes, especially at frequencies greater than 250 Hz. The detailed analysis of skin responses provides useful information to detect the stenosis location, where the sum of the vibration amplitudes attains its maximum value around the stenosis. (C) 2019 IPEM. Published by Elsevier Ltd. All rights reserved.

Subject Keywords

Biophysics, Biomedical Engineering

URI

https://hdl.handle.net/11511/38243

Journal

Medical Engineering and Physics

DOI

https://doi.org/10.1016/j.medengphy.2019.06.007

Collections

Department of Mechanical Engineering, Article

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Citation Formats

H. E. Salman and Y. Yazıcıoğlu, “Computational analysis for non-invasive detection of stenosis in peripheral arteries,” Medical Engineering and Physics, pp. 39–50, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38243.