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Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

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A computational workflow for modeling complex patient-specific coronary stenting cases.

Luca Antonini¹, Gianluca Poletti², Georgia S Karanasiou³

¹Laboratory of Biological Structures Mechanics (LaBS) - Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy; Parametric Design Suisse Sagl, Chiasso (CH), Switzerland.

Computer Methods and Programs in Biomedicine

|November 30, 2024

Summary

This study introduces a new simulation method for coronary stenting, improving digital twin reliability for complex artery cases. The approach accurately predicts outcomes, even with overstretching, enhancing in silico clinical trial capabilities.

Keywords:

Arterial softening Finite element analysis In silico trial OCT Overstretching

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Area of Science:

Computational mechanics
Biomedical engineering
Medical imaging

Background:

In silico clinical trials require reliable simulation of coronary stenting.
Current digital twins of arteries often simplify mechanical behavior for usability.
Complex cases like bifurcations with overstretching present simulation challenges.

Purpose of the Study:

To develop a phenomenological approach for patient-specific coronary artery mechanical description during stenting.
To improve the reliability and usability of digital twins for coronary stenting simulations.
To accurately model complex arterial geometries and overstretching scenarios.

Main Methods:

Utilized pre- and post-operative images to create four vessel models.
Developed multi-step structural stenting simulations with distinct media and adventitia layer properties.
Incorporated a hyperelastic response with strain-dependent softening to simulate overstretching damage.
Classified plaque components (lipidic, calcified, generic) with associated mechanical properties.

Main Results:

Simulation outcomes closely matched clinical results for all stenting procedures.
Recovered lumen area errors were consistently below 15%.
The framework demonstrated improved performance and reliability, particularly for overstretching scenarios.

Conclusions:

The proposed approach effectively accounts for in vivo conditions in simulations.
It accurately predicts lumen reopening and malapposed struts post-stent deployment.
This enhances the utility of digital twins for complex coronary stenting analysis.