Cardiology and Cardiovascular Research

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Shape Optimization of a NURBS Modelled Coronary Stent Using Kriging and Genetic Algorithm

Received: 23 January 2017    Accepted: 09 February 2017    Published: 17 April 2017
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Abstract

In this paper, structural shape of stent has been optimized using NURBS for parameterization of stent structure and target those objectives which are critical for vascular injury. NURBS modeling is done using python coding in RHINO 3D software. For later part of the design, Solidworks is used. The objectives considered in our study are dogboning, foreshortening and arterial wall stresses, all of which are strongly linked to vascular injury leading to restenosis. We use control point weights, strut thickness and strut width as design variables for Latin Hypercube sampling (LHS) in order to generate dataset for Stent deployment simulations. In our study, we generate 80 design data points using LHS in Matlab R2014a. Finite element analysis of stent deployment process is then carried out using ANSYS for all 80 designs of stent generated using LHS. Thereafter, we use Kriging for surrogate modeling and non-dominated sorting genetic algorithm (NSGA-II) in MATLAB for multi-objective design optimization so as to minimize dogboning, foreshortening and arterial wall stresses. As a result, we obtain a range of pareto optimal design parameter values which can be used in clinical design guides so as to accommodate variations observed across different patients.

DOI 10.11648/j.ccr.20170102.13
Published in Cardiology and Cardiovascular Research (Volume 1, Issue 2, April 2017)
Page(s) 39-47
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Stent, Kriging, Foreshortening, Recoil Ratio, Maximum Stress, Optimization

References
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[2] SCHWARTZ, R. S., & HOLMES, D. R. (1994). Pigs, dogs, baboons, and man: lessons for stenting from animal studies. Journal of Interventional Cardiology, 7 (4), 355-368.
[3] Kelliher, D., Clune, R., Campbell, J. S., Robinson, J. C., Appelbe, B., & Buttimer, C. (2008). Sensitivity of shape change on the performance of stainless steel cardio-vascular stents. In Proceedings of the 7th ASMO UK conference on engineering design optimization, Bath (pp. 274-283).
[4] Pant, S., Bressloff, N. W., & Limbert, G. (2012). Geometry parameterization and multidisciplinary constrained optimization of coronary stents. Biomechanics and modeling in mechanobiology, 11 (1-2), 61-82.
[5] Clune, R., Kelliher, D., Robinson, J. C., & Campbell, J. S. (2014). NURBS modeling and structural shape optimization of cardiovascular stents. Structural and Multidisciplinary Optimization, 50 (1), 159-168.
[6] Rogers David, F., & Earnshaw, R. A. (1991). State of the Art in Computer Graphics-Visualization and Modeling.
[7] Pant, S., Limbert, G., Curzen, N. P., & Bressloff, N. W. (2011). Multiobjective design optimisation of coronary stents. Biomaterials, 32 (31), 7755-7773.
[8] Li, N., Zhang, H., & Ouyang, H. (2009). Shape optimization of coronary artery stent based on a parametric model. Finite Elements in Analysis and Design, 45 (6), 468-475.
[9] Tammareddi, S., Sun, G., & Li, Q. (2016). Multiobjective robust optimization of coronary stents. Materials & Design, 90, 682-692.
[10] McKay MD, Beckman RJ, Conover WJ. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics. 2000 Feb 1;42 (1):55-61.
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Author Information
  • Mechanical Engineering Department, Birla Institute of Technology and Science, Pilani, Rajasthan, India

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  • APA Style

    Anierudh Vishwanathan. (2017). Shape Optimization of a NURBS Modelled Coronary Stent Using Kriging and Genetic Algorithm. Cardiology and Cardiovascular Research, 1(2), 39-47. https://doi.org/10.11648/j.ccr.20170102.13

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    ACS Style

    Anierudh Vishwanathan. Shape Optimization of a NURBS Modelled Coronary Stent Using Kriging and Genetic Algorithm. Cardiol. Cardiovasc. Res. 2017, 1(2), 39-47. doi: 10.11648/j.ccr.20170102.13

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    AMA Style

    Anierudh Vishwanathan. Shape Optimization of a NURBS Modelled Coronary Stent Using Kriging and Genetic Algorithm. Cardiol Cardiovasc Res. 2017;1(2):39-47. doi: 10.11648/j.ccr.20170102.13

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  • @article{10.11648/j.ccr.20170102.13,
      author = {Anierudh Vishwanathan},
      title = {Shape Optimization of a NURBS Modelled Coronary Stent Using Kriging and Genetic Algorithm},
      journal = {Cardiology and Cardiovascular Research},
      volume = {1},
      number = {2},
      pages = {39-47},
      doi = {10.11648/j.ccr.20170102.13},
      url = {https://doi.org/10.11648/j.ccr.20170102.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ccr.20170102.13},
      abstract = {In this paper, structural shape of stent has been optimized using NURBS for parameterization of stent structure and target those objectives which are critical for vascular injury. NURBS modeling is done using python coding in RHINO 3D software. For later part of the design, Solidworks is used. The objectives considered in our study are dogboning, foreshortening and arterial wall stresses, all of which are strongly linked to vascular injury leading to restenosis. We use control point weights, strut thickness and strut width as design variables for Latin Hypercube sampling (LHS) in order to generate dataset for Stent deployment simulations. In our study, we generate 80 design data points using LHS in Matlab R2014a. Finite element analysis of stent deployment process is then carried out using ANSYS for all 80 designs of stent generated using LHS. Thereafter, we use Kriging for surrogate modeling and non-dominated sorting genetic algorithm (NSGA-II) in MATLAB for multi-objective design optimization so as to minimize dogboning, foreshortening and arterial wall stresses. As a result, we obtain a range of pareto optimal design parameter values which can be used in clinical design guides so as to accommodate variations observed across different patients.},
     year = {2017}
    }
    

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    AB  - In this paper, structural shape of stent has been optimized using NURBS for parameterization of stent structure and target those objectives which are critical for vascular injury. NURBS modeling is done using python coding in RHINO 3D software. For later part of the design, Solidworks is used. The objectives considered in our study are dogboning, foreshortening and arterial wall stresses, all of which are strongly linked to vascular injury leading to restenosis. We use control point weights, strut thickness and strut width as design variables for Latin Hypercube sampling (LHS) in order to generate dataset for Stent deployment simulations. In our study, we generate 80 design data points using LHS in Matlab R2014a. Finite element analysis of stent deployment process is then carried out using ANSYS for all 80 designs of stent generated using LHS. Thereafter, we use Kriging for surrogate modeling and non-dominated sorting genetic algorithm (NSGA-II) in MATLAB for multi-objective design optimization so as to minimize dogboning, foreshortening and arterial wall stresses. As a result, we obtain a range of pareto optimal design parameter values which can be used in clinical design guides so as to accommodate variations observed across different patients.
    VL  - 1
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