Modeling Implications in Simulation-Based Design of Stents

Variations associated with stenting systems, artery properties, and doctor skills necessitate a better understanding of coronary artery stents so as to facilitate the design of stents that are customized to individual patients. This paper presents the development of an integrated computer simulation-based design approach using engineering finite element analysis (FEA) models for capturing stent knowledge, utility theory-based decision models for representing the design preferences, and statistics-based surrogate models for improving process efficiency. Two focuses of the paper are: 1) understanding the significance of engineering analysis and surrogate models in the simulation-based design of medical devices; 2) investigating the modeling implications in the context of stent design. The study reveals that the advanced nonlinear FEA software with analysis capacities on large deformation and contact interaction has offered a platform to execute high fidelity simulations, yet the selection of appropriate analysis models is still subject to the tradeoff between cost of analysis and accuracy of solution; the cost-prohibitive simulations necessitate the employment of surrogate models in subsequent multi-objective design optimization. A detailed comparison between regression models and Kriging models suggests the importance of sampling schemes in successfully implementing Kriging methods.© 2006 ASME

[1]  Dougal R McLean,et al.  Stent design: implications for restenosis. , 2002, Reviews in cardiovascular medicine.

[2]  George A. Hazelrigg,et al.  A Framework for Decision-Based Engineering Design , 1998 .

[3]  Y. Matsumoto,et al.  Does stent design affect probability of restenosis? A randomized trial comparing Multilink stents with GFX stents. , 2001, American heart journal.

[4]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[5]  F. Auricchio,et al.  Mechanical behavior of coronary stents investigated through the finite element method. , 2002, Journal of biomechanics.

[6]  Dong-Ho Lee,et al.  Improvement of Feasibility of Design Space Using Response Surface and Kriging Method , 2004 .

[7]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  T. Simpson,et al.  Use of Kriging Models to Approximate Deterministic Computer Models , 2005 .

[9]  V. Fuster,et al.  Atherosclerosis and coronary artery diseases , 1996 .

[10]  Patrick W. Serruys,et al.  Handbook of Coronary Stents , 1997 .

[11]  A. Olsson,et al.  On Latin Hypercube Sampling for Stochastic Finite Element Analysis , 1999 .

[12]  Sundar Krishnamurty,et al.  Preference-Based Updating of Kriging Surrogate Models , 2004 .

[13]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[14]  C. Jansen,et al.  Stent design related neointimal tissue proliferation in human coronary arteries; an intravascular ultrasound study. , 2001, European heart journal.

[15]  V. Fuster Atherosclerosis and Coronary Artery Disease , 1996, Nature Medicine.

[16]  T. W. Layne,et al.  A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models , 1998 .

[17]  George A. Hazelrigg,et al.  Thoughts on Model Validation for Engineering Design , 2003 .

[18]  Timothy M. Mauery,et al.  COMPARISON OF RESPONSE SURFACE AND KRIGING MODELS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION , 1998 .

[19]  T. Henry,et al.  Pathophysiology of coronary artery restenosis. , 2002, Reviews in cardiovascular medicine.

[20]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[21]  K Ulm,et al.  Restenosis after coronary placement of various stent types. , 2001, The American journal of cardiology.

[22]  H. Raiffa,et al.  Decisions with Multiple Objectives , 1993 .

[23]  Neal L. Eigler,et al.  Stent Design: Implications for Restenosis , 2002 .