  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.
