Research Papers

An Outflow Boundary Condition Model for Noninvasive Prediction of Fractional Flow Reserve in Diseased Coronary Arteries

[+] Author and Article Information
Iyad A. Fayssal

Computational Mechanics Laboratory,
Mechanical Engineering Department,
American University of Beirut,
Riad El-Solh,
Beirut 1107 2020, Lebanon
e-mail: iaf04@mail.aub.edu

Fadl Moukalled

Mechanical Engineering Department,
American University of Beirut,
Riad El-Solh,
Beirut 1107 2020, Lebanon
e-mail: fmukalled@aub.edu.lb

Samir Alam

Department of Internal Medicine,
American University of Beirut,
Riad El-Solh,
Beirut 1107 2020, Lebanon
e-mail: salam@aub.edu.lb

Hussain Isma'eel

Associate Professor
Department of Internal Medicine,
American University of Beirut,
Riad El-Solh,
Beirut 1107 2020, Lebanon
e-mail: hi09@aub.edu.lb

1Corresponding author.

Manuscript received April 14, 2017; final manuscript received October 16, 2017; published online January 23, 2018. Assoc. Editor: C. Alberto Figueroa.

J Biomech Eng 140(4), 041004 (Jan 23, 2018) (13 pages) Paper No: BIO-17-1154; doi: 10.1115/1.4038250 History: Received April 14, 2017; Revised October 16, 2017

This paper reports on a new boundary condition formulation to model the total coronary myocardial flow and resistance characteristics of the myocardial vascular bed for any specific patient when considered for noninvasive diagnosis of ischemia. The developed boundary condition model gives an implicit representation of the downstream truncated coronary bed. Further, it is based on incorporating patient-specific physiological parameters that can be noninvasively extracted to account for blood flow demand to the myocardium at rest and hyperemic conditions. The model is coupled to a steady three-dimensional (3D) collocated pressure-based finite volume flow solver and used to characterize the “functional significance” of a patient diseased coronary artery segment without the need for predicting the hemodynamics of the entire arterial system. Predictions generated with this boundary condition provide a deep understanding of the inherent challenges behind noninvasive image-based diagnostic techniques when applied to human diseased coronary arteries. The overall numerical method and formulated boundary condition model are validated via two computational-based procedures and benchmarked with available measured data. The newly developed boundary condition is used via a designed computational methodology to (a) confirm the need for incorporating patient-specific physiological parameters when modeling the downstream coronary resistance, (b) explain the discrepancies presented in the literature between measured and computed fractional flow reserve (FFRCT), and (c) discuss the current limitations and future challenges in shifting to noninvasive assessment of ischemia.

Copyright © 2018 by ASME
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Grahic Jump Location
Fig. 1

(a) Patient specific coronary CTA images (DICOM files); (b) coronary artery segmentation; (c) reconstructed 3D model of the left coronary arterial system; (d) generation of 3D mesh using a finite number of elements; (e) quantification of total coronary rest flow to the myocardium and calculation of downstream microvascular resistance; (f) inducing hyperemic condition. The graph shows variation of mean total coronary resistance index with intravenous adenosine infusion for arteries with normal and abnormal flow reserve (Wilson et al. [18]); (g) steady-state mass and momentum equations governing blood flow; (h) a selected example of simulated patient-specific FFR with two upstream (in left main) and downstream (in LAD) disease locations.

Grahic Jump Location
Fig. 2

Block diagram of the overall coupled 3D-lumped numerical solution algorithm

Grahic Jump Location
Fig. 3

(a) Normalized left MBF demand. The figure also displays the computed range of the constant characteristic of myocardial flow (Y0) affirming the dependency of Y0 on patient physiological parameters. (b) Normalized left myocardial downstream resistance computed from the quantified total left myocardial flow demand.

Grahic Jump Location
Fig. 4

(a) Predicted normalized mean flow for a single dog mapped over the measured data of Gould et al. [32]; (b) comparison between the predicted and measured average normalized mean flow for all dogs under rest and hyperemic conditions. The lines numbered (1–12) in the figure correspond to the correlations (third-order polynomials) obtained when simulating the dog models under hyperemic conditions. The lines for rest conditions are not numbered since they are close to each other, and it is hard to distinguish them.

Grahic Jump Location
Fig. 5

Predicted values of Qs/Qh and variation of maximum relative difference for all cases simulated under (a) rest and (b) hyperemic conditions. At any specific disease geometric severity (%DS), Qs/Qh values are the result of varying patient physiological parameters: LV mass, M, (M ranges from 100 to 250 g, incremented by 50 g) and HR fixed at 60 BPM, the CI and VO2I are varied over their reported normal ranges (CI is varied from 2500 to 4000 ml/min/m2, incremented by 500 ml/min/m2, and VO2I is varied from 120 to 160 ml/min/m2, incremented by 10 ml/min/m2). The maximum relative difference in the values of Qs/Qh at a specific %DS is calculated by Eq. (22).

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Fig. 6

(a) Correlation between FFR computed using the new boundary condition and the MBF per unit myocardial mass approach; ((b)–(d)) Bland–Altman plots (provided for comparison purposes) for assesing the agreement level between FFR_MBF0.6, FFR_MBF1, and FFR_MBF1.4 versus FFR_new_boundary_condition, respectively



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