Fully automated construction of three-dimensional finite element simulations from Optical Coherence Tomography

TL;DR

This paper presents an automated method to generate accurate 3D finite element models of coronary arteries from OCT images, enabling large-scale patient-specific simulations for better diagnosis and treatment planning.

Contribution

The authors developed an algorithm that automatically creates detailed, anatomically faithful 3D FE models from OCT images, improving automation and precision over manual methods.

Findings

Automated models closely match manually constructed ones in morphology.

Stress and strain convergence achieved with minimal errors (~6% and ~2%).

Enables large-scale, patient-specific simulations for coronary artery disease.

Abstract

Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease.