Research progress on cardiovascular hemodynamic assessment based on computational fluid dynamics_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

HU Shengyi ^1,2 , SUN Jing ¹ , HUANG Xiaohong ^1,2 ,  ZHENG Zhe ¹

1. Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, P. R. China;
2. 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China;

Corresponding author：

ZHENG Zhe, Email: zhengzhe@fuwai.com

Keywords：

Computational fluid dynamics; hemodynamics; cardiovascular imaging techniques; parameters; machine learing; artificial intelligence; review

DOI：

10.7507/1007-4848.202304068

Video：

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Hemodynamics plays a vital role in the development and progression of cardiovascular diseases, and is closely associated with changes in morphology and function. Reliable detection of hemodynamic changes is essential to improve treatment strategies and enhance patient prognosis. The combination of computational fluid dynamics with cardiovascular imaging technology has extended the accessibility of hemodynamics. This review provides a comprehensive summary of recent developments in the application of computational fluid dynamics for cardiovascular hemodynamic assessment and a succinct discussion for potential future development.

Citation： HU Shengyi, SUN Jing, HUANG Xiaohong, ZHENG Zhe. Research progress on cardiovascular hemodynamic assessment based on computational fluid dynamics. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(2): 319-324. doi: 10.7507/1007-4848.202304068 Copy

1.	Boselli F, Freund JB, Vermot J. Blood flow mechanics in cardiovascular development. Cell Mol Life Sci, 2015, 72(13): 2545-2559.
2.	Miller WL, Sorimachi H, Grill DE, et al. Contributions of cardiac dysfunction and volume status to central haemodynamics in chronic heart failure. Eur J Heart Fail, 2021, 23(7): 1097-1105.
3.	Obokata M, Reddy YNV, Melenovsky V, et al. Deterioration in right ventricular structure and function over time in patients with heart failure and preserved ejection fraction. Eur Heart J, 2019, 40(8): 689-697.
4.	Schnell S, Smith DA, Barker AJ, et al. Altered aortic shape in bicuspid aortic valve relatives influences blood flow patterns. Eur Heart J Cardiovasc Imaging, 2016, 17(11): 1239-1247.
5.	Dux-Santoy L, Guala A, Teixidó-Turà G, et al. Increased rotational flow in the proximal aortic arch is associated with its dilation in bicuspid aortic valve disease. Eur Heart J Cardiovasc Imaging, 2019, 20(12): 1407-1417.
6.	Secomb TW. Hemodynamics. Compr Physiol, 2016, 6(2): 975-1003.
7.	Lenz A, Petersen J, Riedel C, et al. 4D flow cardiovascular magnetic resonance for monitoring of aortic valve repair in bicuspid aortic valve disease. J Cardiovasc Magn Reson, 2020, 22(1): 29.
8.	Engelhard S, van Helvert M, Voorneveld J, et al. US velocimetry in participants with aortoiliac occlusive disease. Radiology, 2021, 301(2): 332-338.
9.	Lantz J, Gupta V, Henriksson L, et al. Intracardiac flow at 4D CT: Comparison with 4D flow MRI. Radiology, 2018, 289(1): 51-58.
10.	Toba T, Otake H, Choi G, et al. Wall shear stress and plaque vulnerability: Computational fluid dynamics analysis derived from cCTA and OCT. JACC Cardiovasc Imaging, 2021, 14(1): 315-317.
11.	Anderson JD, Wendt J. Computational Fluid Dynamics. New York: McGraw-Hill, 1995. 5-6.
12.	Morris PD, Narracott A, von Tengg-Kobligk H, et al. Computational fluid dynamics modelling in cardiovascular medicine. Heart, 2016, 102(1): 18-28.
13.	van Ooij P, Farag ES, Blanken CPS, et al. Fully quantitative mapping of abnormal aortic velocity and wall shear stress direction in patients with bicuspid aortic valves and repaired coarctation using 4D flow cardiovascular magnetic resonance. J Cardiovasc Magn Reson, 2021, 23(1): 9.
14.	Burris NS, Sigovan M, Knauer HA, et al. Systolic flow displacement correlates with future ascending aortic growth in patients with bicuspid aortic valves undergoing magnetic resonance surveillance. Invest Radiol, 2014, 49(10): 635-639.
15.	Meyrignac O, Bal L, Zadro C, et al. Combining volumetric and wall shear stress analysis from CT to assess risk of abdominal aortic aneurysm progression. Radiology, 2020, 295(3): 722-729.
16.	Hayashi H, Itatani K, Akiyama K, et al. Influence of aneurysmal aortic root geometry on mechanical stress to the aortic valve leaflet. Eur Heart J Cardiovasc Imaging, 2021, 22(9): 986-994.
17.	Costopoulos C, Timmins LH, Huang Y, et al. Impact of combined plaque structural stress and wall shear stress on coronary plaque progression, regression, and changes in composition. Eur Heart J, 2019, 40(18): 1411-1422.
18.	Lee JM, Choi G, Koo BK, et al. Identification of high-risk plaques destined to cause acute coronary syndrome using coronary computed tomographic angiography and computational fluid dynamics. JACC Cardiovasc Imaging, 2019, 12(6): 1032-1043.
19.	Elbaz MS, van der Geest RJ, Calkoen EE, et al. Assessment of viscous energy loss and the association with three-dimensional vortex ring formation in left ventricular inflow: In vivo evaluation using four-dimensional flow MRI. Magn Reson Med, 2017, 77(2): 794-805.
20.	Stugaard M, Koriyama H, Katsuki K, et al. Energy loss in the left ventricle obtained by vector flow mapping as a new quantitative measure of severity of aortic regurgitation: A combined experimental and clinical study. Eur Heart J Cardiovasc Imaging, 2015, 16(7): 723-730.
21.	Bahlmann E, Gerdts E, Cramariuc D, et al. Prognostic value of energy loss index in asymptomatic aortic stenosis. Circulation, 2013, 127(10): 1149-1156.
22.	Kamphuis VP, Elbaz MSM, van den Boogaard PJ, et al. Disproportionate intraventricular viscous energy loss in Fontan patients: Analysis by 4D flow MRI. Eur Heart J Cardiovasc Imaging, 2019, 20(3): 323-333.
23.	Hayashi H, Akiyama K, Itatani K, et al. A novel in vivo assessment of fluid dynamics on aortic valve leaflet using epi-aortic echocardiogram. Echocardiography, 2020, 37(2): 323-330.
24.	Li L, Jani V, Craft M, et al. Ventricular flow profile in young patients with single left ventricle Fontan using echocardiographic contrast particle imaging velocimetry. J Am Soc Echocardiogr, 2023, 36(2): 250-252.
25.	Tonino PA, Fearon WF, De Bruyne B, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol, 2010, 55(25): 2816-2821.
26.	Samady H, Molony DS, Coskun AU, et al. Risk stratification of coronary plaques using physiologic characteristics by CCTA: Focus on shear stress. J Cardiovasc Comput Tomogr, 2020, 14(5): 386-393.
27.	Kobayashi K, Wakasa S, Sato K, et al. Quantitative analysis of regional endocardial geometry dynamics from 4D cardiac CT images: Endocardial tracking based on the iterative closest point with an integrated scale estimation. Phys Med Biol, 2019, 64(5): 055009.
28.	Gao F, Chen B, Zhou T, et al. Research on the effect of visceral artery aneurysm's cardiac morphological variation on hemodynamic situation based on time-resolved CT-scan and computational fluid dynamics. Comput Methods Programs Biomed, 2022, 221: 106928.
29.	Rutkowski DR, Roldán-Alzate A, Johnson KM. Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Sci Rep, 2021, 11(1): 10240.
30.	Nightingale M, Scott MB, Sigaeva T, et al. Magnetic resonance imaging-based hemodynamic wall shear stress alters aortic wall tissue biomechanics in bicuspid aortic valve patients. J Thorac Cardiovasc Surg, 2023, S0022-5223(23)00019-3.
31.	Jamaleddin Mousavi S, Jayendiran R, Farzaneh S, et al. Coupling hemodynamics with mechanobiology in patient-specific computational models of ascending thoracic aortic aneurysms. Comput Methods Programs Biomed, 2021, 205: 106107.
32.	Szajer J, Ho-Shon K. A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations—A review. Magn Reson Imaging, 2018, 48: 62-69.
33.	Montalt-Tordera J, Pajaziti E, Jones R, et al. Automatic segmentation of the great arteries for computational hemodynamic assessment. J Cardiovasc Magn Reson, 2022, 24(1): 57.
34.	Zuo X, Xu Z, Jia H, et al. Co-simulation of hypertensive left ventricle based on computational fluid dynamics and a closed-loop network model. Comput Methods Programs Biomed, 2022, 216: 106649.
35.	Maulik R, San O, Rasheed A, et al. Subgrid modelling for two-dimensional turbulence using neural networks. J Fluid Mech, 2019, 858: 122-144.
36.	Kochkov D, Smith JA, Alieva A, et al. Machine learning-accelerated computational fluid dynamics. Proc Natl Acad Sci U S A, 2021, 118(21): e2101784118.
37.	Garrido-Oliver J, Aviles J, Córdova MM, et al. Machine learning for the automatic assessment of aortic rotational flow and wall shear stress from 4D flow cardiac magnetic resonance imaging. Eur Radiol, 2022, 32(10): 7117-7127.
38.	Su B, Zhang JM, Zou H, et al. Generating wall shear stress for coronary artery in real-time using neural networks: Feasibility and initial results based on idealized models. Comput Biol Med, 2020, 126: 104038.
39.	Gharleghi R, Sowmya A, Beier S. Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks. Comput Methods Programs Biomed, 2022, 225: 107013.
40.	Gao Z, Wang X, Sun S, et al. Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging. Neural Netw, 2020, 123: 82-93.
41.	Curzen N, Nicholas Z, Stuart B, et al. Fractional flow reserve derived from computed tomography coronary angiography in the assessment and management of stable chest pain: The FORECAST randomized trial. Eur Heart J, 2021, 42(37): 3844-3852.
42.	Guala A, Dux-Santoy L, Teixido-Tura G, et al. Wall shear stress predicts aortic dilation in patients with bicuspid aortic valve. JACC Cardiovasc Imaging, 2022, 15(1): 46-56.
43.	Michelena HI. Bicuspid aortopathy: Toward individualized risk assessment with imaging biomarkers. JACC Cardiovasc Imaging, 2022, 15(1): 57-59.
44.	Zhang Y, Xiong TY, Li YM, et al. Variation of computed tomographic angiography-based fractional flow reserve after transcatheter aortic valve implantation. Eur Radiol, 2021, 31(8): 6220-6229.
45.	Callaghan FM, Burkhardt B, Valsangiacomo Buechel ER, et al. Assessment of ventricular flow dynamics by 4D-flow MRI in patients following surgical repair of d-transposition of the great arteries. Eur Radiol, 2021, 31(10): 7231-7241.
46.	Alharbi Y, Otton J, Muller DWM, et al. Predicting the outcome of transcatheter mitral valve implantation using image-based computational models. J Cardiovasc Comput Tomogr, 2020, 14(4): 335-342.
47.	Cho JS, Shrestha S, Kagiyama N, et al. A Network-based "phenomics" approach for discovering patient subtypes from high-throughput cardiac imaging data. JACC Cardiovasc Imaging, 2020, 13(8): 1655-1670.
48.	Kobsa S, Akiyama K, Nemeth SK, et al. Correlation between aortic valve protein levels and vector flow mapping of wall shear stress and oscillatory shear index in patients supported with continuous-flow left ventricular assist devices. J Heart Lung Transplant, 2023, 42(1): 64-75.

1. Boselli F, Freund JB, Vermot J. Blood flow mechanics in cardiovascular development. Cell Mol Life Sci, 2015, 72(13): 2545-2559.
2. Miller WL, Sorimachi H, Grill DE, et al. Contributions of cardiac dysfunction and volume status to central haemodynamics in chronic heart failure. Eur J Heart Fail, 2021, 23(7): 1097-1105.
3. Obokata M, Reddy YNV, Melenovsky V, et al. Deterioration in right ventricular structure and function over time in patients with heart failure and preserved ejection fraction. Eur Heart J, 2019, 40(8): 689-697.
4. Schnell S, Smith DA, Barker AJ, et al. Altered aortic shape in bicuspid aortic valve relatives influences blood flow patterns. Eur Heart J Cardiovasc Imaging, 2016, 17(11): 1239-1247.
5. Dux-Santoy L, Guala A, Teixidó-Turà G, et al. Increased rotational flow in the proximal aortic arch is associated with its dilation in bicuspid aortic valve disease. Eur Heart J Cardiovasc Imaging, 2019, 20(12): 1407-1417.
6. Secomb TW. Hemodynamics. Compr Physiol, 2016, 6(2): 975-1003.
7. Lenz A, Petersen J, Riedel C, et al. 4D flow cardiovascular magnetic resonance for monitoring of aortic valve repair in bicuspid aortic valve disease. J Cardiovasc Magn Reson, 2020, 22(1): 29.
8. Engelhard S, van Helvert M, Voorneveld J, et al. US velocimetry in participants with aortoiliac occlusive disease. Radiology, 2021, 301(2): 332-338.
9. Lantz J, Gupta V, Henriksson L, et al. Intracardiac flow at 4D CT: Comparison with 4D flow MRI. Radiology, 2018, 289(1): 51-58.
10. Toba T, Otake H, Choi G, et al. Wall shear stress and plaque vulnerability: Computational fluid dynamics analysis derived from cCTA and OCT. JACC Cardiovasc Imaging, 2021, 14(1): 315-317.
11. Anderson JD, Wendt J. Computational Fluid Dynamics. New York: McGraw-Hill, 1995. 5-6.
12. Morris PD, Narracott A, von Tengg-Kobligk H, et al. Computational fluid dynamics modelling in cardiovascular medicine. Heart, 2016, 102(1): 18-28.
13. van Ooij P, Farag ES, Blanken CPS, et al. Fully quantitative mapping of abnormal aortic velocity and wall shear stress direction in patients with bicuspid aortic valves and repaired coarctation using 4D flow cardiovascular magnetic resonance. J Cardiovasc Magn Reson, 2021, 23(1): 9.
14. Burris NS, Sigovan M, Knauer HA, et al. Systolic flow displacement correlates with future ascending aortic growth in patients with bicuspid aortic valves undergoing magnetic resonance surveillance. Invest Radiol, 2014, 49(10): 635-639.
15. Meyrignac O, Bal L, Zadro C, et al. Combining volumetric and wall shear stress analysis from CT to assess risk of abdominal aortic aneurysm progression. Radiology, 2020, 295(3): 722-729.
16. Hayashi H, Itatani K, Akiyama K, et al. Influence of aneurysmal aortic root geometry on mechanical stress to the aortic valve leaflet. Eur Heart J Cardiovasc Imaging, 2021, 22(9): 986-994.
17. Costopoulos C, Timmins LH, Huang Y, et al. Impact of combined plaque structural stress and wall shear stress on coronary plaque progression, regression, and changes in composition. Eur Heart J, 2019, 40(18): 1411-1422.
18. Lee JM, Choi G, Koo BK, et al. Identification of high-risk plaques destined to cause acute coronary syndrome using coronary computed tomographic angiography and computational fluid dynamics. JACC Cardiovasc Imaging, 2019, 12(6): 1032-1043.
19. Elbaz MS, van der Geest RJ, Calkoen EE, et al. Assessment of viscous energy loss and the association with three-dimensional vortex ring formation in left ventricular inflow: In vivo evaluation using four-dimensional flow MRI. Magn Reson Med, 2017, 77(2): 794-805.
20. Stugaard M, Koriyama H, Katsuki K, et al. Energy loss in the left ventricle obtained by vector flow mapping as a new quantitative measure of severity of aortic regurgitation: A combined experimental and clinical study. Eur Heart J Cardiovasc Imaging, 2015, 16(7): 723-730.
21. Bahlmann E, Gerdts E, Cramariuc D, et al. Prognostic value of energy loss index in asymptomatic aortic stenosis. Circulation, 2013, 127(10): 1149-1156.
22. Kamphuis VP, Elbaz MSM, van den Boogaard PJ, et al. Disproportionate intraventricular viscous energy loss in Fontan patients: Analysis by 4D flow MRI. Eur Heart J Cardiovasc Imaging, 2019, 20(3): 323-333.
23. Hayashi H, Akiyama K, Itatani K, et al. A novel in vivo assessment of fluid dynamics on aortic valve leaflet using epi-aortic echocardiogram. Echocardiography, 2020, 37(2): 323-330.
24. Li L, Jani V, Craft M, et al. Ventricular flow profile in young patients with single left ventricle Fontan using echocardiographic contrast particle imaging velocimetry. J Am Soc Echocardiogr, 2023, 36(2): 250-252.
25. Tonino PA, Fearon WF, De Bruyne B, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol, 2010, 55(25): 2816-2821.
26. Samady H, Molony DS, Coskun AU, et al. Risk stratification of coronary plaques using physiologic characteristics by CCTA: Focus on shear stress. J Cardiovasc Comput Tomogr, 2020, 14(5): 386-393.
27. Kobayashi K, Wakasa S, Sato K, et al. Quantitative analysis of regional endocardial geometry dynamics from 4D cardiac CT images: Endocardial tracking based on the iterative closest point with an integrated scale estimation. Phys Med Biol, 2019, 64(5): 055009.
28. Gao F, Chen B, Zhou T, et al. Research on the effect of visceral artery aneurysm's cardiac morphological variation on hemodynamic situation based on time-resolved CT-scan and computational fluid dynamics. Comput Methods Programs Biomed, 2022, 221: 106928.
29. Rutkowski DR, Roldán-Alzate A, Johnson KM. Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Sci Rep, 2021, 11(1): 10240.
30. Nightingale M, Scott MB, Sigaeva T, et al. Magnetic resonance imaging-based hemodynamic wall shear stress alters aortic wall tissue biomechanics in bicuspid aortic valve patients. J Thorac Cardiovasc Surg, 2023, S0022-5223(23)00019-3.
31. Jamaleddin Mousavi S, Jayendiran R, Farzaneh S, et al. Coupling hemodynamics with mechanobiology in patient-specific computational models of ascending thoracic aortic aneurysms. Comput Methods Programs Biomed, 2021, 205: 106107.
32. Szajer J, Ho-Shon K. A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations—A review. Magn Reson Imaging, 2018, 48: 62-69.
33. Montalt-Tordera J, Pajaziti E, Jones R, et al. Automatic segmentation of the great arteries for computational hemodynamic assessment. J Cardiovasc Magn Reson, 2022, 24(1): 57.
34. Zuo X, Xu Z, Jia H, et al. Co-simulation of hypertensive left ventricle based on computational fluid dynamics and a closed-loop network model. Comput Methods Programs Biomed, 2022, 216: 106649.
35. Maulik R, San O, Rasheed A, et al. Subgrid modelling for two-dimensional turbulence using neural networks. J Fluid Mech, 2019, 858: 122-144.
36. Kochkov D, Smith JA, Alieva A, et al. Machine learning-accelerated computational fluid dynamics. Proc Natl Acad Sci U S A, 2021, 118(21): e2101784118.
37. Garrido-Oliver J, Aviles J, Córdova MM, et al. Machine learning for the automatic assessment of aortic rotational flow and wall shear stress from 4D flow cardiac magnetic resonance imaging. Eur Radiol, 2022, 32(10): 7117-7127.
38. Su B, Zhang JM, Zou H, et al. Generating wall shear stress for coronary artery in real-time using neural networks: Feasibility and initial results based on idealized models. Comput Biol Med, 2020, 126: 104038.
39. Gharleghi R, Sowmya A, Beier S. Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks. Comput Methods Programs Biomed, 2022, 225: 107013.
40. Gao Z, Wang X, Sun S, et al. Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging. Neural Netw, 2020, 123: 82-93.
41. Curzen N, Nicholas Z, Stuart B, et al. Fractional flow reserve derived from computed tomography coronary angiography in the assessment and management of stable chest pain: The FORECAST randomized trial. Eur Heart J, 2021, 42(37): 3844-3852.
42. Guala A, Dux-Santoy L, Teixido-Tura G, et al. Wall shear stress predicts aortic dilation in patients with bicuspid aortic valve. JACC Cardiovasc Imaging, 2022, 15(1): 46-56.
43. Michelena HI. Bicuspid aortopathy: Toward individualized risk assessment with imaging biomarkers. JACC Cardiovasc Imaging, 2022, 15(1): 57-59.
44. Zhang Y, Xiong TY, Li YM, et al. Variation of computed tomographic angiography-based fractional flow reserve after transcatheter aortic valve implantation. Eur Radiol, 2021, 31(8): 6220-6229.
45. Callaghan FM, Burkhardt B, Valsangiacomo Buechel ER, et al. Assessment of ventricular flow dynamics by 4D-flow MRI in patients following surgical repair of d-transposition of the great arteries. Eur Radiol, 2021, 31(10): 7231-7241.
46. Alharbi Y, Otton J, Muller DWM, et al. Predicting the outcome of transcatheter mitral valve implantation using image-based computational models. J Cardiovasc Comput Tomogr, 2020, 14(4): 335-342.
47. Cho JS, Shrestha S, Kagiyama N, et al. A Network-based "phenomics" approach for discovering patient subtypes from high-throughput cardiac imaging data. JACC Cardiovasc Imaging, 2020, 13(8): 1655-1670.
48. Kobsa S, Akiyama K, Nemeth SK, et al. Correlation between aortic valve protein levels and vector flow mapping of wall shear stress and oscillatory shear index in patients supported with continuous-flow left ventricular assist devices. J Heart Lung Transplant, 2023, 42(1): 64-75.

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