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Updated: Feb 19, 2026

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
Published on: December 6, 2024
Influence of Coronary Plaque Morphology on Local Mechanical States and Associated In-Stent Restenosis
Janina C Datz1,2, Ivo Steinbrecher3, Johannes Krefting4
1Institute for Computational Mechanics, Technical University of Munich, Munich 85748, Germany;Department of Cardiology, TUM University Hospital German Heart Center, Technical University of Munich, Munich 80636, Germany.
Abstract:
In-stent restenosis (ISR) after percutaneous coronary intervention (PCI) is a multifactorial process. Specific morphological lesion characteristics were observed to contribute to the occurrence of ISR. Local mechanical factors, such as stresses and strains, are known to influence tissue adaptation after stent implantation. However, the influence of morphological features on those local mechanical states and, hence, on the occurrence of ISR remains understudied. This work explores how local mechanical quantities relate to ISR by evaluating the stress distributions in the artery wall during and after stent implantation for morphology-informed lesion examples. We perform computational simulations of the stenting procedure with physics-based patient-specific coronary artery models. Different morphologies are assessed using the spatial plaque composition information from high-resolution coronary computed tomography angiography (CCTA) data. In the sample cases, elevated local tensile stresses were consistently found at sites corresponding to ISR. We found that specific morphological characteristics like circumferential or asymmetric block calcifications result in higher stresses in the surrounding tissue. These findings show that for the observed connection between plaque morphology and ISR, the local mechanical state may represent a relevant link. This study provides a mechanistic, illustrative insight for the examined cases. Future work with larger cohorts and systematic follow-up can establish statistically robust associations.
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