Table 1.
Comparison of strut geometry and performance metrics of clinically tested bioresorbable stents (BRSs) and modern metallic drug-eluting stents (DESs) for coronary application [4,12,20–25].
Fig 1.
Schematic diagram showing (a) the biaxial stretching process in the machine direction (MD) and transverse direction (TD); (b) the definition of aspect ratio (Ar), defined as the quotient of the stretch ratio in the TD (λTD) and and the stretch ratio in the MD (λMD) and; (c) the alignment of the MD and the TD with a stent’s axial and circumferential axes, respectively.
Fig 2.
Constitutive model stress-strain (σ-ε) curves for (a) Ar = 1, which generated a stent design of equal strength and stiffness in both the axial and circumferential directions; (b) Ar < 1, which generated a stent design that was stiffer and stronger in the axial direction; and (c) Ar > 1, which generated a stent design that was stiffer and stronger in the circumferential direction.
Fig 3.
Geometry parameterisation in terms of strut width (w), strut thickness (t) and strut length (l).
Fig 4.
Finite element deployment simulation showing the stent in its (a) initial crimped state; (b) deployed (expanded) state and (c) final (recoiled) state.
Fig 5.
Schematic representations of tests for: (a) cross-sectional area (post-dilation), CSA; (b) foreshortening, FS; (c) stent-to-artery ratio, SAR and (d) radial collapse pressure, RCP.
Table 2.
High and low levels for design parameters (Ar, w, t and l).
Table 3.
Baseline stent design parameters (Ar, w, t, and l) and its respective performance metrics (CSA, FS, SAR, and RCP).
Table 4.
Design parameters (Ar, w, t and l) and respective performance metrics (CSA, FS, SAR and RCP) for each point considered under the optimised Latin hypercube sampling plan.
Table 5.
Statistical model coefficients for CSA, FS, SAR and RCP.
Fig 6.
Standardised residual vs. predicted response using the statistical model in Eq 16 for (a) CSA; (b) FS; (c) SAR and (d) RCP.
Fig 7.
Predicted response using the statistical model in Eq 16 vs. actual (measured) response from finite element simulations for (a) CSA; (b) FS; (c) SAR and (d) RCP.
Fig 8.
Comparison of absolute t-values (for coefficients) from multiple regression analyses highlighting significant (p < 0.05) main factors and two-way interactions for (a) CSA; (b) FS; (c) SAR and (d) RCP.
Fig 9.
Response surfaces highlighting the combined influence of Ar and w on each performance metric (CSA, FS, SAR and RCP), holding t and l constant at their baseline values (t = 150 μm and l = 1050 μm).
Fig 10.
Response surfaces highlighting the combined influence of Ar and t on each performance metric (CSA, FS, SAR and RCP), holding w and l constant at their baseline values (w = 150 μm and l = 1050 μm).
Fig 11.
Response surfaces highlighting the combined influence of Ar and l on each performance metric (CSA, FS, SAR and RCP), holding w and t constant at their baseline values (w = 150 μm and t = 150 μm).
Fig 12.
Response surfaces highlighting the combined influence of w and t on each performance metric (CSA, FS, SAR and RCP), holding Ar and l constant at their baseline values (Ar = 1.35 and l = 1050 μm).
Fig 13.
Response surfaces highlighting the combined influence of w and l on each performance metric (CSA, FS, SAR and RCP), holding Ar and t constant at their baseline values (Ar = 1.35 and t = 150 μm).
Fig 14.
Response surfaces highlighting the combined influence of t and l on each performance metric (CSA, FS, SAR and RCP), holding Ar and w constant at their baseline values (Ar = 1.35 and w = 150 μm).
Fig 15.
Trade-off curves for all permutations of the four performance metrics: (a) CSA vs. FS; (b) CSA vs. SAR, (c) CSA vs. RCP and (d) FS vs. SAR, (e) FS vs. RCP and (f) SAR vs. RCP.
Table 6.
Minimum and maximum values for each performance metric (CSA, FS, SAR and RCP).
Table 7.
Comparison between baseline (base.) and optimal (opt.) stent designs highlighting design parameters and their respective performance metrics.
Fig 16.
Visual comparison of normalised performance metrics and design parameters between the baseline design and the optimal design.