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A novel method for non-invasively detecting the severity and location of aortic aneurysms
- Source :
- Biomechanics and Modeling in Mechanobiology
- Publication Year :
- 2017
- Publisher :
- Springer Berlin Heidelberg, 2017.
-
Abstract
- © The Author(s) 2017. The influence of an aortic aneurysm on blood flow waveforms is well established, but how to exploit this link for diagnostic purposes still remains challenging. This work uses a combination of experimental and computational modelling to study how aneurysms of various size affect the waveforms. Experimental studies are carried out on fusiform-type aneurysm models, and a comparison of results with those from a one-dimensional fluid–structure interaction model shows close agreement. Further mathematical analysis of these results allows the definition of several indicators that characterize the impact of an aneurysm on waveforms. These indicators are then further studied in a computational model of a systemic blood flow network. This demonstrates the methods’ ability to detect the location and severity of an aortic aneurysm through the analysis of flow waveforms in clinically accessible locations. Therefore, the proposed methodology shows a high potential for non-invasive aneurysm detectors/monitors.
- Subjects :
- Systemic blood
Computer science
Numerical models
0206 medical engineering
Aneurysm detection
Systemic circulation
Diagnostic Techniques, Cardiovascular
experimental models
02 engineering and technology
030204 cardiovascular system & hematology
03 medical and health sciences
Aortic aneurysm
0302 clinical medicine
Aneurysm
one-dimensional modelling
Modelling and Simulation
medicine
Humans
cardiovascular diseases
Waveforms
systemic circulation
numerical models
Original Paper
business.industry
aneurysm detection
Mechanical Engineering
Hemodynamics
Models, Cardiovascular
Pattern recognition
One-dimensional modelling
Blood flow
medicine.disease
Flow network
020601 biomedical engineering
Aortic Aneurysm
waveforms
Modeling and Simulation
cardiovascular system
Artificial intelligence
business
Experimental models
Biomedical engineering
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 16177940 and 16177959
- Volume :
- 16
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Biomechanics and Modeling in Mechanobiology
- Accession number :
- edsair.doi.dedup.....0a189049a83f681691a3e9b967e465f0