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Dynamic Coronary Blood Flow Velocity and Wall Shear Stress Estimation Using Ultrasound in an Ex Vivo Porcine Heart

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Abstract

Purpose

Wall shear stress (WSS) is a critically important physical factor contributing to atherosclerosis. Mapping the spatial distribution of local, oscillatory WSS can identify important mechanisms underlying the progression of coronary artery disease.

Methods

In this study, blood flow velocity and time-varying WSS were estimated in the left anterior descending (LAD) coronary artery of an ex vivo beating porcine heart using ultrasound with an 18 MHz linear array transducer aligned with the LAD in a forward-viewing orientation. A pulsatile heart loop with physiologically-accurate flow was created using a pulsatile pump. The coronary artery wall motion was compensated using a local block matching technique. Next, 2D and 3D velocity magnitude and WSS maps in the LAD coronary artery were estimated at different time points in the cardiac cycle using an ultrafast Doppler approach. The blood flow velocity estimated using the presented approach was compared with a commercially-available, calibrated single element blood flow velocity measurement system.

Results

The resulting root mean square error (RMSE) of 2D velocity magnitude acquired from a high frequency, linear array transducer was less than 8% of the maximum velocity estimated by the commercial system.

Conclusion

When implemented in a forward-viewing intravascular ultrasound device, the presented approach will enable dynamic estimation of WSS, an indicator of plaque vulnerability in coronary arteries.

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References

  1. Tsao, C. W., et al. Heart disease and stroke statistics—2022 update: a report from the American Heart Association. Circulation. 145:e153–e639, 2022.

    Article  PubMed  Google Scholar 

  2. Eshtehardi, P., et al. High wall shear stress and high-risk plaque: an emerging concept. Int. J. Cardiovasc. Imaging. 33:1089–1099, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Koskinas, K. C., et al. The role of low endothelial shear stress in the conversion of atherosclerotic lesions from stable to unstable plaque. Curr. Opin. Cardiol. 24:580–590, 2009.

    Article  PubMed  Google Scholar 

  4. Fukumoto, Y., et al. Localized elevation of shear stress is related to coronary plaque rupture: a 3-dimensional intravascular ultrasound study with in-vivo color mapping of shear stress distribution. J. Am. Coll. Cardiol. 51:645–650, 2008.

    Article  PubMed  Google Scholar 

  5. Kandangwa, P., et al. Influence of right coronary artery motion, flow pulsatility and non-Newtonian rheology on wall shear stress metrics. Front. in Bioeng. Biotechnol. 10:962687, 2022.

    Article  Google Scholar 

  6. Ku, D. N., D. P. Giddens, C. K. Zarins, and S. Glagov. Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arteriosclerosis. 5:293–302, 1985.

    Article  CAS  PubMed  Google Scholar 

  7. Kolandavel, M. K., E.-T. Fruend, S. Ringgaard, and P. G. Walker. The effects of time varying curvature on species transport in coronary arteries. Ann. Biomed. Eng. 34:1820–1832, 2006.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Moore, J. E., Jr., et al. Fluid wall shear stress measurements in a model of the human abdominal aorta: oscillatory behavior and relationship to atherosclerosis. Atherosclerosis. 110:225–240, 1994.

    Article  CAS  PubMed  Google Scholar 

  9. Sakellarios, A., et al. Prediction of atherosclerotic disease progression using LDL transport modelling: a serial computed tomographic coronary angiographic study. Eur. Heart J. 18:11–18, 2017.

    Google Scholar 

  10. Toutouzas, K., et al. Accurate and reproducible reconstruction of coronary arteries and endothelial shear stress calculation using 3D OCT: comparative study to 3D IVUS and 3D QCA. Atherosclerosis. 240:510–519, 2015.

    Article  CAS  PubMed  Google Scholar 

  11. Gogas, B. D., et al. Feasibility of optical coherence tomography–derived computational fluid dynamics in calcified vessels to assess treatment with orbital atherectomy. JACC. 9:e65–e66, 2016.

    PubMed  Google Scholar 

  12. Van Der Giessen, A. G., et al. Plaque and shear stress distribution in human coronary bifurcations: a multi-slice computed tomography study. In: Summer Bioengineering Conference, 2007, pp. 477–478.

  13. Zhong, L., et al. Application of patient-specific computational fluid dynamics in coronary and intra-cardiac flow simulations: challenges and opportunities. Front. Physiol. 9:742, 2018.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  14. Masaryk, A. M., et al. In vitro and in vivo comparison of three MR measurement methods for calculating vascular shear stress in the internal carotid artery. Am. J. Neuroradiol. 20:237–245, 1999.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Fonken, J., et al. The impact of a limited field-of-view on computed hemodynamics in abdominal aortic aneurysms: evaluating the feasibility of completing ultrasound segmentations with parametric geometries. Ann. Biomed. Eng. 51:1–14, 2023.

    Article  Google Scholar 

  16. Deng, Z., et al. Noninvasive measurement of pressure gradient across a coronary stenosis using phase contrast (PC)-MRI: a feasibility study. Magn. Reson. Med. 77:529–537, 2017.

    Article  CAS  PubMed  Google Scholar 

  17. Poelma, C., P. Vennemann, R. Lindken, and J. Westerweel. In vivo blood flow and wall shear stress measurements in the vitelline network. Exp. Fluids. 45:703–713, 2008.

    Article  CAS  Google Scholar 

  18. Li, M., et al. High pulsatility flow induces adhesion molecule and cytokine mRNA expression in distal pulmonary artery endothelial cells. Ann. Biomed. Eng. 37:1082–1092, 2009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yiu, B. Y., and C. Alfred. Least-squares multi-angle Doppler estimators for plane-wave vector flow imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 63:1733–1744, 2016.

    Article  PubMed  Google Scholar 

  20. Haniel, J., et al. Efficacy of ultrasound vector flow imaging in tracking omnidirectional pulsatile flow. Med. Phys. 50:1699, 2022.

    Article  Google Scholar 

  21. Ekroll, I. K., et al. An extended least squares method for aliasing-resistant vector velocity estimation. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 63:1745–1757, 2016.

    Article  PubMed  Google Scholar 

  22. Chee, A. J., C. K. Ho, B. Y. Yiu, and C. Alfred. Time-resolved wall shear rate mapping using high-frame-rate ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 69:3367–3381, 2022.

    Article  PubMed  Google Scholar 

  23. Udesen, J., and J. A. Jensen. Investigation of transverse oscillation method. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 53:959–971, 2006.

    Article  PubMed  Google Scholar 

  24. Lenge, M., et al. Plane-wave transverse oscillation for high-frame-rate 2-D vector flow imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 62:2126–2137, 2015.

    Article  PubMed  Google Scholar 

  25. Salles, S., et al. 2-D arterial wall motion imaging using ultrafast ultrasound and transverse oscillations. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 62:1047–1058, 2015.

    Article  PubMed  Google Scholar 

  26. Jensen, J., et al. Fast plane wave 2-D vector flow imaging using transverse oscillation and directional beamforming. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 64:1050–1062, 2017.

    Article  PubMed  Google Scholar 

  27. Aizawa, K., et al. Brachial artery vasodilatory response and wall shear rate determined by multigate Doppler in a healthy young cohort. J. Appl. Physiol. 124:150–159, 2018.

    Article  PubMed  Google Scholar 

  28. Ramalli, A., et al. Continuous simultaneous recording of brachial artery distension and wall shear rate: a new boost for flow-mediated vasodilation. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 66:463–471, 2018.

    Article  PubMed  Google Scholar 

  29. Wang, I. C., H. Huang, W. T. Chang, and C. C. Huang. Wall shear stress mapping for human femoral artery based on ultrafast ultrasound vector Doppler estimations. Med. Phys. 48:6755–6764, 2021.

    Article  PubMed  Google Scholar 

  30. Poelma, C., et al. Ultrasound imaging velocimetry: toward reliable wall shear stress measurements. Eur. J. Mech. B. 35:70–75, 2012.

    Article  Google Scholar 

  31. Correia, M., et al. Quantitative imaging of coronary flows using 3D ultrafast Doppler coronary angiography. Phys. Med. Biol. 65:105013, 2020.

    Article  CAS  PubMed  Google Scholar 

  32. Pinter, S. Z., et al. Evaluation of umbilical vein blood volume flow in preeclampsia by angle-independent 3D sonography. J. Ultrasound Med. 37:1633–1640, 2018.

    Article  PubMed  Google Scholar 

  33. Welsh, A. W., et al. Three-dimensional US fractional moving blood volume: validation of renal perfusion quantification. Radiology. 293:460–468, 2019.

    Article  PubMed  Google Scholar 

  34. Pinter, S. Z., et al. Volumetric blood flow in transjugular intrahepatic portosystemic shunt revision using 3-dimensional Doppler sonography. J. Ultrasound Med. 34:257–266, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Correia, M., J. Provost, M. Tanter, and M. Pernot. 4D ultrafast ultrasound flow imaging: in vivo quantification of arterial volumetric flow rate in a single heartbeat. Phys. Med. Biol. 61:L48, 2016.

    Article  ADS  PubMed  Google Scholar 

  36. Hong, J., et al. A dual-mode imaging catheter for intravascular ultrasound application. IEEE Trans. Med. Imaging. 38:657–663, 2018.

    Article  Google Scholar 

  37. Janjic, J., et al. Sparse ultrasound image reconstruction from a shape-sensing single-element forward-looking catheter. IEEE Trans. Biomed. Eng. 65:2210–2218, 2018.

    Article  PubMed  Google Scholar 

  38. Kumar, V., et al. Unambiguous identification and visualization of an acoustically active catheter by ultrasound imaging in real time: theory, algorithm, and phantom experiments. IEEE Trans. Biomed. Eng. 65:1468–1475, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kim, S., B. Jing, and B. D. Lindsey. Forward-viewing estimation of 3D blood flow velocity fields by intravascular ultrasound: influence of the catheter on velocity estimation in stenoses. Ultrasonics. 117:106558, 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kim, S., et al. Forward-viewing ultrasound-based wall shear stress estimation in coronary arteries: comparison with computational fluid dynamics (Submitted).

  41. Lindsey, B. D., et al. 3-D intravascular characterization of blood flow velocity fields with a forward-viewing 2-D array. Ultrasound in Medicine Biology. 46:2560–2571, 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Kumar, A., et al. High coronary shear stress in patients with coronary artery disease predicts myocardial infarction. J. Am. Coll. Cardiol. 72:1926–1935, 2018.

    Article  PubMed  Google Scholar 

  43. Shi, Y., F. J. de Ana, S. J. Chetcuti, and M. O’Donnell. Motion artifact reduction for IVUS-based thermal strain imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 52:1312–1319, 2005.

    Article  PubMed  Google Scholar 

  44. Leung, K. E., et al. Motion compensation for intravascular ultrasound palpography. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 53:1269–1280, 2006.

    Article  PubMed  Google Scholar 

  45. Shiina, T., N. Nitta, H. Endo, and M. Yamagishi. Assessment of vulnerable coronary plaque by intravascular elasticity imaging. IEEE Ultrason. Symp. 2004:364–367, 2004.

    Google Scholar 

  46. Cormier, P., J. Porée, C. Bourquin, and J. Provost. Dynamic myocardial ultrasound localization angiography. IEEE Trans. Med. Imaging. 40:3379–3388, 2021.

    Article  PubMed  Google Scholar 

  47. Dilba, K., et al. The association between time-varying wall shear stress and the development of plaque ulcerations in carotid arteries from the plaque at risk study. Front. Cardiovasc. Med. 8:732646, 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Agra, E. J., et al. Left ventricular thinning and distension in pig hearts as a reproducible ex vivo model of functional mitral regurgitation. ASAIO J. (1992). 66:1016, 2020.

    Article  CAS  PubMed Central  Google Scholar 

  49. Amedi, A., et al. Hemodynamic outcomes after undersizing ring annuloplasty and focal suture annuloplasty for surgical repair of functional tricuspid regurgitation. J. Thorac. Cardiovasc. Surg. 164:76-87 e71, 2022.

    Article  PubMed  Google Scholar 

  50. Bernard, A., et al. 3D echocardiographic reference ranges for normal left ventricular volumes and strain: results from the EACVI NORRE study. Eur. Heart J. 18:475–483, 2017.

    Google Scholar 

  51. Jing, B., M. E. Brown, M. E. Davis, and B. D. Lindsey. Imaging the activation of low-boiling-point phase-change contrast agents in the presence of tissue motion using ultrafast inter-frame activation ultrasound imaging. Ultrasound Med. Biol. 46:1474–1489, 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Lindsey, B. D., et al. High resolution ultrasound superharmonic perfusion imaging: In vivo feasibility and quantification of dynamic contrast-enhanced acoustic angiography. Ann. Biomed. Eng. 45:939–948, 2017.

    Article  PubMed  Google Scholar 

  53. Newsome, I. G., T. M. Kierski, and P. A. Dayton. Assessment of the superharmonic response of microbubble contrast agents for acoustic angiography as a function of microbubble parameters. Ultrasound Med. Biol. 45:2515–2524, 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Dewan, M., G. D. Hager, and C. H. Lorenz. Image-based coronary tracking and beat-to-beat motion compensation: feasibility for improving coronary MR angiography. Magn. Reson. Med. 60:604–615, 2008.

    Article  PubMed  Google Scholar 

  55. Zahnd, G., et al. Evaluation of a Kalman-based block matching method to assess the bi-dimensional motion of the carotid artery wall in B-mode ultrasound sequences. Med. Image Anal. 17:573–585, 2013.

    Article  PubMed  Google Scholar 

  56. De Korte, C., et al. Morphological and mechanical information of coronary arteries obtained with intravascular elastography. Feasibility study in vivo. Eur. Heart J. 23:405–413, 2002.

    Article  PubMed  Google Scholar 

  57. Pisters, R., et al. Instantaneous wave-free ratio and fractional flow reserve in clinical practice. Neth. Hear. J. 26:385–392, 2018.

    Article  CAS  Google Scholar 

  58. Yeung, F., S. F. Levinson, and K. J. Parker. Multilevel and motion model-based ultrasonic speckle tracking algorithms. Ultrasound Med. Biol. 24:427–441, 1998.

    Article  CAS  PubMed  Google Scholar 

  59. Friemel, B. H., L. N. Bohs, and G. E. Trahey. Relative performance of two-dimensional speckle-tracking techniques: normalized correlation, non-normalized correlation and sum-absolute-difference. In: 1995 IEEE Ultrasonics Symposium. Proceedings. An International Symposium, 1995, pp. 1481–1484.

  60. Bohs, L. N., and G. E. Trahey. A novel method for angle independent ultrasonic imaging of blood flow and tissue motion. IEEE Trans. Biomed. Eng. 38:280–286, 1991.

    Article  CAS  PubMed  Google Scholar 

  61. Danilouchkine, M. G., F. Mastik, and A. F. van der Steen. Accuracy in prediction of catheter rotation in IVUS with feature-based optical flow—a phantom study. IEEE Trans. Inf Technol. Biomed. 12:356–365, 2008.

    Article  PubMed  Google Scholar 

  62. Demené, C., et al. Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases Doppler and fUltrasound sensitivity. IEEE Trans. Med. Imaging. 34:2271–2285, 2015.

    Article  PubMed  Google Scholar 

  63. Pihl, M. J., et al. A transverse oscillation approach for estimation of three-dimensional velocity vectors, part II: experimental validation. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 61:1608–1618, 2014.

    Article  PubMed  Google Scholar 

  64. Jensen, J., M. B. Stuart, and J. A. Jensen. High frame rate vector velocity estimation using plane waves and transverse oscillation. In: 2015 Ieee International Ultrasonics Symposium (Ius), 2015, pp. 1–4.

  65. Jensen, J. A. Comparison of vector velocity imaging using directional beamforming and transverse oscillation for a convex array transducer. In: Medical Imaging 2014: Ultrasonic Imaging and Tomography, 2014, pp. 279–286.

  66. Kim, S., J. M. Tempestti, A. Veneziani, and B. D. Lindsey. A new method for estimating wall shear stress estimation using Doppler ultrasound imaging in coronary arteries. (In preparation).

  67. Riemer, K., et al. Determining haemodynamic wall shear stress in the rabbit aorta in vivo using contrast-enhanced ultrasound image velocimetry. Ann. Biomed. Eng. 48:1728–1739, 2020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ge, J., et al. Intravascular ultrasound imaging of angiographically normal coronary arteries: a prospective study in vivo. Heart. 71:572–578, 1994.

    Article  CAS  Google Scholar 

  69. Zeng, D., et al. A study on the compliance of a right coronary artery and its impact on wall shear stress. J. Biomech. Eng. 130:041014, 2008.

    Article  PubMed  Google Scholar 

  70. Jhunjhunwala, P., et al. Non-Newtonian blood flow in left coronary arteries with varying stenosis: a comparative study. Mol. Cell. Biomech. 13:1–21, 2016.

    Google Scholar 

  71. Doucette, J. W., et al. Validation of a Doppler guide wire for intravascular measurement of coronary artery flow velocity. Circulation. 85:1899–1911, 1992.

    Article  CAS  PubMed  Google Scholar 

  72. Ofili, E. O., A. J. Labovitz, and M. J. Kern. Coronary flow velocity dynamics in normal and diseased arteries. Am. J. Cardiol. 71:D3–D9, 1993.

    Article  Google Scholar 

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Acknowledgements

The authors thank John Oshinski for helpful discussions.

Funding

This work is supported by Grant R01EB031101 from the U.S. National Institutes of Health.

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Authors and Affiliations

  1. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 801 Ferst Dr., Atlanta, GA, 30332, USA

    Saeyoung Kim

  2. Interdisciplinary BioEngineering Graduate Program, Georgia Institute of Technology, 315 Ferst Dr., Atlanta, GA, 30332, USA

    Saeyoung Kim, Muralidhar Padala & Brooks D. Lindsey

  3. Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, GA, 30332, USA

    Bowen Jing & Brooks D. Lindsey

  4. Division of Cardiothoracic Surgery, Joseph P. Whitehead Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA

    Brooks A. Lane & Muralidhar Padala

  5. Structural Heart Research and Innovation Laboratory, Carlyle Fraser Heart Center, Emory University Hospital Midtown, Atlanta, GA, USA

    Brooks A. Lane & Muralidhar Padala

  6. Department of Mathematics, Emory University, 400 Dowman Dr NE, Atlanta, GA, 30322, USA

    Jimena Martín Tempestti & Alessandro Veneziani

  7. Department of Computer Science, Emory University, 400 Dowman Dr NE, Atlanta, GA, 30322, USA

    Alessandro Veneziani

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Kim, S., Jing, B., Lane, B.A. et al. Dynamic Coronary Blood Flow Velocity and Wall Shear Stress Estimation Using Ultrasound in an Ex Vivo Porcine Heart. Cardiovasc Eng Tech 15, 65–76 (2024). https://doi.org/10.1007/s13239-023-00697-9

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