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Computational Analysis of Flow and Stress Patterns in Patient Specific Thoracic Aortic Aneurysm Models

  • Alessandro Borghi
  • Nigel B. Wood
  • Raad H. Mohiaddin
  • Xiao Yun Xu
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 5)

Abstract

Thoracic Aortic Aneurysms (TAAs) are associated with low frequency in a given population but high mortality rate. No reliable surgical criterion is available at present but internal wall stress has proved to be more reliable as a predictor of rupture than the maximum diameter in case of Abdominal Aortic Aneurysms (AAAs). However, few studies have been reported on the role of biomechanical factors in the development and rupture of TAAs. This chapter describes a computational mechanics model of TAA based on patient-specific anatomical and flow conditions, acquired from Magnetic Resonance Imaging (MRI). The model has been applied to five patients with TAAs at different locations of the aorta. The results showed no correlation between peak stress and aneurysm size such as the maximum diameter. The effects of intra-luminal thrombus (ILT) and its mechanical properties on wall stress patterns were investigated. It has been found that the shape, size and location of ILT have a significant effect on wall stress patterns. Peak stress calculated using a fully coupled fluid-structure interaction simulation was similar to that predicted by a static solid simulation, in agreement with previous studies of AAA. The work presented here serves as a first step towards developing a reliable predictive tool to allow improved prognosis and surgical decision making for TAA patients.

Keywords

Aortic Aneurysm Wall Stress Fluid Structure Interaction Stress Pattern Thoracic Aortic Aneurysm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

The work reported here was sponsored by the British Heart Foundation

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Alessandro Borghi
    • 1
  • Nigel B. Wood
    • 1
  • Raad H. Mohiaddin
    • 2
  • Xiao Yun Xu
    • 1
  1. 1.Department of Chemical EngineeringImperial College LondonLondonUK
  2. 2.Royal Brompton and Harefield NHS Foundation TrustLondonUK

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