Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published: January 15, 2022

doi:

Harry J. Carpenter, Mergen H. Ghayesh, Anthony C. Zander, Juanita L. Ottaway, Giuseppe Di Giovanni, Stephen J. Nicholls, Peter J. Psaltis^5,6

¹School of Mechanical Engineering,University of Adelaide, ²South Australian Health and Medical Research Institute (SAHMRI), ³Vascular Research Centre, Lifelong Health Theme,South Australian Health and Medical Research Institute (SAHMRI), ⁴Monash Cardiovascular Research Centre, ⁵Adelaide Medical School,University of Adelaide, ⁶Department of Cardiology,Central Adelaide Local Health Network

Summary

There is a need to determine which atherosclerotic lesions will progress in the coronary vasculature to guide intervention before myocardial infarction occurs. This article outlines the biomechanical modeling of arteries from Optical Coherence Tomography using fluid-structure interaction techniques in a commercial finite element solver to help predict this progression.

Abstract

In this paper, we present a complete workflow for the biomechanical analysis of atherosclerotic plaque in the coronary vasculature. With atherosclerosis as one of the leading causes of global death, morbidity and economic burden, novel ways of analyzing and predicting its progression are needed. One such computational method is the use of fluid-structure interaction (FSI) to analyze the interaction between the blood flow and artery/plaque domains. Coupled with in vivo imaging, this approach could be tailored to each patient, assisting in differentiating between stable and unstable plaques. We outline the three-dimensional reconstruction process, making use of intravascular Optical Coherence Tomography (OCT) and invasive coronary angiography (ICA). The extraction of boundary conditions for the simulation, including replicating the three-dimensional motion of the artery, is discussed before the setup and analysis is conducted in a commercial finite element solver. The procedure for describing the highly nonlinear hyperelastic properties of the artery wall and the pulsatile blood velocity/pressure is outlined along with setting up the system coupling between the two domains. We demonstrate the procedure by analyzing a non-culprit, mildly stenotic, lipid-rich plaque in a patient following myocardial infarction. Established and emerging markers related to atherosclerotic plaque progression, such as wall shear stress and local normalized helicity, respectively, are discussed and related to the structural response in the artery wall and plaque. Finally, we translate the results to potential clinical relevance, discuss limitations, and outline areas for further development. The method described in this paper shows promise for aiding in the determination of sites at risk of atherosclerotic progression and, hence, could assist in managing the significant death, morbidity, and economic burden of atherosclerosis.

Introduction

Coronary artery disease (CAD) is the most common type of heart disease and one of the leading causes of death and economic burden globally¹^,². In the United States, roughly one in every eight deaths is attributed to CAD³^,⁴, while most global deaths from CAD are now seen in low- and middle-income countries⁵. Atherosclerosis is the predominant driver of these deaths, with plaque rupture or erosion leading to coronary artery occlusion and acute myocardial infarction (AMI)⁶. Even after revascularization of culprit coronary lesions, patients have substantial risk of recurrent major adverse cardiovascular events (MACE) after AMI, largely due to the concomitant presence of other non-culprit plaques that are also vulnerable to rupture⁷. Intracoronary imaging provides an opportunity to detect these high-risk plaques⁸. Although intravascular ultrasound (IVUS) is the gold standard for evaluating plaque volume, it has limited resolution to identify microstructural features of vulnerable plaque in contrast to the high resolution (10-20 µm) of optical coherence tomography (OCT). A thin and inflamed fibrous cap overlying a large lipid pool has been demonstrated to be the most important signature of a vulnerable plaque⁹ and is best identified and measured by OCT among currently available intracoronary imaging modalities¹⁰. Importantly, OCT is also able to assess other high-risk plaque features, including: lipid arc; macrophage infiltration; presence of thin cap fibroatheroma (TCFA), which is defined as lipid-rich core with overlying thin fibrous cap (<65 µm); spotty calcification; and plaque microchannels. OCT detection of these high-risk features in non-culprit plaques post-AMI has been associated with up to a 6-fold increased risk of future MACE¹¹. However, despite this, the ability of angiography and OCT imaging to predict which coronary plaques will progress and ultimately rupture or erode is limited, with positive predictive values of only 20%-30%⁸. This limited predictive ability hinders clinical decision-making around which non-culprit plaques to treat (e.g., by stenting)⁷^,¹².

In addition to patient factors and the biological characteristics of plaque, biomechanical forces in the coronary arteries are also important determinants of plaque progression and instability¹³. One technique that shows promise for helping to comprehensively evaluate these forces is fluid-structure interaction (FSI)¹⁴ simulation. Wall shear stress (WSS), also called endothelial shear stress, has been a traditional focal point for coronary biomechanics research¹⁵, with a general understanding that WSS plays an etiological role in atherosclerosis formation¹⁶. Predominantly simulated using computational fluid dynamics (CFD) techniques, low WSS regions have been associated with intimal thickening¹⁷, vascular remodeling¹⁸ and the prediction of lesion progression¹⁹ and future MACE²⁰. Recent advances in these analyses suggest the underlying WSS vector field topology²¹, and its multidirectional characteristics²², as a better predictor of atherosclerosis risk than WSS magnitude alone. However, WSS only captures a glimpse of the overall biomechanical system at the lumen wall, and much like imaging modalities, no one biomechanical metric can reliably discern high risk atherosclerotic features.

Further metrics are emerging as potentially important in atherosclerosis formation. Intraluminal flow characteristics²³ are one such example, with helical flow, quantified through various indices²⁴, suggested as playing an atheroprotective role by suppressing disturbed flow patterns²⁵^,²⁶. While CFD techniques can analyze these flow characteristics and present a wide range of useful results, they do not consider the underlying interactions between the blood flow, artery structure and general heart motion. This simplification of the dynamic system to a rigid wall misses potentially critical results such as fibrous cap stress. While the debate both for and against the need for FSI over CFD continues²⁷^,²⁸^,²⁹, many comparisons neglect to include the impact of ventricle function. This limitation can be overcome with FSI, which has shown that dynamic bending and compression exerted on the artery through the influence of the ventricle function can significantly impact plaque and artery structural stress as well as flow metrics such as WSS³⁰^,³¹^,³². This is important as structural stresses are also a key metric for analyzing and predicting plaque rupture³³^,³⁴ and have been suggested to co-locate with regions of plaque increase¹⁴^,³⁵. Capturing these interactions allows for a more realistic representation of the coronary environment and the potential mechanisms of disease progression.

Addressing this, here we outline the process of developing a patient-specific geometry from OCT imaging³⁶ and the setting up and running of an artery FSI simulation using a commercial finite element solver. The process to manually extract the lumen, lipid and outer artery wall is detailed before the three-dimensional computational reconstruction of the patient's artery. We outline the simulation set-up, coupling and the process of comparing baseline, and follow-up OCT imaging parameters to determine lesion progression. Finally, we discuss the post-processing of numerical results and how these data may have clinical relevance by comparing the biomechanical results with lesion progression/regression. The overall method is demonstrated on non-culprit, mildly stenotic, lipid-rich plaques in the right coronary artery (RCA) of a 58-year-old Caucasian male patient who presented with an acute non-ST elevation myocardial infarction in the setting of hypertension, type 2 diabetes mellitus, obesity (BMI 32.6) and a family history of premature CAD. Coronary angiography and OCT imaging were performed during his initial admission, and then 12 months later as part of an ongoing clinical trial (COCOMO-ACS trial ACTRN12618000809235). We anticipate that this technique can be further refined and used for identifying coronary plaques that are at high risk of progressing.

Protocol

The following deidentified data was analyzed from a patient recruited into the ongoing COCOMO-ACS randomized-controlled trial (ACTRN12618000809235; Royal Adelaide Hospital HREC reference number: HREC/17/RAH/366), with additional ethics approval granted by Central Adelaide Local Health Network (CALHN) Research Services for the purpose of biomechanical simulation (CALHN Reference Number 14179). Figure 1 summarizes the complete workflow outlined in the following protocol, which can be applied t…

Representative Results

Representative results are presented for both established and emerging biomechanical markers of atherosclerosis progression. Established metrics such as WSS and WSS-derived results (including time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI)) are visualized in Figure 10. The wall shear stress over the cardiac cycle is largely driven by the blood velocity, however, artery geometry and its motion/contraction play a significant role in its spatial distribution. This can …

Discussion

The use of FSI methods to analyze coronary biomechanics is still a developing field from both numerical modelling and clinical result aspects. Here we have described the outline of setting up a patient specific FSI analysis, based on the finite element/finite volume methods, utilizing OCT and angiographic imaging. While the method we describe here utilizes a commercial finite element solver, the procedure can be applied to any FSI capable software. There are still several limitations to be improved upon in the methodolog…

Declarações

The authors have nothing to disclose.

Acknowledgements

The authors would like to acknowledge the support provided by The University of Adelaide, Royal Adelaide Hospital (RAH) and the South Australian Health and Medical Research Institute (SAHMRI). The COCOMO-ACS trial is an investigator-initiated study funded by project grants from the National Health and Medical Research Council (NHMRC) of Australia (ID1127159) and National Heart Foundation of Australia (ID101370). H.J.C. is supported by a scholarship from the Westpac Scholars Trust (Future Leaders Scholarship) and acknowledges support from The University of Adelaide, School of Mechanical Engineering and the Department of Education, Skills and Employment Research Training Program (RTP) scholarship. S.J.N. receives a Principal Research Fellowship from the NHMRC (ID1111630). P.J.P. receives a Level 2 Future Leader Fellowship from the National Heart Foundation of Australia (FLF102056) and Level 2 Career Development Fellowship from the NHMRC (CDF1161506).

Materials

ANSYS Workbench (version 19.0)	ANSYS	Commercial finite element solver
MATLAB (version 2019b)	Mathworks	Commercial programming platform
MicroDicom/ImageJ	MicroDicom/ImageJ	Open Source DICOM reader
Visual Studio (version 2019)	Microsoft	Commercial Integrated Development Environment

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Carpenter, H. J., Ghayesh, M. H., Zander, A. C., Ottaway, J. L., Di Giovanni, G., Nicholls, S. J., Psaltis, P. J. Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression. J. Vis. Exp. (179), e62933, doi:10.3791/62933 (2022).