Chemical engineering methods in better understanding of blood hydrodynamics in atherosclerosis disease

References

1. Alpert, JS. A few unpleasant facts about atherosclerotic arterial disease in the United States and the world. Am J Med 2012;125:839–40. https://doi.org/10.1016/j.amjmed.2012.04.031.Suche in Google Scholar PubMed

2. Amini, M, Zayeri, F, Salehi, M. Trend analysis of cardiovascular disease mortality, incidence, and mortality-to-incidence ratio: results from global burden of disease study 2017. BMC Publ Health 2021;21. https://doi.org/10.1186/s12889-021-10429-0.Suche in Google Scholar PubMed PubMed Central

3. Kim, H, Kim, S, Han, S, Rane, PP, Fox, KM, Qian, Y, et al.. Prevalence and incidence of atherosclerotic cardiovascular disease and its risk factors in Korea: a nationwide population-based study. BMC Publ Health 2019;19. https://doi.org/10.1186/s12889-019-7439-0.Suche in Google Scholar PubMed PubMed Central

4. Spector, R. New insight into the dietary cause of atherosclerosis: implications for pharmacology. J Pharmacol Exp Therapeut 2016;358:103–8. https://doi.org/10.1124/jpet.116.233296.Suche in Google Scholar PubMed

5. Henning, RJ. Obesity and obesity-induced inflammatory disease contribute to atherosclerosis: a review of the pathophysiology and treatment of obesity. Am J Cardiovasc Dis 2021;11.Suche in Google Scholar

6. Giersiepen, M, Wurzinger, LJ, Opitz, R, Reul, H. Estimation of shear stress-related blood damage in heart valve prostheses-in vitro comparison of 25 aortic valves. Int J Artif Organs 1990;13:300–6. https://doi.org/10.1177/039139889001300507.Suche in Google Scholar

7. Lacasse, D, Garon, A, Pelletier, D. Mechanical hemolysis in blood flow: user-independent predictions with the solution of a partial differential equation. Comput Methods Biomech Biomed Eng 2007;10:1–12. https://doi.org/10.1080/10255840600985535.Suche in Google Scholar PubMed

8. Jędrzejczak, K, Makowski, Ł, Orciuch, W. Model of blood rheology including hemolysis based on population balance. Commun Nonlinear Sci Numer Simul 2023;116:106802. https://doi.org/10.1016/J.CNSNS.2022.106802.Suche in Google Scholar

9. Michel, JB, Martin-Ventura, JL. Red blood cells and hemoglobin in human atherosclerosis and related arterial diseases. Int J Mol Sci 2020;21:1–20. https://doi.org/10.3390/ijms21186756.Suche in Google Scholar PubMed PubMed Central

10. Jędrzejczak, K, Antonowicz, A, Makowski, Ł, Orciuch, W, Wojtas, K, Kozłowski, M. Computational fluid dynamics validated by micro particle image velocimetry to estimate the risk of hemolysis in arteries with atherosclerotic lesions. Chem Eng Res Des 2023;196:342–53. https://doi.org/10.1016/j.cherd.2023.06.041.Suche in Google Scholar

11. Jędrzejczak, K, Orciuch, W, Wojtas, K, Kozłowski, M, Piasecki, P, Narloch, J, et al.. Prediction of hemodynamic-related hemolysis in carotid stenosis and aiding in treatment planning and risk stratification using computational fluid dynamics. Biomedicines 2024;12. https://doi.org/10.3390/biomedicines12010037.Suche in Google Scholar PubMed PubMed Central

12. https://www.bruker.com/en/products-and-solutions/preclinical-imaging/mri/biospec/biospec-180-11.html [Accessed 24 Sep 2024].Suche in Google Scholar

13. Jędrzejczak, K, Makowski, Ł, Orciuch, W, Wojtas, K, Kozłowski, M. Hemolysis of red blood cells in blood vessels modeled via computational fluid dynamics. Int J Numer Method Biomed Eng 2023;39. https://doi.org/10.1002/cnm.3699.Suche in Google Scholar PubMed

14. Wojtas, K, Kozłowski, M, Orciuch, W, Makowski, Ł. Computational fluid dynamics simulations of mitral paravalvular leaks in human heart. Materials 2021;14. https://doi.org/10.3390/ma14237354.Suche in Google Scholar PubMed PubMed Central

15. Kozłowski, M, Wojtas, K, Orciuch, W, Smolka, G, Wojakowski, W, Makowski, Ł. Parameters of flow through paravalvular leak channels from computational fluid dynamics simulations-data from real-life cases and comparison with a simplified model. J Clin Med 2022;11. https://doi.org/10.3390/jcm11185355.Suche in Google Scholar PubMed PubMed Central

16. Kozłowski, M, Wojtas, K, Orciuch, W, Jędrzejek, M, Smolka, G, Wojakowski, W, et al.. Potential applications of computational fluid dynamics for predicting hemolysis in mitral paravalvular leaks. J Clin Med 2021;10. https://doi.org/10.3390/jcm10245752.Suche in Google Scholar PubMed PubMed Central

17. Gori, F, Boghi, A. Three-dimensional numerical simulation of blood flow in two coronary stents. Numeri Heat Transf A Appl 2011;59:231–46. https://doi.org/10.1080/10407782.2011.541147.Suche in Google Scholar

18. Albadawi, M, Abuouf, Y, Elsagheer, S, Ookawara, S, Ahmed, M. Predicting the onset of consequent stenotic regions in carotid arteries using computational fluid dynamics. Phys Fluids 2021;33. https://doi.org/10.1063/5.0068998.Suche in Google Scholar

19. AL-Rawi, M, AL-Jumaily, AM, Belkacemi, D. Non-invasive diagnostics of blockage growth in the descending aorta-computational approach. Med Biol Eng Comput 2022;60:3265–79. https://doi.org/10.1007/s11517-022-02665-2.Suche in Google Scholar PubMed PubMed Central

20. Kopylova, V, Boronovskiy, S, Nartsissov, Y. Approaches to vascular network, blood flow, and metabolite distribution modeling in brain tissue. Biophys Rev 2023;15:1335–50. https://doi.org/10.1007/s12551-023-01106-0.Suche in Google Scholar PubMed PubMed Central

21. Viola, F, Del Corso, G, De Paulis, R, Verzicco, R. GPU accelerated digital twins of the human heart open new routes for cardiovascular research. Sci Rep 2023;13. https://doi.org/10.1038/s41598-023-34098-8.Suche in Google Scholar PubMed PubMed Central

22. Lodi Rizzini, M, Candreva, A, Mazzi, V, Pagnoni, M, Chiastra, C, Aben, JP, et al.. Blood flow energy identifies coronary lesions culprit of future myocardial infarction. Ann Biomed Eng 2023;52:226–38. https://doi.org/10.1007/s10439-023-03362-3.Suche in Google Scholar PubMed PubMed Central

23. Belkacemi, D, Tahar Abbes, M, Al-Rawi, M, Al-Jumaily, AM, Bachene, S, Laribi, B. Intraluminal thrombus characteristics in AAA patients: non-invasive diagnosis using CFD. Bioengineering 2023;10. https://doi.org/10.3390/bioengineering10050540.Suche in Google Scholar PubMed PubMed Central

24. Khan, PM, Sharma, SD, Chakraborty, S, Roy, S. Effect of heart rate on the hemodynamics in healthy and stenosed carotid arteries. Phys Fluids 2023;35. https://doi.org/10.1063/5.0153323.Suche in Google Scholar

25. Gils, C, Hansen, DL, Nybo, M, Frederiksen, H. Elevated hemolysis index is associated with higher risk of cardiovascular diseases. Clin Chem Lab Med 2023;61:1497–505. https://doi.org/10.1515/cclm-2023-0114.Suche in Google Scholar PubMed

26. Carbonaro, D, Mezzadri, F, Ferro, N, De Nisco, G, Audenino, AL, Gallo, D, et al.. Design of innovative self-expandable femoral stents using inverse homogenization topology optimization. Comput Methods Appl Mech Eng 2023;416:116288. https://doi.org/10.1016/J.CMA.2023.116288.Suche in Google Scholar

27. Valentim, MXG, Zinani, FSF, da Fonseca, CE, Wermuth, DP. Systematic review on the application of computational fluid dynamics as a tool for the design of coronary artery stents. Beni Suef Univ J Basic Appl Sci 2023;12. https://doi.org/10.1186/s43088-023-00382-9.Suche in Google Scholar

28. Jayendiran, R, Nour, B, Ruimi, A. Fluid-structure interaction (FSI) analysis of stent-graft for aortic endovascular aneurysm repair (EVAR): material and structural considerations. J Mech Behav Biomed Mater 2018;87:95–110. https://doi.org/10.1016/J.JMBBM.2018.07.020.Suche in Google Scholar PubMed

29. Martin, DM, Murphy, EA, Boyle, FJ. Computational fluid dynamics analysis of balloon-expandable coronary stents: influence of stent and vessel deformation. Med Eng Phys 2014;36:1047–56. https://doi.org/10.1016/j.medengphy.2014.05.011.Suche in Google Scholar PubMed

30. Rigatelli, G, Zuin, M, Dell’Avvocata, F, Vassilev, D, Daggubati, R, Nguyen, T, et al.. Evaluation of coronary flow conditions in complex coronary artery bifurcations stenting using computational fluid dynamics: impact of final proximal optimization technique on different double-stent techniques. Cardiovasc Revascularization Med 2017;18:233–40. https://doi.org/10.1016/J.CARREV.2017.01.002.Suche in Google Scholar PubMed

31. Wüstenhagen, C, Borowski, F, Grabow, N, Schmitz, KP, Stiehm, M. Comparison of stented bifurcation and straight vessel 3D-simulation with a prior simulated velocity profile inlet. Curr Directions Biomed Eng 2016;2:293–6. https://doi.org/10.1515/cdbme-2016-0065.Suche in Google Scholar

32. Gundert, TJ, Marsden, AL, Yang, W, LaDisa, J, JF. Optimization of cardiovascular stent design using computational fluid dynamics. J Biomech Eng 2012;134. https://doi.org/10.1115/1.4005542.Suche in Google Scholar PubMed

33. Boite, Y, Suaiden Klein, T, de Andrade Medronho, R, Wajnberg, E. Numerical simulation of flow-diverting stent: comparison between branches in bifurcation brain aneurysm. Biomech Model Mechanobiol 2023;22:1801–14. https://doi.org/10.1007/s10237-023-01733-2.Suche in Google Scholar PubMed

34. Kim, HJ, Vignon-Clementel, IE, Coogan, JS, Figueroa, CA, Jansen, KE, Taylor, CA. Patient-specific modeling of blood flow and pressure in human coronary arteries. Ann Biomed Eng 2010;38:3195–209. https://doi.org/10.1007/s10439-010-0083-6.Suche in Google Scholar PubMed

35. Shahid, L, Rice, J, Berhane, H, Rigsby, C, Robinson, J, Griffin, L, et al.. Enhanced 4D flow MRI-based CFD with adaptive mesh refinement for flow dynamics assessment in coarctation of the aorta. Ann Biomed Eng 2022;50:1001–16. https://doi.org/10.1007/s10439-022-02980-7.Suche in Google Scholar PubMed PubMed Central

36. Razavi, A, Shirani, E, Sadeghi, MR. Numerical simulation of blood pulsatile flow in a stenosed carotid artery using different rheological models. J Biomech 2011;44:2021–30. https://doi.org/10.1016/j.jbiomech.2011.04.023.Suche in Google Scholar PubMed

37. Boyd, J, Buick, JM, Green, S. Analysis of the Casson and Carreau-Yasuda non-Newtonian blood models in steady and oscillatory flows using the Lattice Boltzmann method. Phys Fluids 2007;19:93103. https://doi.org/10.1063/1.2772250.Suche in Google Scholar

38. Siauw, WL, Ng, EYK, Mazumdar, J. Unsteady stenosis flow prediction: a comparative study of non-Newtonian models with operator splitting scheme. Med Eng Phys 2000;22:265–77. https://doi.org/10.1016/S1350-4533(00)00036-9.Suche in Google Scholar PubMed

39. Shibeshi, SS, Collins, WE. The rheology of blood flow in a branched arterial system. Appl Rheol 2005;15:398–405. https://doi.org/10.1515/arh-2005-0020.Suche in Google Scholar

40. Johnston, BM, Johnston, PR, Corney, S, Kilpatrick, D. Non-Newtonian blood flow in human right coronary arteries: transient simulations. J Biomech 2006;39:1116–28. https://doi.org/10.1016/j.jbiomech.2005.01.034.Suche in Google Scholar PubMed

41. Doost, SN, Zhong, L, Su, B, Morsi, YS. The numerical analysis of non-Newtonian blood flow in human patient-specific left ventricle. Comput Methods Progr Biomed 2016;127:232–47. https://doi.org/10.1016/j.cmpb.2015.12.020.Suche in Google Scholar PubMed

42. Morbiducci, U, Gallo, D, Massai, D, Ponzini, R, Deriu, MA, Antiga, L, et al.. On the importance of blood rheology for bulk flow in hemodynamic models of the carotid bifurcation. J Biomech 2011;44:2427–38. https://doi.org/10.1016/j.jbiomech.2011.06.028.Suche in Google Scholar PubMed

43. Ionescu, CM. A memory-based model for blood viscosity. Commun Nonlinear Sci Numer Simul 2017;45:29–34. https://doi.org/10.1016/j.cnsns.2016.09.017.Suche in Google Scholar

44. Giannokostas, K, Moschopoulos, P, Varchanis, S, Dimakopoulos, Y, Tsamopoulos, J. Advanced constitutive modeling of the thixotropic elasto-visco-plastic behavior of blood: description of the model and rheological predictions. Materials 2020;13:4184. https://doi.org/10.3390/ma13184184.Suche in Google Scholar PubMed PubMed Central

45. Jariwala, S, Horner, JS, Wagner, NJ, Beris, AN. Application of population balance-based thixotropic model to human blood. J Nonnewton Fluid Mech 2020;281:104294. https://doi.org/10.1016/j.jnnfm.2020.104294.Suche in Google Scholar

46. Owens, RG. A new microstructure-based constitutive model for human blood. J Nonnewton Fluid Mech 2006;140:57–70. https://doi.org/10.1016/j.jnnfm.2006.01.015.Suche in Google Scholar

47. Moyers-Gonzalez, M, Owens, RG, Fang, J. A non-homogeneous constitutive model for human blood. Part 1. Model derivation and steady flow. J Fluid Mech 2008;617:327–54. https://doi.org/10.1017/s002211200800428x.Suche in Google Scholar

48. Moyers-Gonzalez, MA, Owens, RG. A non-homogeneous constitutive model for human blood: Part II. Asymptotic solution for large péclet numbers. J Nonnewton Fluid Mech 2008;155:146–60. https://doi.org/10.1016/j.jnnfm.2008.06.009.Suche in Google Scholar

49. Moyers-Gonzalez, MA, Owens, RG, Fang, J. A non-homogeneous constitutive model for human blood: Part III. Oscillatory flow. J Nonnewton Fluid Mech 2008;155:161–73. https://doi.org/10.1016/j.jnnfm.2008.04.001.Suche in Google Scholar

50. Antonowicz, A, Wojtas, K, Makowski, Ł, Orciuch, W, Kozłowski, M. Particle image velocimetry of 3D-printed anatomical blood vascular models affected by atherosclerosis. Materials 2023;16:1055. https://doi.org/10.3390/ma16031055.Suche in Google Scholar PubMed PubMed Central

51. Vergine, F, Maddalena, L. Stereoscopic particle image velocimetry measurements of supersonic, turbulent, and interacting streamwise vortices: challenges and application. Prog Aero Sci 2014;66:1–16. https://doi.org/10.1016/J.PAEROSCI.2013.12.001.Suche in Google Scholar

52. Saaid, H, Voorneveld, J, Schinkel, C, Westenberg, J, Gijsen, F, Segers, P, et al.. Tomographic PIV in a model of the left ventricle: 3D flow past biological and mechanical heart valves. J Biomech 2019;90:40–9. https://doi.org/10.1016/J.JBIOMECH.2019.04.024.Suche in Google Scholar

53. Arvand, A, Hormes, M, Reul, H. A validated computational fluid dynamics model to estimate hemolysis in a rotary blood pump. Artif Organs 2005;29:531–40. https://doi.org/10.1111/j.1525-1594.2005.29089.x.Suche in Google Scholar PubMed

54. Goubergrits, L, Affeld, K. Numerical estimation of blood damage in artificial organs. Artif Organs 2004;28:499–507. https://doi.org/10.1111/j.1525-1594.2004.07265.x.Suche in Google Scholar PubMed

55. Gu, L, Smith, WA. Evaluation of computational models for hemolysis estimation. Am Soc Artif Intern Organs J 2005;51:202–7. https://doi.org/10.1097/01.mat.0000161939.29905.93.Suche in Google Scholar PubMed

56. Arwatz, G, Smits, AJ. A viscoelastic model of shear-induced hemolysis in laminar flow. Biorheology 2013;50:45–55. https://doi.org/10.3233/bir-130626.Suche in Google Scholar

57. Arora, D. Computational hemodynamics: hemolysis and viscoelasticity. Houston, Texas, USA: Rice University; 2006.Suche in Google Scholar

58. Ezzeldin, HM, de Tullio, MD, Vanella, M, Solares, SD, Balaras, E. A strain-based model for mechanical hemolysis based on a coarse-grained red blood cell model. Ann Biomed Eng 2015;43:1398–409. https://doi.org/10.1007/s10439-015-1273-z.Suche in Google Scholar PubMed

59. Vitello, DJ, Ripper, RM, Fettiplace, MR, Weinberg, GL, Vitello, JM. Blood density is nearly equal to water density: a validation study of the gravimetric method of measuring intraoperative blood loss. J Vet Med 2015;2015. https://doi.org/10.1155/2015/152730.Suche in Google Scholar PubMed PubMed Central

60. Chen, Y, Sharp, MK. A strain-based flow-induced hemolysis prediction model calibrated by in vitro erythrocyte deformation measurements. Artif Organs 2011;35:145–56. https://doi.org/10.1111/j.1525-1594.2010.01050.x.Suche in Google Scholar PubMed

61. Dirkes, N, Key, F, Behr, M. Eulerian formulation of the tensor-based morphology equations for strain-based blood damage modeling. Comput Methods Appl Mech Eng 2024;426. https://doi.org/10.1016/j.cma.2024.116979.Suche in Google Scholar

62. Shi, Y, Lawford, P, Hose, R. Review of zero-D and 1-D models of blood flow in the cardiovascular system. Biomed Eng Online 2011;10. https://doi.org/10.1186/1475-925x-10-33.Suche in Google Scholar

63. Boujena, S, Kafi, O, El Khatib, N. A 2D mathematical model of blood flow and its interactions in an atherosclerotic artery. Math Model Nat Phenom 2014;9:46–68. https://doi.org/10.1051/mmnp/20149605.Suche in Google Scholar

64. Trejo-Soto, C, Hernández-Machado, A. Normalization of blood viscosity according to the hematocrit and the shear rate. Micromachines 2022;13. https://doi.org/10.3390/mi13030357.Suche in Google Scholar PubMed PubMed Central

65. Ameenuddin, M, Anand, M, Massoudi, M. Effects of shear-dependent viscosity and hematocrit on blood flow. Appl Math Comput 2019;356:299–311. https://doi.org/10.1016/J.AMC.2019.03.028.Suche in Google Scholar

66. Pinto, SIS, Romano, E, António, CC, Sousa, LC, Castro, CF. The impact of non-linear viscoelastic property of blood in right coronary arteries hemodynamics — a numerical implementation. Int J Non Lin Mech 2020;123:103477. https://doi.org/10.1016/J.IJNONLINMEC.2020.103477.Suche in Google Scholar

67. Garon, A, Farinas, M-I. Fast three-dimensional numerical hemolysis approximation. Artif Organs 2004;28:1016–25. https://doi.org/10.1111/j.1525-1594.2004.00026.x.Suche in Google Scholar PubMed

68. Rydquist, G, Esmaily, M. A cell-resolved, Lagrangian solver for modeling red blood cell dynamics in macroscale flows. J Comput Phys 2022;461:111204. https://doi.org/10.1016/j.jcp.2022.111204.Suche in Google Scholar PubMed PubMed Central

69. Cantwell, CD, Moxey, D, Comerford, A, Bolis, A, Rocco, G, Mengaldo, G, et al.. Nektar++: an open-source spectral/hp element framework. Comput Phys Commun 2015;192:205–19. https://doi.org/10.1016/J.CPC.2015.02.008.Suche in Google Scholar

70. Tan, J, Sinno, TR, Diamond, SL. A parallel fluid–solid coupling model using LAMMPS and Palabos based on the immersed boundary method. J Comput Sci 2018;25:89–100. https://doi.org/10.1016/J.JOCS.2018.02.006.Suche in Google Scholar

71. Latt, J, Malaspinas, O, Kontaxakis, D, Parmigiani, A, Lagrava, D, Brogi, F, et al.. Palabos: parallel Lattice Boltzmann solver. Comput Math Appl 2021;81:334–50. https://doi.org/10.1016/J.CAMWA.2020.03.022.Suche in Google Scholar

72. Zhang, Y, Adams, J, Wang, VY, Horwitz, L, Tartibi, M, Morgan, AE, et al.. A finite element model of the cardiac ventricles with coupled circulation: biventricular mesh generation with hexahedral elements, airbags and a functional mockup interface to the circulation. Comput Biol Med 2021;137:104840. https://doi.org/10.1016/J.COMPBIOMED.2021.104840.Suche in Google Scholar

73. Griffith, BE. Immersed boundary model of aortic heart valve dynamics with physiological driving and loading conditions. Int J Numer Method Biomed Eng 2012;28:317–45. https://doi.org/10.1002/cnm.1445.Suche in Google Scholar PubMed

74. Crespo, AJC, Domínguez, JM, Rogers, BD, Gómez-Gesteira, M, Longshaw, S, Canelas, R, et al.. DualSPHysics: open-source parallel CFD solver based on smoothed particle hydrodynamics (SPH). Comput Phys Commun 2015;187:204–16. https://doi.org/10.1016/J.CPC.2014.10.004.Suche in Google Scholar

75. Africa, PC, Fumagalli, I, Bucelli, M, Zingaro, A, Fedele, M, Dede’, L, et al.. Lifex-cfd: an open-source computational fluid dynamics solver for cardiovascular applications. Comput Phys Commun 2024;296:109039. https://doi.org/10.1016/J.CPC.2023.109039.Suche in Google Scholar

76. Updegrove, A, Wilson, NM, Merkow, J, Lan, H, Marsden, AL, Shadden, SC. SimVascular: an open source pipeline for cardiovascular simulation. Ann Biomed Eng 2017;45:525–41. https://doi.org/10.1007/s10439-016-1762-8.Suche in Google Scholar PubMed PubMed Central

77. Chimakurthi, SK, Reuss, S, Tooley, M, Scampoli, S. ANSYS workbench system coupling: a state-of-the-art computational framework for analyzing Multiphysics problems. Eng Comput 2018;34:385–411. https://doi.org/10.1007/s00366-017-0548-4.Suche in Google Scholar

78. Sun, L, Ding, L, Li, L, Yin, N, Yang, N, Zhang, Y, et al.. Hemodynamic characteristics of cardiovascular system in simulated zero and partial gravities based on CFD modeling and simulation. Life 2023;13. https://doi.org/10.3390/life13020407.Suche in Google Scholar PubMed PubMed Central

79. Zhao, S, Wu, W, Samant, S, Khan, B, Kassab, GS, Watanabe, Y, et al.. Patient-specific computational simulation of coronary artery bifurcation stenting. Sci Rep 2021;11. https://doi.org/10.1038/s41598-021-95026-2.Suche in Google Scholar PubMed PubMed Central

80. Bouma, BE, Tearney, GJ, Yabushita, H, Shishkov, M, Kauffman, CR, DeJoseph Gauthier, D, et al.. Evaluation of intracoronary stenting by intravascular optical coherence tomography. Heart 2003;89:317–20. https://doi.org/10.1136/heart.89.3.317.Suche in Google Scholar PubMed PubMed Central

81. Jang, I-K, Bouma, BE, Kang, D-H, Park, S-J, Park, S-W, Seung, K-B, et al.. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol 2002;39. https://doi.org/10.1016/s0735-1097(01)01799-5.Suche in Google Scholar PubMed

82. Dohad, S, Zhu, A, Krishnan, S, Wang, F, Wang, S, Cox, J, et al.. Optical coherence tomography guided carotid artery stent procedure: technique and potential applications. Cathet Cardiovasc Interv 2018;91:521–30. https://doi.org/10.1002/ccd.27344.Suche in Google Scholar PubMed

83. Schwindt, AG, Bennett, JG, Crowder, WH, Dohad, S, Janzer, SF, George, JC, et al.. Lower extremity revascularization using optical coherence tomography-guided directional atherectomy: final results of the EValuation of the PantheriS optical COherence tomography ImagiNg atherectomy system for use in the peripheral vasculature (VISION) study. J Endovasc Ther 2017;24:355–66. https://doi.org/10.1177/1526602817701720.Suche in Google Scholar PubMed

84. Araki, M, Park, SJ, Dauerman, HL, Uemura, S, Kim, JS, Di Mario, C, et al.. Optical coherence tomography in coronary atherosclerosis assessment and intervention. Nat Rev Cardiol 2022;19:684–703. https://doi.org/10.1038/s41569-022-00687-9.Suche in Google Scholar PubMed PubMed Central

85. Waxman, S, Dixon, SR, L’Allier, P, Moses, JW, Petersen, JL, Cutlip, D, et al.. In vivo validation of a catheter-based near-infrared spectroscopy system for detection of lipid core coronary plaques. Initial results of the SPECTACL study. JACC Cardiovasc Imaging 2009;2:858–68. https://doi.org/10.1016/j.jcmg.2009.05.001.Suche in Google Scholar PubMed

86. Finn, AV, Kolodgie, FD, Virmani, R. Correlation between carotid intimal/medial thickness and atherosclerosis: a point of view from pathology. Arterioscler Thromb Vasc Biol 2010;30:177–81. https://doi.org/10.1161/atvbaha.108.173609.Suche in Google Scholar

87. Lal, BK, Hobson, RW, Pappas, PJ, Kubicka, R, Hameed, M, Chakhtura, EY, et al.. Pixel distribution analysis of B-mode ultrasound scan images predicts histologic features of atherosclerotic carotid plaques. J Vasc Surg 2002;35:1210–17. https://doi.org/10.1067/mva.2002.122888.Suche in Google Scholar PubMed

88. Noflatscher, M, Hunjadi, M, Schreinlechner, M, Sommer, P, Lener, D, Theurl, M, et al.. Inverse correlation of cholesterol efflux capacity with peripheral plaque volume measured by 3D ultrasound. Biomedicines 2023;11:1918. https://doi.org/10.3390/biomedicines11071918.Suche in Google Scholar PubMed PubMed Central

89. Hegner, A, Wittek, A, Derwich, W, Huß, A, Gámez, AJ, Blase, C. Using averaged models from 4D ultrasound strain imaging allows to significantly differentiate local wall strains in calcified regions of abdominal aortic aneurysms. Biomech Model Mechanobiol 2023;22:1709–27. https://doi.org/10.1007/s10237-023-01738-x.Suche in Google Scholar PubMed PubMed Central

90. Moneta, GL, Edwards, JM, Chitwood, RW, Taylor, LM, Lee, RW, Cummings, CA, et al.. Correlation of North American symptomatic carotid endarterectomy trial (NASCET) angiographic definition of 70% to 99% internal carotid artery stenosis with duplex scanning. J Vasc Surg 1993;17:152–9. https://doi.org/10.1016/0741-5214(93)90019-I.Suche in Google Scholar

91. Josephson, SA, Bryant, SO, Mak, HK, Johnston, SC, Dillon, WP, Smith, WS. Evaluation of carotid stenosis using CT angiography in the initial evaluation of stroke and TIA. Neurology 2004;63:457. https://doi.org/10.1212/01.WNL.0000135154.53953.2C.Suche in Google Scholar PubMed

92. Maurovich-Horvat, P, Ferencik, M, Voros, S, Merkely, B, Hoffmann, U. Comprehensive plaque assessment by coronary CT angiography. Nat Rev Cardiol 2014;11:390–402. https://doi.org/10.1038/nrcardio.2014.60.Suche in Google Scholar PubMed

93. Divakaran, S, Cheezum, MK, Hulten, EA, Bittencourt, MS, Silverman, MG, Nasir, K, et al.. Use of cardiac CT and calcium scoring for detecting coronary plaque: implications on prognosis and patient management. Br J Radiol 2015;88. https://doi.org/10.1259/bjr.20140594.Suche in Google Scholar PubMed PubMed Central

94. Motoyama, S, Ito, H, Sarai, M, Kondo, T, Kawai, H, Nagahara, Y, et al.. Plaque characterization by coronary computed tomography angiography and the likelihood of acute coronary events in mid-term follow-up. J Am Coll Cardiol 2015;66:337–46. https://doi.org/10.1016/J.JACC.2015.05.069.Suche in Google Scholar PubMed

95. Maurovich-Horvat, P, Hoffmann, U, Vorpahl, M, Nakano, M, Virmani, R, Alkadhi, H. The napkin-ring sign: CT signature of high-risk coronary plaques? JACC Cardiovasc Imaging 2010;3:440–4. https://doi.org/10.1016/J.JCMG.2010.02.003.Suche in Google Scholar

96. Dweck, MR, Williams, MC, Moss, AJ, Newby, DE, Fayad, ZA. Computed tomography and cardiac magnetic resonance in ischemic heart disease. J Am Coll Cardiol 2016;68:2201–16. https://doi.org/10.1016/J.JACC.2016.08.047.Suche in Google Scholar PubMed PubMed Central

97. Akçakaya, M, Basha, TA, Chan, RH, Manning, WJ, Nezafat, R. Accelerated isotropic sub-millimeter whole-heart coronary MRI: compressed sensing versus parallel imaging. Magn Reson Med 2014;71:815–22. https://doi.org/10.1002/mrm.24683.Suche in Google Scholar PubMed PubMed Central

98. Ong, WY, Im, K, Anias, EGD, Atthias, M, Tuber, S, Lamm, CDF, et al.. Coronary magnetic resonance angiography for the detection of coronary stenoses. N Engl J Med 2001;345.10.1056/NEJMoa010866Suche in Google Scholar PubMed

99. Hatsukami, TS, Ross, R, Polissar, NL, Yuan, C. Visualization of fibrous cap thickness and rupture in human atherosclerotic carotid plaque in vivo with high-resolution magnetic resonance imaging. Circulation 2000;102:959–64.10.1161/01.CIR.102.9.959Suche in Google Scholar

100. Kerwin, WS, Zhao, X, Chun, Y, Hatsukami, TS, Maravilla, KR, Underhill, HR, et al.. Contrast-enhanced MRI of carotid atherosclerosis: dependence on contrast agent. J Magn Reson Imag 2009;30:35–40. https://doi.org/10.1002/jmri.21826.Suche in Google Scholar PubMed PubMed Central

101. Zhang, J, Rothenberger, SM, Brindise, MC, Markl, M, Rayz, VL, Vlachos, PP. Wall shear stress estimation for 4D flow MRI using Navier–Stokes equation correction. Ann Biomed Eng 2022;50:1810–25. https://doi.org/10.1007/s10439-022-02993-2.Suche in Google Scholar PubMed PubMed Central

102. Nath, R, Kazemi, A, Callahan, S, Stoddard, MF, Amini, AA. 4Dflow-VP-Net: a deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI. Magn Reson Med 2023;90:2175–89. https://doi.org/10.1002/mrm.29791.Suche in Google Scholar PubMed PubMed Central

103. Sotelo, J, Mura, J, Hurtado, D, Uribe, S. A novel MATLAB toolbox for processing 4D flow MRI data. USA: International Society of Magnetic Resonance in Medicine (ISMRM); 2019.Suche in Google Scholar

104. Dyverfeldt, P, Bissell, M, Barker, AJ, Bolger, AF, Carlhäll, CJ, Ebbers, T, et al.. 4D flow cardiovascular magnetic resonance Consensus Statement. J Cardiovasc Magn Reson 2015;17:72. https://doi.org/10.1186/S12968-015-0174-5.Suche in Google Scholar

105. DeMarchi, N, White, C. Echo particle image velocimetry. J Vis Exp 2012:4265. https://doi.org/10.3791/4265.Suche in Google Scholar PubMed PubMed Central

106. Voorneveld, J, Saaid, H, Schinkel, C, Radeljic, N, Lippe, B, Gijsen, FJH, et al.. 4-D echo-particle image velocimetry in a left ventricular phantom. Ultrasound Med Biol 2020;46:805–17. https://doi.org/10.1016/J.ULTRASMEDBIO.2019.11.020.Suche in Google Scholar PubMed

107. Caridi, GCA, Torta, E, Mazzi, V, Chiastra, C, Audenino, AL, Morbiducci, U, et al.. Smartphone-based particle image velocimetry for cardiovascular flows applications: a focus on coronary arteries. Front Bioeng Biotechnol 2022;10. https://doi.org/10.3389/fbioe.2022.1011806.Suche in Google Scholar PubMed PubMed Central

108. Park, H, Yeom, E, Lee, SJ. X-ray PIV measurement of blood flow in deep vessels of a rat: an in vivo feasibility study. Sci Rep 2016;6. https://doi.org/10.1038/srep19194.Suche in Google Scholar PubMed PubMed Central