Ann Biomed Eng. 2025 Dec 2. doi: 10.1007/s10439-025-03923-8. Online ahead of print.
ABSTRACT
PURPOSE: Clinical failure rates associated with in-stent restenosis are difficult to predict and manage, particularly at the patient-specific level. Studies have linked biomechanical factors to focal disease development and progression, suggesting that physics-based simulations using finite element (FE) approaches hold potential to mitigate stent failure rates. However, insufficient validation to assess the accuracy of model predictions limit model credibility for clinical translation. Herein, we established a computational framework to validate vascular stent deployment by integrating robust simulation and rigorous experimental approaches.
METHODS: Experimental testing characterized the transient deformation of a commercially available balloon-expandable stent system, and high-resolution image data were post-processed to create a representative FE model. Non-linear material behaviors and physical boundary conditions were varied to create mixed-fidelity models that assessed the effects of modeling assumptions on stent deformation metrics.
RESULTS: Qualitative comparisons of stent deployment stages showed that high-fidelity FE models captured the characteristic burst opening of the stent edges, followed by the central stent region. Quantitative metrics determined from pressure-diameter curves showed strong agreement, with root mean square error and concordance correlation coefficient values for the proximal, central, and distal diameters ranging from 0.31 mm and 0.96, respectively (lowest fidelity) to 0.21 mm and 0.99 (highest fidelity). Analysis of higher-order metrics (i.e., dog-boning, foreshortening) further demonstrated strong agreement.
CONCLUSION: This framework successfully established a validation plan for vascular stent deployment, analyzed errors in model development, and demonstrated the utility of quantitative assessments, potentially improving the translatability of in silico tools and reducing device failure rates.
PMID:41329417 | DOI:10.1007/s10439-025-03923-8