Computational fluid dynamics modelling in cardiovascular medicine

Heart

Volumn 102, Issue 1, 2016, Pages 18-28

  Morris, Paul D ^a   Narracott, Andrew ^a   Von Tengg Kobligk, Hendrik ^b   Soto, Daniel Alejandro Silva ^b   Hsiao, Sarah ^c   Lungu, Angela ^c   Evans, Paul ^d   Bressloff, Neil W ^e   Lawford, Patricia V ^d   Rodney Hose, D ^b   Gunn, Julian P ^e  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine INSIGNEO Institute for in silico Medicine Department of Mechanical Engineering The University of Sheffield   (United Kingdom)

^c SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST   (United Kingdom)

^d UNIVERSITY OF BERN   (Switzerland)

^e UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOMEDICINE; CARDIAC IMAGING; CARDIOVASCULAR MEDICINE; CARDIOVASCULAR SYSTEM; COMPUTATIONAL FLUID DYNAMICS; COMPUTER SIMULATION; DIAGNOSTIC IMAGING; EQUIPMENT DESIGN; HUMAN; IMAGE RECONSTRUCTION; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PERSONALIZED MEDICINE; PRIORITY JOURNAL; RISK ASSESSMENT; SHEAR STRESS; TREATMENT PLANNING; WORKFLOW; ANIMAL; BIOLOGICAL MODEL; CARDIOVASCULAR DISEASES; COMPUTER AIDED DESIGN; DEVICES; HEART SURGERY; HEMODYNAMICS; IMAGE PROCESSING; PATHOLOGY; PATHOPHYSIOLOGY; PREDICTIVE VALUE; PROCEDURES; PROSTHESIS DESIGN; PROSTHESIS IMPLANTATION;

ANIMALS; CARDIAC SURGICAL PROCEDURES; CARDIOVASCULAR DISEASES; CARDIOVASCULAR SYSTEM; COMPUTER SIMULATION; COMPUTER-AIDED DESIGN; DIAGNOSTIC IMAGING; HEMODYNAMICS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, CARDIOVASCULAR; PREDICTIVE VALUE OF TESTS; PROSTHESIS DESIGN; PROSTHESIS IMPLANTATION;

EID: 84957968584     PISSN: 13556037     EISSN: 1468201X     Source Type: Journal    
DOI: 10.1136/heartjnl-2015-308044     Document Type: Article

References (74)

1. Taylor CA, Figueroa CA. Patient-specific modeling of cardiovascular mechanics. Annu Rev Biomed Eng 2009;11:109-34
2. Smith N, de Vecchi A, McCormick M, et al. euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interface Focus 2011;1:349-64
3. Gasser TC, Nchimi A, Swedenborg J, et al. A novel strategy to translate the biomechanical rupture risk of abdominal aortic aneurysms to their equivalent diameter risk: method and retrospective validation. Eur J Vasc Endovasc Surg 2014;47:288-95
4. Radaelli AG, Augsburger L, Cebral JR, et al. Reproducibility of haemodynamical simulations in a subject-specific stented aneurysm model-A report on the Virtual Intracranial Stenting Challenge 2007. J Biomech 2008;41:2069-81
5. Morris PD, Ryan D, Morton AC, et al. Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) Study. JACC Cardiovasc Interv 2013;6:149-57
6. Noørgaard BL, Leipsic J, Gaur S, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 2014;63:1145-55
7. Tu S, Barbato E, Koøszegi Z, et al. Fractional flow reserve calculation from 3-dimensional quantitative coronary angiography and TIMI frame count: a fast computer model to quantify the functional significance of moderately obstructed coronary arteries. JACC Cardiovasc Interv 2014;7:768-77
8. Morris PD, van de Vosse FN, Lawford PV, et al. "Virtual" (computed) fractional flow reserve: current challenges and limitations. JACC Cardiovasc Interv 2015;8:1009-17
9. Sotiropoulos F, Borazjani I. A review of state-of-The-Art numerical methods for simulating flow through mechanical heart valves. Med Biol Eng Comput 2009;47:245-56
10. Simon HA, Ge L, Borazjani I, et al. Simulation of the three-dimensional hinge flow fields of a bileaflet mechanical heart valve under aortic conditions. Ann Biomed Eng 2010;38:841-53
11. Yoganathan AP, Chandran KB, Sotiropoulos F. Flow in prosthetic heart valves: state-of-The-Art and future directions. Ann Biomed Eng 2005;33:1689-94
12. Astorino M, Hamers J, Shadden SC, et al. A robust and efficient valve model based on resistive immersed surfaces. Int J Numer Method Biomed Eng 2012;28:937-59
13. De Hart J, Peters GW, Schreurs PJ, et al. A three-dimensional computational analysis of fluid-structure interaction in the aortic valve. J Biomech 2003;36:103-12
14. Erhart P, Hyhlik-Dürr A, Geisbüsch P, et al. Finite element analysis in asymptomatic, symptomatic, and ruptured abdominal aortic aneurysms: in search of new rupture risk predictors. Eur J Vasc Endovasc Surg 2015;49:239-45
15. Georgakarakos E, Ioannou CV, Papaharilaou Y, et al. Computational evaluation of aortic aneurysm rupture risk: what have we learned so far? J Endovasc Ther 2011;18:214-25
16. Molony DS, Kavanagh EG, Madhavan P, et al. A computational study of the magnitude and direction of migration forces in patient-specific abdominal aortic aneurysm stent-grafts. Eur J Vasc Endovasc Surg 2010;40:332-9
17. Karmonik C, Müller-Eschner M, Partovi S, et al. Computational fluid dynamics investigation of chronic aortic dissection hemodynamics versus normal aorta. Vasc Endovascular Surg 2013;47:625-31
18. Chen D, Müller-Eschner M, von Tengg-Kobligk H, et al. A patient-specific study of type-B aortic dissection: evaluation of true-false lumen blood exchange. Biomed Eng Online 2013;12:65
19. Cheng Z, Juli C, Wood NB, et al. Predicting flow in aortic dissection: comparison of computational model with PC-MRI velocity measurements. Med Eng Phys 2014;36:1176-84
20. Chien S. Mechanotransduction and endothelial cell homeostasis: the wisdom of the cell. Am J Physiol Heart Circ Physiol 2007;292:H1209-24
21. LaDisa JF, Jr., Olson LE, Molthen RC, et al. Alterations in wall shear stress predict sites of neointimal hyperplasia after stent implantation in rabbit iliac arteries. Am J Physiol Heart Circ Physiol 2005;288:H2465-75
22. Jin S, Yang Y, Oshinski J, et al. Flow patterns and wall shear stress distributions at atherosclerotic-prone sites in a human left coronary artery-An exploration using combined methods of CT and computational fluid dynamics. Conf Proc IEEE Eng Med Biol Soc 2004;5:3789-91
23. Lee J, Smith NP. The multi-scale modelling of coronary blood flow. Ann Biomed Eng 2012;40:2399-413
24. Jiménez JM, Davies PF. Hemodynamically driven stent strut design. Ann Biomed Eng 2009;37:1483-94
25. Seshadhri S, Janiga G, Beuing O, et al. Impact of stents and flow diverters on hemodynamics in idealized aneurysm models. J Biomech Eng 2011;133:071005
26. Morlacchi S, Migliavacca F. Modeling stented coronary arteries: where we are, where to go. Ann Biomed Eng 2013;41:1428-44
27. Wentzel JJ, Krams R, Schuurbiers JC, et al. Relationship between neointimal thickness and shear stress after Wallstent implantation in human coronary arteries. Circulation 2001;103:1740-5
28. Peach TW, Ngoepe M, Spranger K, et al. Personalizing flow-diverter intervention for cerebral aneurysms: from computational hemodynamics to biochemical modeling. Int J Numer Method Biomed Eng 2014;30:1387-407
29. Schneiders JJ, Marquering HA, van Ooij P, et al. Additional value of intra-Aneurysmal hemodynamics in discriminating ruptured versus unruptured intracranial aneurysms. AJNR Am J Neuroradiol 2015;10:1920-6
30. Qureshi MU, Vaughan GA, Sainsbury C, et al. Numerical simulation of blood flow and pressure drop in the pulmonary arterial and venous circulation. Biomech Model Mechanobiol 2014;13:1137-54
31. Lungu A, Wild JM, Capener D, et al. MRI model-based non-invasive differential diagnosis in pulmonary hypertension. J Biomech 2014;47:2941-7
32. Tang BT, Pickard SS, Chan FP, et al. Wall shear stress is decreased in the pulmonary arteries of patients with pulmonary arterial hypertension: an image-based, computational fluid dynamics study. Pulm Circ 2012;2:470-6
33. Kheyfets VO, Rios L, Smith T, et al. Patient-specific computational modeling of blood flow in the pulmonary arterial circulation. Comput Methods Programs Biomed 2015;120:88-101
34. Niederer SA, Plank G, Chinchapatnam P, et al. Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy. Cardiovasc Res 2011;89:336-43
35. Chan BT, Lim E, Chee KH, et al. Review on CFD simulation in heart with dilated cardiomyopathy and myocardial infarction. Comput Biol Med 2013;43:377-85
36. Aguado-Sierra J, Krishnamurthy A, Villongco C, et al. Patient-specific modeling of dyssynchronous heart failure: a case study. Prog Biophys Mol Biol 2011;107:147-55
37. Sermesant M, Chabiniok R, Chinchapatnam P, et al. Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation. Med Image Anal 2012;16:201-15
38. Brown AG. Patient-specific local and systemic haemodynamics in the presence of a left ventricular assist device. Department of cardiovascular science. Sheffield: University of Sheffield, 2012
39. Brown AG, Shi Y, Arndt A, et al. Importance of realistic LVAD profiles for assisted aortic simulations: evaluation of optimal outflow anastomosis locations. Comput Methods Biomech Biomed Engin 2012;15:669-80
40. Chiu WC, Girdhar G, Xenos M, et al. Thromboresistance comparison of the HeartMate II ventricular assist device with the device thrombogenicity emulationoptimized HeartAssist 5 VAD. J Biomech Eng 2014;136:021014
41. Farag MB, Karmonik C, Rengier F, et al. Review of recent results using computational fluid dynamics simulations in patients receiving mechanical assist devices for end-stage heart failure. Methodist Debakey Cardiovasc J 2014;10:185-9
42. Ladisa JF Jr, Taylor CA, Feinstein JA. Aortic coarctation: recent developments in experimental and computational methods to assess treatments for this simple condition. Prog Pediatr Cardiol 2010;30:45-9
43. Pennati G, Corsini C, Hsia TY, et al. Computational fluid dynamics models and congenital heart diseases. Front Pediatr 2013;1:4
44. Pittaccio S, Migliavacca F, Dubini G, et al. On the use of computational models for the quantitative assessment of surgery in congenital heart disease. Anadolu Kardiyol Derg 2005;5:202-9
45. van de Vosse FN, Stergiopulos N. Pulse wave propagation in the arterial tree. Annu Rev Fluid Mech 2011;43:467-99
46. 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;10:33
47. Kim HJ, Vignon-Clementel IE, Figueroa CA, et al. On coupling a lumped parameter heart model and a three-dimensional finite element aorta model. Ann Biomed Eng 2009;37:2153-69
48. Rodriguez L, Thomas JD, Monterroso V, et al. Validation of the proximal flow convergence method. Calculation of orifice area in patients with mitral stenosis. Circulation 1993;88:1157-65
49. Weese J, Groth A, Nickisch H, et al. Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations. Med Biol Eng Comput 2013;51:1209-19
50. Barber DC, Hose DR. Automatic segmentation of medical images using image registration: diagnostic and simulation applications. J Med Eng Technol 2005;29:53-63
51. Zhao F, Xie X. An overview of interactive medical image segmentation. Ann BMVA 2013;7:1-22
52. Kung E, Pennati G, Migliavacca F, et al. A simulation protocol for exercise physiology in Fontan patients using a closed loop lumped-parameter model. J Biomech Eng 2014;136:107001
53. Zeng D, Boutsianis E, Ammann M, et al. A study on the compliance of a right coronary artery and its impact on wall shear stress. J Biomech Eng 2008;130:041014
54. Brown AG, Shi Y, Marzo A, et al. Accuracy vs. computational time: translating aortic simulations to the clinic. J Biomech 2012;45:516-23
55. Bertoglio C, Barber D, Gaddum N, et al. Identification of artery wall stiffness: in vitro validation and in vivo results of a data assimilation procedure applied to a 3D fluid-structure interaction model. J Biomech 2014;47:1027-34
56. Reporting of Computational Modeling Studies in Medical Device Submissions. Rockeville, MD: Draft Guidance for Industry and Food and Drug Administration Staff, 2014
57. Bluestein D, Girdhar G, Einav S, et al. Device thrombogenicity emulation: a novel methodology for optimizing the thromboresistance of cardiovascular devices. J Biomech 2013;46:338-44
58. Pijls NH, Fearon WF, Tonino PA, et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention in patients with multivessel coronary artery disease: 2-year follow-up of the FAME (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) study. J Am Coll Cardiol 2010;56:177-84
59. Papafaklis MI, Muramatsu T, Ishibashi Y, et al. Fast virtual functional assessment of intermediate coronary lesions using routine angiographic data and blood flow simulation in humans: comparison with pressure wire-fractional flow reserve. EuroIntervention 2014;10:574-83
60. U.S. Food and Drug Administration. De novo classification request for FFRCT V. 1.4 (De Novo Summary (DEN130045)). 2014
61. Sonntag SJ, Li W, Becker M, et al. Combined computational and experimental approach to improve the assessment of mitral regurgitation by echocardiography. Ann Biomed Eng 2014;42:971-85
62. Swift AJ, Wild JM, Nagle SK, et al. Quantitative magnetic resonance imaging of pulmonary hypertension: a practical approach to the current state of the art. J Thorac Imaging 2014;29:68-79
63. Morlacchi S, Colleoni SG, Cárdenes R, et al. Patient-specific simulations of stenting procedures in coronary bifurcations: two clinical cases. Med Eng Phys 2013;35:1272-81
64. Villa-Uriol MC, Berti G, Hose DR, et al. NeurIST complex information processing toolchain for the integrated management of cerebral aneurysms. Interface Focus 2011;1:308-19
65. Chen D, Müller-Eschner M, Kotelis D, et al. A longitudinal study of type-B aortic dissection and endovascular repair scenarios: computational analyses. Med Eng Phys 2013;35:1321-30
66. Venkatasubramaniam AK, Fagan MJ, Mehta T, et al. A comparative study of aortic wall stress using finite element analysis for ruptured and non-ruptured abdominal aortic aneurysms. Eur J Vasc Endovasc Surg 2004;28:168-76
67. Martufi G, Christian Gasser T. Review: the role of biomechanical modeling in the rupture risk assessment for abdominal aortic aneurysms. J Biomech Eng 2013;135:021010
68. Hunter P, Chapman T, Coveney PV, et al. A vision and strategy for the virtual physiological human: 2012 update. Interface Focus 2013;3:20130004
69. http://www.avicenna-isct.org (accessed Jul 2015).
70. Donders WP, Huberts W, van de Vosse FN, et al. Personalization of models with many model parameters: an efficient sensitivity analysis approach. Int J Numer Method Biomed Eng 2015;31. Published Online First: 27th May 2015. doi:10.1002/cnm.2727. Epub 2015 Jun 15
71. Stewart S, Hariharan P, Paterson E, et al. Results of FDA's first interlaboratory computational study of a nozzle with a sudden contraction and conical diffuser. Cardiovasc Eng Technol 2013;4:374-91
72. vph-share.eu. http://vph-share.eu (accessed Jul 2015).
73. Cates CU, Gallagher AG. The future of simulation technologies for complex cardiovascular procedures. Eur Heart J 2012;33:2127-34
74. http://vph-portal.eu (accessed Jul 2015).