Influence of coronary plaque morphology on local mechanical states and associated in-stent restenosis

TL;DR

ISR after PCI is multifactorial, and local mechanical states influenced by plaque morphology may drive ISR. The study uses physics-based, patient-specific coronary artery simulations of PCI that incorporate CCTA-derived plaque morphology through Gaussian Mixture Model mapping to compute wall stresses and relate them to ISR outcomes. It finds that circumferential and asymmetric calcifications create higher local stresses and that stress-based analyses (notably thresholds around $30$–$40$ kPa) show strong associations with ISR, supporting the role of morphology-driven mechanics in restenosis risk. These findings suggest that plaque morphology and local mechanical environment can inform non-invasive ISR risk assessment and guide personalized PCI planning.

Abstract

In-stent restenosis after percutaneous coronary intervention is a multifactorial process. Specific morphological lesion characteristics were observed to contribute to the occurrence of in-stent restenosis. Local mechanical factors, such as stresses and strains, are known to influence tissue adaptation after stent implantation. However, the influence of morphological features on those local mechanical states and, hence, on the occurrence of in-stent restenosis remains understudied. This work investigates the correlation between local mechanical quantities and in-stent restenosis by evaluating the stress distributions in the artery wall during and after stent implantation for informative lesion morphologies. We perform computational simulations of the stenting procedure with physics-based patient-specific coronary artery models. Different morphologies are assessed using the spatial plaque composition information from high-resolution coronary computed tomography angiography data. We quantify the correlation between in-stent restenosis and local tensional stresses. We found that specific morphological characteristics like circumferential or asymmetric block calcifications result in higher stresses in the surrounding tissue. This study concludes that local stresses are critical for assessing the individual in-stent restenosis risk.

Figures (18)

• Figure 1: Extract of a patient-specific coronary artery model. The fibrotic tissue and normal intima are not shown for a better overview.
• Figure 2: Gaussian Mixture Model (GMM) for the plaque tissue of an exemplary lesion. Light blue: CCTA data; Gaussians of the different components and their initial means, i.e., the pre-defined mean values for each component, are shown in yellow, green, red, or purple for lipid-rich tissue, fibrosis, normal intimal tissue, or calcifications, respectively. Black line: Mixture of all components.
• Figure 3: Plaque segmentation of an example lesion. a) Cross-section of the CCTA image orthogonal to the vessel centerline with initially segmented outlines. b) Same slice in the clustered model with assigned plaque components
• Figure 4: Stent placement in the computational model guided by the angiography imaging during the intervention
• Figure 5: Lesion sections of sample patients with calcification patterns (yellow).