Non-invasive characterization of complex coronary lesions

Abstract

Conventional invasive diagnostic imaging techniques do not adequately resolve complex Type B and C coronary lesions, which present unique challenges, require personalized treatment and result in worsened patient outcomes. These lesions are often excluded from large-scale non-invasive clinical trials and there does not exist a validated approach to characterize hemodynamic quantities and guide percutaneous intervention for such lesions. This work identifies key biomarkers that differentiate complex Type B and C lesions from simple Type A lesions by introducing and validating a coronary angiography-based computational fluid dynamic (CFD-CA) framework for intracoronary assessment in complex lesions at ultrahigh resolution. Among 14 patients selected in this study, 7 patients with Type B and C lesions were included in the complex lesion group including ostial, bifurcation, serial lesions and lesion where flow was supplied by collateral bed. Simple lesion group included 7 patients with lesions that were discrete, $$<10\hbox {mm}$$ < 10 mm long and readily accessible. Intracoronary assessment was performed using CFD-CA framework and validated by comparing to clinically measured pressure-based index, such as FFR. Local pressure, endothelial shear stress (ESS) and velocity profiles were derived for all patients. We validates the accuracy of our CFD-CA framework and report excellent agreement with invasive measurements ($$n=14, R^2 = 0.6, p = 0.0013$$ n = 14 , R 2 = 0.6 , p = 0.0013 ). Ultra-high resolution achieved by the model enable physiological assessment in complex lesions and quantify hemodynamic metrics in all vessels up to 1mm in diameter. Importantly, we demonstrate that in contrast to traditional pressure-based metrics, there is a significant difference in the intracoronary hemodynamic forces, such as ESS, in complex lesions compared to simple lesions at both resting and hyperemic physiological states [n = 14, $$p=0.03$$ p = 0.03 ]. Higher ESS was observed in the complex lesion group ($$7.0\pm 4.7$$ 7.0 ± 4.7 Pa) than in simple lesion group ($$4.8\pm 3.6$$ 4.8 ± 3.6 Pa). Complex coronary lesions have higher ESS compared to simple lesions, such differential hemodynamic evaluation can provide much the needed insight into the increase in adverse outcomes for such patients and has incremental prognostic value over traditional pressure-based indices, such as FFR.

The effect of beta-blockers on hemodynamic parameters in patient-specific blood flow simulations of type-B aortic dissection: a virtual study

“…Taylor et al have suggested that the total microvascular resistance at hyperemic condition reduces up to 24% of the healthy value 53 , 55 . Recently, Randles et al have used a 25% reduction in microvascular resistance at hyperemic condition compared with the normal one 54 .…”

Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction

“…ML models have been used to automatically segment medical images for creating 3D computer models in recent years. Especially in patient−specific biomechanical modeling, each cardiovascular disease leads to multi−feature 3D morphologies, such as atherosclerosis [ 94 ], aneurysm [ 95 ], and occlusive diseases [ 96 , 97 ]. For instance, Berhane et al [ 98 ] used deep learning to generate an automatic 3D segmentation model of the aorta based on 4D−flow magnetic resonance imaging (MRI).…”

Modelling of hemodynamics in bifurcation lesions of coronary arteries before and after myocardial revascularization

“…Two key approaches are known to determine the boundary conditions. The first one involves predicting conditions based on models of the [19,20], also known as Windkessel models [21][22][23].…”

“…The first one involves predicting conditions based on models of the arterial system. The most popular are lumped models [19,20], also known as Windkessel models [21–23].…”