Morphometric Reconstruction of Coronary Vasculature Incorporating Uniformity of Flow Dispersion.

Ravi Namani,Yoram Lanir,Ghassan S Kassab

doi:10.3389/fphys.2018.01069

Abstract

Experimental limitations in measurements of coronary flow in the beating heart have led to the development of in silico models of reconstructed coronary trees. Previous coronary reconstructions relied primarily on anatomical data, including statistical morphometry (e.g., diameters, length, connectivity, longitudinal position). Such reconstructions are non-unique, however, often leading to unrealistic predicted flow features. Thus, it is necessary to impose physiological flow constraints to ensure realistic tree reconstruction. Since a vessel flow depends on its diameter to fourth power, diameters are the logical candidates to guide vascular reconstructions to achieve realistic flows. Here, a diameter assignment method was developed where each vessel diameter was determined depending on its downstream tree size, aimed to reduce flow dispersion to within measured range. Since the coronary micro-vessels are responsible for a major portion of the flow resistance, the auto regulated coronary flow was analyzed in a morphometry-based reconstructed 400 vessel arterial microvascular sub-tree spanning vessel orders 1–6. Diameters in this subtree were re-assigned based on the flow criteria. The results revealed that diameter re-assignment, while adhering to measured morphometry, significantly reduced the flow dispersion to realistic levels while adhering to measured morphometry. The resulting network flow has longitudinal pressure distribution, flow fractal nature, and near-neighboring flow autocorrelation, which agree with measured coronary flow characteristics. Collectively, these results suggest that a realistic coronary tree reconstruction should impose not only morphometric data but also flow considerations. The work is of broad significance in providing a novel computational framework in the field of coronary microcirculation. It is essential for the study of coronary circulation by model simulation, based on a realistic network structure.

Highlights

The coronary network supplies oxygen-carrying blood to each myocyte in the myocardium to meet its metabolic demand
The flow control mechanisms are myogenic one which responds to the local trans-vascular pressure (Johnson, 1980; Kuo et al, 1988, 1990a; Davis, 2012); shear mechanism which responds to the local wall shear of the flowing blood (Kuo et al, 1990b, 1995; Jones et al, 1995b); and the metabolic mechanism which responds to imbalance in oxygen supply/demand in the myocardium (Feigl, 1983; Kanatsuka et al, 1989)
Despite considerable progress in recent decades in understanding coronary flow, further advances are impeded primarily due to considerable difficulties in experimental measurement of flow features in the heart which are due to the significant movement during the cardiac cycle, the inaccessibility of deep myocardial layer vessels, the dynamic myocardium-vessel interaction (MVI), and the difficulties in controlling the levels of each flow regulation mechanism under in vivo conditions

Summary

Introduction

The coronary network supplies oxygen-carrying blood to each myocyte in the myocardium to meet its metabolic demand This task is assigned to the coronary network consisting of millions of vessels of different diameters which are embedded within the myocardium. Coronary Reconstruction Incorporating Flow Uniformity responds to external loading by the periodically contracting myocardium to affect the coronary lumen area and the blood flow; (3) The three major flow control mechanisms which act in concert to actively regulate the coronary arterial diameters, aimed to meet the myocardium metabolic demand (Feigl, 1983). Previous modeling studies considered either the network structure (Karch et al, 1999; Beard and Bassingthwaighte, 2000) or the vessel interaction with the contracting myocardium (Downey and Kirk, 1975; Spaan et al, 1981; Krams et al, 1989; Rabbany et al, 1989; Kresh et al, 1990; Manor et al, 1994; Zinemanas et al, 1994; Vis et al, 1997; Smith, 2004; Westerhof et al, 2006; Algranati et al, 2010), or only the flow regulation (Liao and Kuo, 1997; Cornelissen et al, 2002; Carlson et al, 2008)

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Conclusion