Data-driven reduced order surrogate modeling for coronary in-stent restenosis

Area of Science:

• Computational modeling
• Biomedical engineering
• Cardiovascular research

Background:

• Coronary in-stent restenosis (ISR) is a complex process involving multiple biological factors and drug elution.
• Accurate modeling of ISR requires significant computational resources and time.

Purpose of the Study:

• To develop a novel, non-intrusive, data-driven reduced order modeling approach for coronary in-stent restenosis.
• To reduce computational costs and time for multiphysics simulations of ISR.

Main Methods:

• A 3D convolutional autoencoder was trained for dimensionality reduction in the offline phase.
• Two approaches were explored to handle 5D input datasets, incorporating time and geometry reshaping.
• Gaussian process regression was used to correlate latent variables with input parameters.

Main Results:

• The model accurately predicts neointimal growth acceleration between 30-60 days post-PCI.
• Surrogate models demonstrated high accuracy, with one approach showing smaller errors.
• Analysis revealed nonlinear, periodic patterns in ISR rates related to drug flux, enabling identification of optimal drug dosage ranges.

Conclusions:

• The non-intrusive reduced order surrogate model is effective for predicting ISR outcomes.
• The method supports real-time simulations and optimization of percutaneous coronary intervention parameters.