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Development of a Fabric-Reinforced Porous Graft for Vascular Tissue Engineering Using Finite Element Methods and Genetic Algorithms

  • Mark S. Yeoman
  • B. Daya Reddy
  • Deon Bezuidenhout
  • Hellmut C. Bowles
  • Peter Zilla
  • Thomas Franz
Chapter
Part of the Studies in Mechanobiology, Tissue Engineering and Biomaterials book series (SMTEB, volume 15)

Abstract

Small to medium diameter vascular grafts have met with little success over the past 50 years. Surface thrombogenicity and anastomotic intimal hyperplasia, the main reasons for graft failure, are believed to be governed by a lack of endothelialisation and compliance mismatch between graft and host artery. High-porosity polyurethane grafts allow for cellular ingrowth and vascularization, they however encounter detrimental ballooning and low burst strength. To improve the structural properties, a support is required that will not adversely affect ingrowth permissibility of the graft. In this study, an approach combining finite element methods and genetic algorithms was developed to adopt the concept of arterial mechanics, which are predominantly governed by medial and adventitial layer, to tissue-regenerative vascular grafts. The numerical method was able to identify the mechanical properties of adventitial knit fabrics that optimally complement three different intimal/medial porous polyurethane structures to provide grafts with a compliance of 5.3, 5.5 and 6.0 %/100 mmHg. Grafts featuring fabrics manufactured according to the numerically specifications exhibited an in vitro compliance of \(2.1\pm0.8\), \(3.0 \pm 2.4\) and \(4.0\pm 0.7\) %/100 mmHg. Beyond the demonstration of the feasibility of numerical method, it was shown that the graft system of adventitially reinforced polymer with well-defined interconnected porosity can be expected to facilitate the ingrowth and regeneration of vascular tissue for all pore sizes studied.

Keywords

Axial Stress Strain Energy Function Transverse Strain Dynamic Compliance Membrane Element 
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

Acknowledgments

This work was mainly funded through a research collaboration grant by Medtronic Inc. (Minneapolis, MN, USA) to the University of Cape Town. The authors acknowledge the assistance of Richard Steventon with the GA coding.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mark S. Yeoman
    • 1
  • B. Daya Reddy
    • 2
  • Deon Bezuidenhout
    • 3
  • Hellmut C. Bowles
    • 4
  • Peter Zilla
    • 3
  • Thomas Franz
    • 3
    • 5
    • 6
  1. 1.Continuum Blue Ltd.HengoedUK
  2. 2.Centre for Research in Computational and Applied MechanicsUniversity of Cape TownCape TownSouth Africa
  3. 3.Cardiovascular Research Unit, Chris Barnard Division of Cardiothoracic Surgery, Faculty of Health SciencesUniversity of Cape TownCape TownSouth Africa
  4. 4.Finite Element Analysis Services (Pty.) Ltd.ParklandsSouth Africa
  5. 5.Research OfficeUniversity of Cape TownCape TownSouth Africa
  6. 6.Centre for Research in Computational and Applied MechanicsUniversity of Cape TownCape TownSouth Africa

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