Research Papers

Computational Study of Computed Tomography Contrast Gradients in Models of Stenosed Coronary Arteries

[+] Author and Article Information
Parastou Eslami, Jung-Hee Seo

Department of Mechanical Engineering,
Johns Hopkins University,
Baltimore, MD 21218

Amir Ali Rahsepar, Richard George

Division of Cardiology,
Department of Medicine,
Johns Hopkins University,
Baltimore, MD 21218

Albert C. Lardo

Division of Cardiology,
Department of Medicine,
Johns Hopkins University,
Baltimore, MD 21218
Biomedical Engineering,
Johns Hopkins University,
Baltimore, MD 21218

Rajat Mittal

Department of Mechanical Engineering,
Johns Hopkins University,
Baltimore, MD 21218
e-mail: mittal@jhu.edu

1Corresponding author.

2P. Eslami and J.-H. Seo contributed equally to this work.

Manuscript received October 1, 2014; final manuscript received June 15, 2015; published online July 9, 2015. Assoc. Editor: Francis Loth.

J Biomech Eng 137(9), 091002 (Sep 01, 2015) (11 pages) Paper No: BIO-14-1487; doi: 10.1115/1.4030891 History: Received October 01, 2014; Revised June 15, 2015; Online July 09, 2015

Recent computed tomography coronary angiography (CCTA) studies have noted higher transluminal contrast agent gradients in arteries with stenotic lesions, but the physical mechanism responsible for these gradients is not clear. We use computational fluid dynamics (CFD) modeling coupled with contrast agent dispersion to investigate the mechanism for these gradients. Simulations of blood flow and contrast agent dispersion in models of coronary artery are carried out for both steady and pulsatile flows, and axisymmetric stenoses of severities varying from 0% (unobstructed) to 80% are considered. Simulations show the presence of measurable gradients with magnitudes that increase monotonically with stenotic severity when other parameters are held fixed. The computational results enable us to examine and validate the hypothesis that transluminal contrast gradients (TCG) are generated due to the advection of the contrast bolus with time-varying contrast concentration that appears at the coronary ostium. Since the advection of the bolus is determined by the flow velocity in the artery, the magnitude of the gradient, therefore, encodes the coronary flow velocity. The correlation between the flow rate estimated from TCG and the actual flow rate in the computational model of a physiologically realistic coronary artery is 96% with a R2 value of 0.98. The mathematical formulae connecting TCG to flow velocity derived here represent a novel and potentially powerful approach for noninvasive estimation of coronary flow velocity from CT angiography.

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Fig. 2

Representative AIF measured in actual CCTA as well as the fitted function that is employed in the simulations in a human studies (a) and a canonical study (b). Part (c) is a schematic to illustrate the mechanism described in the paper: TCG is the transluminal (spatial) projection of the time profile of the concentration of the contrast agent and hence is driven by the coronary blood flow velocity (VCF).

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Fig. 1

Representative example of TCG for a stenosed artery. Luminal cross sections are sampled every 0.5 mm and plotted over the vessel length to obtain an axial variation of cross-sectional averaged attenuation (HU) (top figure). Bottom figure shows the axial and cross-sectional visualizations of lumen area by contrast agent. HU is the Hounsfield unit for the attenuation level. The lesion section is shown with an arrow. CT imaging is acquired using a 320-row detector CT scanner (AquilionTMOne—Toshiba Medical Systems Corporation, Otawara, Japan).

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Fig. 3

Extraction of cross-sectional lumen area along the axial direction from the CFD simulation for the calculation of TCG

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Fig. 8

(a) Three-dimensional model of a patient-specific coronary artery for the normal (unstenosed) case. (b) Model of the artery with 70% stenosis. (c) Computational meshes employed in the various segments of the model.

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Fig. 4

(a) An idealized 3D model of the coronary artery for the normal (unstenosed) case, where Qn is referred to the normal (no stenosis) flow rate. (b) Model of the artery with a stenosis, where Qs is referred to the flow rate in the vessel with stenosis. (c) Computational meshes employed in the various segments of the model.

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Fig. 5

Pressure (a), velocity magnitude (b), and normalized contrast agent concentration (c) (C/Cmax) for the Qn = 50 ml/min case with a 70% area constriction. (d) Velocity magnitude and streamlines in the stenosed region in idealized model.

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Fig. 6

Normalized transluminal attenuation profiles along the axial direction of main arterial segment for (a) steady flow with Qn = 50 ml/min (PA = 3 mm Hg and PB = 0 mm Hg), (b) pulsatile flow with Qn = 50 ml/min (PA = 3 mm Hg and PB = 2.7 mm Hg), (c) steady flow with Qn = 69 ml/min (PA = 4.5 mm Hg and PB = 0 mm Hg), and (d) pulsatile flow with Qn = 69 ml/min (PA = 4.5 mm Hg and PB = 4.05 mm Hg). (1) and (2) indicate the locations of bifurcations shown in Fig. 4. The attenuation profiles along the stenosed section (between (1) and (2)) are fitted by the linear function; as + b and the slope a represents the normalized TCG (TCG*). All the results are for the idealized model at the peak of AIF and the percentage refers to different area stenosis levels.

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Fig. 7

Correlation between (a) TCG and the inverse square of flow rate (1/Q2) for steady flow, (b) for pulsatile flow, (c) TCG and the inverse square of bolus time, (1/Td2) for 70% stenosis and Qn = 50 ml/min, steady flow, and (d) TCG* and FFRQ for pulsatile flow for the idealized model

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Fig. 9

Patient-specific computational results: pressure (a), normalized contrast agent concentration (C/Cmax) for the Qn = 375 ml/min case with a 70% area constriction (b), and velocity magnitude and streamlines in the stenosed region (c), cross-sectional plane in which velocity contour in (c) is shown. The segmentations S1–S4 are segments of the main LAD before each branch.

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Fig. 10

Patient-specific normalized transluminal attenuation profiles along the axial direction of main arterial segment in LAD (steady flow) with PA = 3 mm Hg and PB = 0 mm Hg. (a) Correlation between CFD calculation of flow rate and TAFE calculation of the flow rate in the no-stenosis (normal) and 70% area stenosis cases (b).

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Fig. 11

(a) Patient-specific velocity magnitude profile comparison between different grid levels for the cross sections shown in (b) in planes in the main LAD after the stenosis. (b) Cross-sectional plane used in (a). The four different grid levels are Coarse with ≈2×105, Normal with ≈3×105, Fine with ≈4.55×105, and FineR with ≈9.25×105 tetrahedral elements.



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