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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Becker, C. R., Ohnesorge, B. M., Schoepf, U. J., and Reiser, M. F., 2000, “Current Development of Cardiac Imaging With Multidetector-Row CT,” Eur. J. Radiol., 36(2), pp. 97–103. [CrossRef] [PubMed]
Vincent, H., and Reddy, G. P., 2010, Cardiovascular Imaging, 1 Har/Psc, ed., Vol. 2, Saunders, Philadelphia, PA.
Choi, J.-H., Koo, B.-K., Yoon, Y. E., Min, J. K., Song, Y.-B., Hahn, J.-Y., Choi, S.-H., Gwon, H.-C., and Choe, Y. H., 2012, “Diagnostic Performance of Intracoronary Gradient-Based Methods by Coronary Computed Tomography Angiography for the Evaluation of Physiologically Significant Coronary Artery Stenoses: A Validation Study With Fractional Flow Reserve,” Eur. Heart J. Cardiovasc. Imaging, 13(12), pp. 1001–1007. [CrossRef] [PubMed]
Choi, J.-H., Min, J. K., Labounty, T. M., Lin, F. Y., Mendoza, D. D., Shin, D. H., Ariaratnam, N. S., Koduru, S., Granada, J. F., Gerber, T. C., Oh, J. K., Gwon, H. C., and Choe, Y. H., “Intracoronary Transluminal Attenuation Gradient in Coronary CT Angiography for Determining Coronary Artery Stenosis,” JACC Cardiovasc. Imaging, 4(11), pp. 1149–1157. [CrossRef] [PubMed]
Chow, B. J. W., Kass, M., Gagné, O., Chen, L., Yam, Y., Dick, A., and Wells, G. A., 2011, “Can Differences in Corrected Coronary Opacification Measured With Computed Tomography Predict Resting Coronary Artery Flow?” J. Am. Coll. Cardiol., 57(11), pp. 1280–1288. [CrossRef] [PubMed]
Stuijfzand, W. J., Danad, I., Raijmakers, P. G., Marcu, C. B., Heymans, M. W., van Kuijk, C. C., van Rossum, A. C., Nieman, K., Min, J. K., Leipsic, J., van Royen, N., and Knaapen, P., 2014, “Additional Value of Transluminal Attenuation Gradient in CT Angiography to Predict Hemodynamic Significance of Coronary Artery Stenosis,” JACC Cardiovasc. Imaging, 7(4), pp. 374–386. [CrossRef] [PubMed]
Nakanishi, R., and Budoff, M. J., 2014, “A New Approach in Risk Stratification by Coronary CT Angiography,” Scientifica (Cairo), 2014(2014), p. 278039. [CrossRef] [PubMed]
Steigner, M. L., Mitsouras, D., Whitmore, A. G., Otero, H. J., Wang, C., Buckley, O., Levit, N. A., Hussain, A. Z., Cai, T., Mather, R. T., Smedby, O., DiCarli, M. F., and Rybicki, F. J., 2010, “Iodinated Contrast Opacification Gradients in Normal Coronary Arteries Imaged With Prospectively ECG-Gated Single Heart Beat 320-Detector Row Computed Tomography,” Circ. Cardiovasc. Imaging, 3(2), pp. 179–186. [CrossRef] [PubMed]
San Román, J. A., Vilacosta, I., Castillo, J. A., Rollán, M. J., Hernández, M., Peral, V., Garcimartín, I., de la Torre, M. M., and Fernández-Avilés, F., 1998, “Selection of the Optimal Stress Test for the Diagnosis of Coronary Artery Disease,” Heart, 80(4), pp. 370–376. [CrossRef] [PubMed]
Lloyd-Jones, D., Adams, R. J., Brown, T. M., Carnethon, M., Dai, S., De Simone, G., Ferguson, T. B., Ford, E., Furie, K., Gillespie, C., Go, A., Greenlund, K., Haase, N., Hailpern, S., Ho, P. M., Howard, V., Kissela, B., Kittner, S., Lackland, D., Lisabeth, L., Marelli, A., McDermott, M. M., Meigs, J., Mozaffarian, D., Mussolino, M., Nichol, G., Roger, V. L., Rosamond, W., Sacco, R., Sorlie, P., Stafford, R., Thom, T., Wasserthiel-Smoller, S., Wong, N. D., and Wylie-Rosett, J., 2010, “Executive Summary: Heart Disease and Stroke Statistics-2010 Update: A Report From the American Heart Association,” Circulation, 121(7), pp. 46–215. [CrossRef]
Patel, M. R., Peterson, E. D., Dai, D., Brennan, J. M., Redberg, R. F., Anderson, H. V., Brindis, R. G., and Douglas, P. S., 2010, “Low Diagnostic Yield of Elective Coronary Angiography,” N. Engl. J. Med., 363(1), pp. 886–895. [CrossRef]
Lange, R. A., and Hillis, L. D., 2003, “Cardiology Patient Pages. Diagnostic Cardiac Catheterization,” Circulation, 107(17), pp. e111–e113. [CrossRef] [PubMed]
Nakazato, R., Park, H. B., Berman, D. S., Gransar, H., Koo, B. K., Erglis, A., Lin, F. Y., Dunning, A. M., Budoff, M. J., Malpeso, J., Leipsic, J., and Min, J. K., 2013, “Noninvasive Fractional Flow Reserve Derived From Computed Tomography Angiography for Coronary Lesions of Intermediate Stenosis Severity Results From the DeFACTO Study,” Circ. Cardiovasc. Imaging, 6(6), pp. 881–889. [CrossRef] [PubMed]
Durant, J., Waechter, I., Hermans, R., Weese, J., and Aach, T., 2008, “Toward Quantitative Virtual Angiography: Evaluation With In Vitro Studies,” 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14–17, pp. 632–635. [CrossRef]
Calamante, F., Yim, P. J., and Cebral, J. R., 2003, “Estimation of Bolus Dispersion Effects in Perfusion MRI Using Image-Based Computational Fluid Dynamics,” Neuroimage, 19(2), pp. 341–353. [CrossRef] [PubMed]
Kim, T., Cheer, A. Y., and Dwyer, H. A., 2004, “A Simulated Dye Method for Flow Visualization With a Computational Model for Blood Flow,” J. Biomech., 37(8), pp. 1125–1136. [CrossRef] [PubMed]
George, R. T., Ichihara, T., Lima, J. A, and Lardo, A. C., 2010, “A Method for Reconstructing the Arterial Input Function During Helical CT: Implications for Myocardial Perfusion Distribution Imaging,” Radiology, 255(2), pp. 396–404. [CrossRef] [PubMed]
Foley, W. D., and Karcaaltincaba, M., 2003, “Computed Tomography Angiography: Principles and Clinical Applications,” J. Comput. Assist Tomogr., 27(Supp. 1), pp. 23–30. [CrossRef]
Bishop, A. H., and Samady, H., 2004, “Fractional Flow Reserve: Critical Review of an Important Physiologic Adjunct to Angiography,” Am. Heart J., 147(5), pp. 792–802. [CrossRef] [PubMed]
Yoon, Y. E., Choi, J.-H., Kim, J.-H., Park, K.-W., Doh, J.-H., Kim, Y.-J., Koo, B.-K., Min, J. K., Erglis, A., Gwon, H.-C., Choe, Y. H., Choi, D.-J., Kim, H.-S., Oh, B.-H., and Park, Y.-B., 2012, “Noninvasive Diagnosis of Ischemia-Causing Coronary Stenosis Using CT Angiography: Diagnostic Value of Transluminal Attenuation Gradient and Fractional Flow Reserve Computed From Coronary CT Angiography Compared to Invasively Measured Fractional Flow Reserve,” JACC Cardiovasc. Imaging, 5(11), pp. 1088–1096. [CrossRef] [PubMed]
Taylor, G., 1953, “Dispersion of Soluble Matter in Solvent Flowing Slowly Through a Tube,” Proc. R. Soc. A, 219(1137), pp. 186–203. [CrossRef]
Hozumi, T., Yoshida, K., Akasaka, T., Asami, Y., Ogata, Y., Takagi, T., Kaji, S., Kawamoto, T., Ueda, Y., and Morioka, S., “Noninvasive Assessment of Coronary Flow Velocity and Coronary Flow Velocity Reserve in the Left Anterior Descending Coronary Artery by Doppler Echocardiography: Comparison With Invasive Technique,” J. Am. Coll. Cardiol., 32(5), pp. 1251–1259. [CrossRef] [PubMed]
Funabashi, N., Kobayashi, Y., Perlroth, M., and Rubin, G. D., 2003, “Coronary Artery: Quantitative Evaluation of Normal Diameter Determined With Electron-Beam CT Compared With Cine Coronary Angiography Initial Experience,” Radiology, 226(1), pp. 263–271. [CrossRef] [PubMed]
Gould, K. L., Lipscomb, K., and Hamilton, G. W., 1974, “Physiologic Basis for Assessing Critical Coronary Stenosis. Instantaneous Flow Response and Regional Distribution During Coronary Hyperemia as Measures of Coronary Flow Reserve,” Am. J. Cardiol., 33(1), pp. 87–94. [CrossRef] [PubMed]
Wong, D. T. L., Ko, B. S., Cameron, J. D., Nerlekar, N., Leung, M. C. H., Malaiapan, Y., Crossett, M., Leong, D. P., Worthley, S. G., Troupis, J., Meredith, I. T., and Seneviratne, S. K., 2013, “Transluminal Attenuation Gradient in Coronary Computed Tomography Angiography Is a Novel Noninvasive Approach to the Identification of Functionally Significant Coronary Artery Stenosis: A Comparison With Fractional Flow Reserve,” J. Am. Coll. Cardiol., 61(12), pp. 1271–1279. [CrossRef] [PubMed]
George, R. T., Rahsepar, A. A., Eslami, P., Seo, J. H., Mittal, R., Zhao, D., Guallar, E., Jacobson, L. P., Budoff, M., Post, W. S., and Lardo, A. C., 2014, “Abstract 17975: Coronary and Myocardial Blood Flow Measurements Derived From Coronary Computed Tomography Angiography and Transluminal Attenuation Flow Encoding in the Multicenter AIDS Cohort Study,” Circulation, 130: A17975.
George, R. T., Rahsepar, A. A., Seo, J.-H., Eslami, P., Korley, F. K., Lardo, A. C., and Mittal, R., 2014, “Abstract: Application of Transluminal Attenuation Flow Encoding (TAFE) to Quantify Absolute Coronary Blood Flow,” SCCT 9th Annual Science Meeting, pp. 7–9.
Lardo, A. C., Rahsepar, A. A., Seo, J. H., Eslami, P., Korley, F., George, R. T., and Flow, A., “Computed Tomography Transluminal Attenuation Flow Encoding (TAFE): Formulation, Preclinical Validation, and Clinical Feasibility,” JCCT (in press).
Chung, J., and Hulbert, G. M., 1993, “A Time Integration Algorithm for Structural Dynamics With Improved Numerical Dissipation: The Generalized-Alpha Method,” ASME J. Appl. Mech., 60(2), pp. 371–375. [CrossRef]
Barth, D., and Jespersen, T. J., 1989, “The Design and Application of Upwind Schemes on Unstructured Meshes,” AIAA Paper No. 89–0366. [CrossRef]


Grahic Jump Location
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).

Grahic Jump Location
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. 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. 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.

Grahic Jump Location
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.

Grahic Jump Location
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. 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. 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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