A platform for high-fidelity patient-specific structural modelling of atherosclerotic arteries: from intravascular imaging to three-dimensional stress distributions

Abstract

The pathophysiology of atherosclerotic lesions, including plaque rupture triggered by mechanical failure of the vessel wall, depends directly on the plaque morphology-modulated mechanical response. The complex interplay between lesion morphology and structural behaviour can be studied with high-fidelity computational modelling. However, construction of three-dimensional (3D) and heterogeneous models is challenging, with most previous work focusing on two-dimensional geometries or on single-material lesion compositions. Addressing these limitations, we here present a semi-automatic computational platform, leveraging clinical optical coherence tomography images to effectively reconstruct a 3D patient-specific multi-material model of atherosclerotic plaques, for which the mechanical response is obtained by structural finite-element simulations. To demonstrate the importance of including multi-material plaque components when recovering the mechanical response, a computational case study was conducted in which systematic variation of the intraplaque lipid and calcium was performed. The study demonstrated that the inclusion of various tissue components greatly affected the lesion mechanical response, illustrating the importance of multi-material formulations. This platform accordingly provides a viable foundation for studying how plaque micro-morphology affects plaque mechanical response, allowing for patient-specific assessments and extension into clinically relevant patient cohorts.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

“…However, structural based analysis still lags due to the added complications of generating smooth and sufficiently connected regions for finite element mesh generation. To the best of our knowledge, the only framework to integrate image classification, segmentation, 3D reconstruction and structural simulation is that recently presented by Kadry et al [ 206 ]. This framework, shown in Figure 14 , built on their previous works to classify pixels into six tissue components within a constrained wall area region, making use of 3D mode filtering to improve spatial consistency and continuity of contours [ 113 , 114 , 131 ].…”

“…Initial OCT images were stacked to form a pseudo-3D image sequence before classification with a CNN and generation of label maps which were subsequently smoothed into contours to generate the digital phantom which was converted to a finite element mesh for structural simulation. Republished with permission of The Royal Society Publishing, from [ 206 ]; permission conveyed through Copyright Clearance Centre, Inc.…”

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

“…Recently, to overcome the previous limitation of homogenized or simplified material representations, an inverse FEA approach was developed to derive non-linear material properties of heterogeneous coronary plaque components using OCT imaging data acquired at differing pressures by incorporating interfaces between various intra-plaque components into the objective function ( Narayanan et al, 2021 ). The importance of including multi-material plaque components has also been demonstrated by the greatly varied lesion mechanical responses ( Kadry et al, 2021 ).…”

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

“…Initial OCT images were stacked to form a pseudo-3D image sequence before classification with a CNN and generation of label maps which were subsequently smoothed into contours to generate the digital phantom which was converted to a finite element mesh for structural simulation. Republished with permission of The Royal Society Publishing, from [193]; permission conveyed through Copyright Clearance Centre, Inc.…”