Optimizing through computational modeling to reduce dogboning of functionally graded coronary stent material
- 162 Downloads
Coronary artery disease is the leading cause of death among the men and women. One of the most suitable treatments for this problem is balloon angioplasty with stenting. Functionally graded material (FGM) stents have shown suitable mechanical behavior in simulations. While their deformation was superior to uniform materials, the study was aimed at finding the most suitable configuration to reach the optimum performance. A combination of finite element method (FEM) and optimization algorithm have been used to fulfil this objective. To do that, three different conditions have been investigated in a Palmaz-Schatz geometry, where in the first and second ones the stent was a combination of steel and CoCr alloy (L605), and the third condition was a combination of CoCr alloy (L605) and CoCr alloy (F562). In the first and third conditions, dogboning was the objective function, but in the second condition a non-uniform deformation indicator was chosen as the objective function. In all three conditions the heterogeneous index was the control variable. The stent in the third condition showed a poor performance. While in the steel/CoCr alloy (L605) stents the heterogeneous index of 0.374 showed the lowest maximum dogboning, the heterogeneous index of 5 had more uniform deformation. Overall due to the lower dogboning of the steel/CoCr alloy (L605) stent with heterogeneous index of 0.374, this stent is recommended as the optimum stent in this geometrical configuration.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no competing interests.
- 4.Karimi A, Navidbakhsh M, Rahmati SM, Sera T, Kudo S. A combination of constitutive damage model and artificial neural networks to characterize the mechanical properties of the healthy and atherosclerotic human coronary arteries. Artif Organs. 2017 Feb 2. 10.1111/aor.12855.
- 5.Karimi A, Navidbakhsh M, Rahmati SM, Sera T, Kudo S, A combination of experimental and numerical methods to investigate the role of strain rate on the mechanical properties and collagen fiber orientations of the healthy and atherosclerotic human coronary arteries. Bioengineered. 2017;8(2):154–70CrossRefGoogle Scholar
- 10.Bahreinizad H, Oscuii HN. Numerical investigation of role of synovial fluid in a poroelastic model of natural human knee joint during walking. 2015 22nd Iranian Conference on Biomedical Engineering (ICBME): IEEE; 2015. p. 126–31.Google Scholar
- 16.De Beule M, Van Impe R, Verhegghe B, Segers P, Verdonck P. Finite element analysis and stent design: Reduction of dogboning. Technol Health Care. 2006;14:233–41.Google Scholar
- 18.Schiavone A, Zhao L, Abdel-Wahab A. Dynamic simulation of stent deployment–effects of design, material and coating. J Phy: Conference Series: IOP Publishing; 2013. p. 012032.Google Scholar
- 19.Miyamoto Y, Kaysser W, Rabin B, Kawasaki A, Ford RG. Functionally graded materials: design, processing and applications: Springer Science & Business Media; 2013.Google Scholar
- 22.Sadollah A, Bahreininejad A. Optimum functionally gradient materials for dental implant using simulated annealing: INTECH Open Access Publisher; 2012.Google Scholar
- 26.Conn AR, Scheinberg K, Vicente LN. Introduction to derivative-free optimization: Siam; 2009.Google Scholar
- 27.Luenberger DG. Introduction to linear and nonlinear programming. MA: Addison-Wesley Reading; 1973.Google Scholar
- 32.Pochrząst M, Walke W, Kaczmarek M. Biomechanical characterization of the balloon-expandable slotted tube stents. J Achiev Mater Manufact Eng. 2009;37:340–7.Google Scholar
- 33.Powell MJ. The BOBYQA algorithm for bound constrained optimization without derivatives. Cambridge NA Report NA2009/06, University of Cambridge, Cambridge 2009.Google Scholar
- 35.Karimi A, Razaghi R, Shojaei A, Navidbakhsh M. An experimental-nonlinear finite element study of a balloon expandable stent inside a realistic stenotic human coronary artery to investigate plaque and arterial wall injury. Biomed Eng/Biomedizinische Technik. 2015;60:593–602.Google Scholar
- 38.Choy S, Sun C, Leong K, Tan K, Wei J. Functionally graded material by additive manufacturing. Proceedings of the 2nd International Conference on Progress in Additive Manufacturing (Pro-AM 2016). Singapore: Research Publishing. 2016.Google Scholar