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Research Papers

Development of a Computational Fluid Dynamics Model for Myocardial Bridging

[+] Author and Article Information
Ashkan Javadzadegan

Faculty of Medicine and Health Sciences,
Macquarie University,
Level 1, 75 Talavera Road,
Sydney 2109, NSW, Australia;
ANZAC Research Institute,
The University of Sydney,
Sydney 2139, NSW, Australia
e-mail: ashkan.javadzadegan@mq.edu.au

Abouzar Moshfegh

Faculty of Medicine and Health Sciences,
Macquarie University,
Sydney 2109, NSW, Australia;
ANZAC Research Institute,
The University of Sydney,
Sydney 2139, NSW, Australia

David Fulker, Tracie Barber

School of Mechanical and
Manufacturing Engineering,
University of New South Wales,
Sydney 2052, Australia

Yi Qian

Faculty of Medicine and Health Sciences,
Macquarie University,
Sydney 2109, NSW, Australia

Leonard Kritharides

ANZAC Research Institute,
The University of Sydney,
Sydney 2139, NSW, Australia;
Department of Cardiology,
Concord Hospital,
The University of Sydney,
Sydney 2139, NSW, Australia

Andy S. C. Yong

Faculty of Medicine and Health Sciences,
Macquarie University,
Sydney 2109, NSW, Australia;
ANZAC Research Institute,
The University of Sydney,
Sydney 2139, NSW, Australia;
Department of Cardiology,
Concord Hospital,
The University of Sydney,
Sydney 2139, NSW, Australia

1Corresponding author.

Manuscript received November 4, 2017; final manuscript received April 24, 2018; published online May 24, 2018. Assoc. Editor: Alison Marsden.

J Biomech Eng 140(9), 091010 (May 24, 2018) (11 pages) Paper No: BIO-17-1504; doi: 10.1115/1.4040127 History: Received November 04, 2017; Revised April 24, 2018

Computational fluid dynamics (CFD) modeling of myocardial bridging (MB) remains challenging due to its dynamic and phasic nature. This study aims to develop a patient-specific CFD model of MB. There were two parts to this study. The first part consisted of developing an in silico model of the left anterior descending (LAD) coronary artery of a patient with MB. In this regard, a moving-boundary CFD algorithm was developed to simulate the patient-specific muscle compression caused by MB. A second simulation was also performed with the bridge artificially removed to determine the hemodynamics in the same vessel in the absence of MB. The second part of the study consisted of hemodynamic analysis of three patients with mild and moderate and severe MB in their LAD by means of the developed in silico model in the first part. The average shear stress in the proximal and bridge segments for model with MB were significantly different from those for model without MB (proximal segment: 0.32 ± 0.14 Pa (with MB) versus 0.97 ± 0.39 Pa (without MB), P < 0.0001 — bridge segment: 2.60 ± 0.94 Pa (with MB) versus 1.50 ± 0.64 Pa (without MB), P < 0.0001). When all three patients were evaluated, increasing the degree of vessel compression shear stress in the proximal segment decreased, whereas the shear stress in the bridge segment increased. The presence of MB resulted in hemodynamic abnormalities in the proximal segment, whereas segments within the bridge exhibited hemodynamic patterns which tend to discourage atheroma development.

Copyright © 2018 by ASME
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Figures

Grahic Jump Location
Fig. 2

Patient (I): (a) angiography: 3D centerline extraction, (b) IVUS: inner lumen detection, (c) coregistration of angiography and IVUS, and (d) 3D vessel reconstruction

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Fig. 3

Life-size silicone phantom of a coronary artery at end diastole: (a) front view, (b) top view, and (c) IVUS catheter inserted through the phantom

Grahic Jump Location
Fig. 1

Patient (I): intracoronary pressure and velocity measurements distal to the bridge segment: (a) rest and (b) peak stress

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Fig. 4

Patient (I): Mesh dependency test

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Fig. 5

Patient (I): ECG and angiographic frames. Frames 1–6 correspond to vessel compression and frames 7–15 correspond to vessel relaxation. The location of MB is shown by arrows on frame 6.

Grahic Jump Location
Fig. 6

Patient (I): CFD setting: (a) the bridge location and its characteristic, (b) the vessel divided into bridge segment (segment (II)) and nonbridge segments (segment (I) and segment (III)), and (c) measured proximal and distal pressure waveforms

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Fig. 7

Patient (I): change in lumen area of bridge segment with corresponding vessel shape during one cardiac cycle. The bridge segment is obvious in the middle of the vessel.

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Fig. 11

Patient (I): time-averaged WSS contours for model: (a) with MB and (b) without MB

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Fig. 12

Patient (I): comparison between models with and without MB. Changes in WSS throughout the cardiac cycle (left) and statistical difference in WSS (right). (a) WSSproximal, (b) WSSbridge, and (c) WSSdistal.

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Fig. 8

Patient (I): RTN distribution along the proximal segment of model with MB

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Fig. 9

Patient (I): comparison between CFD-derived and Doppler-measured velocity profiles within (a) proximal, (b) bridge, and (c) distal segments

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Fig. 10

Patient (I): (a) angiogram during end diastole with defined proximal, bridge, and distal segments; (b) time-averaged WSS contours; (c) WSSproximal, WSSbridge, and WSSdistal throughout the cardiac cycle; and (d) blood flow streamlines. A zoomed-in view of streamlines in the proximal segment is shown where recirculation zones are marked by arrows. VRZ indicates volume of flow recirculation zones.

Grahic Jump Location
Fig. 13

Patient (I): changes in VRZ throughout the cardiac cycle within the proximal segment for models with and without MB

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Fig. 14

Wall shear stress contours: (a) patient (I), (b) patient (II), and (c) patient (III)

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Fig. 15

Patterns of blood flow streamlines: (a) patient (I), (b) patient (II), and (c) patient (III). A zoomed-in view of streamlines in the proximal segment is shown. VRZ indicates volume of flow recirculation zones.

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