A multiphysics modeling approach for in-stent restenosis: Theoretical aspects and finite element implementation

Area of Science:

• Computational Biology
• Biomedical Engineering
• Pathology Modeling

Background:

• In silico models are crucial for validating disease mechanisms and personalizing interventions in soft biological tissues.
• In-stent restenosis, a common complication post-stent implantation, requires advanced modeling for mechanistic understanding and risk mitigation.

Purpose of the Study:

• To develop and validate a high-fidelity computational framework for simulating in-stent restenosis in coronary arteries.
• To integrate cellular and molecular factors into a continuum mechanical model of volumetric growth during restenosis.
• To lay the groundwork for patient-specific risk prediction and optimization of stent implantation parameters.

Main Methods:

• A fully-coupled Lagrangian finite element framework was implemented.
• Advection-reaction-diffusion equations modeled key factors: platelet-derived growth factor, transforming growth factor-β, extracellular matrix, and smooth muscle cell density.
• Continuum mechanics described volumetric growth coupled with vessel wall constituent evolution.

Main Results:

• The computational model successfully replicated the pathology of in-stent restenosis.
• Numerical examples demonstrated the model's behavior and its ability to emulate a stented artery.
• Qualitative validation confirmed the model's potential for simulating restenotic processes.

Conclusions:

• The developed finite element framework provides a robust tool for studying in-stent restenosis.
• The model's integration with patient-specific data holds promise for predicting restenosis risk.
• This approach can assist in tailoring stent implantation strategies to minimize adverse outcomes.