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Related Experiment Video

Updated: Jan 19, 2026

A Murine Model of Stent Implantation in the Carotid Artery for the Study of Restenosis

A Murine Model of Stent Implantation in the Carotid Artery for the Study of Restenosis

Published on: May 14, 2013

Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In

P S Zun^1,2,3, A J Narracott^4,5, C Chiastra^6,7

¹Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands. pavel.zun@gmail.com.

Cardiovascular Engineering and Technology

|September 19, 2019

Summary

This study validates a computational model for in-stent restenosis, a common side effect of coronary interventions. The model accurately predicts tissue growth, crucial for understanding and potentially preventing restenosis after stenting.

Keywords:

In silico modelling Model validation Multiscale modelling Restenosis

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Related Experiment Videos

Last Updated: Jan 19, 2026

A Murine Model of Stent Implantation in the Carotid Artery for the Study of Restenosis

A Murine Model of Stent Implantation in the Carotid Artery for the Study of Restenosis

Published on: May 14, 2013

Imaging In-Stent Restenosis: An Inexpensive, Reliable, and Rapid Preclinical Model

Imaging In-Stent Restenosis: An Inexpensive, Reliable, and Rapid Preclinical Model

Published on: September 14, 2009

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

Published on: June 21, 2011

Area of Science:

Biomedical Engineering
Computational Biology
Cardiovascular Research

Background:

Coronary artery restenosis is a significant complication following percutaneous coronary intervention.
Computational modeling offers a powerful tool to investigate the complex mechanisms underlying restenosis.

Purpose of the Study:

To validate a 3D in silico model of in-stent restenosis.
To compare computational predictions with in vivo experimental data in porcine coronary arteries.

Main Methods:

A multiscale computational model incorporating stent deployment, blood flow, and tissue growth (SMC proliferation, ECM production) was developed.
Validation involved simulating stent deployment using micro-CT data and comparing neointimal growth predictions to histological analysis at 14 and 28 days post-stenting.

Main Results:

The computational model demonstrated strong agreement with experimental histological data for neointimal area.
At 14 days, predicted neointimal area was 22±4% vs. 20±3% in vivo; at 28 days, it was 41±3% vs. 42±3% in vivo.

Conclusions:

The presented validation methodology is detailed and location-specific for in silico restenosis models.
The model accurately predicted neointimal growth, highlighting the importance of including vessel curvature and ECM production for accurate simulation.