Image-based computational fluid dynamics for estimating pressure drop and fractional flow reserve across iliac artery stenosis: A comparison with in-vivo measurements.
CFD
FFR
fractional flow reserve
iliac artery stenosis
pressure drop
Journal
International journal for numerical methods in biomedical engineering
ISSN: 2040-7947
Titre abrégé: Int J Numer Method Biomed Eng
Pays: England
ID NLM: 101530293
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
revised:
07
12
2020
received:
24
02
2020
accepted:
06
01
2021
pubmed:
16
1
2021
medline:
5
4
2022
entrez:
15
1
2021
Statut:
ppublish
Résumé
Computational Fluid Dynamics (CFD) and time-resolved phase-contrast magnetic resonance imaging (PC-MRI) are potential non-invasive methods for the assessment of the severity of arterial stenoses. Fractional flow reserve (FFR) is the current "gold standard" for determining stenosis severity in the coronary arteries but is an invasive method requiring insertion of a pressure wire. CFD derived FFR (vFFR) is an alternative to traditional catheter derived FFR now available commercially for coronary artery assessment, however, it can potentially be applied to a wider range of vulnerable vessels such as the iliac arteries. In this study CFD simulations are used to assess the ability of vFFR in predicting the stenosis severity in a patient with a stenosis of 77% area reduction (>50% diameter reduction) in the right iliac artery. Variations of vFFR, overall pressure drop and flow split between the vessels were observed by using different boundary conditions. Correlations between boundary condition parameters and resulting flow variables are presented. The study concludes that vFFR has good potential to characterise iliac artery stenotic disease.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e3437Informations de copyright
© 2021 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.
Références
W.H.O. Cardiovascular diseases (CVDs). 2019; https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
Berry C, Corcoran D, Hennigan B, Watkins S, Layland J, Oldroyd KG. Fractional flow reserve-guided management in stable coronary disease and acute myocardial infarction: recent developments. Eur Heart J. 2015;36(45):3155-3164.
Pijls NH, van Son JA, Kirkeeide RL, de Bruyne B, Gould KL. Experimental basis of determining maximum coronary, myocardial, and collateral blood flow by pressure measurements for assessing functional stenosis severity before and after percutaneous transluminal coronary angioplasty. Circulation. 1993;87(4):1354-1367.
Lotfi A, Jeremias A, Fearon WF, et al. Expert consensus statement on the use of fractional flow reserve, intravascular ultrasound, and optical coherence tomography: a consensus statement of the Society of Cardiovascular Angiography and Interventions. Catheter Cardiovasc Interv. 2014;83(4):509-518.
van de Hoef TP, Meuwissen M, Escaned J, et al. Fractional flow reserve as a surrogate for inducible myocardial ischaemia. Nat Rev Cardiol. 2013;10(8):439-452.
Morris PD, van de Vosse F, Lawford PV, Hose DR, Gunn JP. “Virtual” (computed) fractional flow reserve: current challenges and limitations. J Am Coll Cardiol Intv. 2015;8(8):1009-1017.
Leiner T, Tordoir JHM, Kessels AGH, et al. Comparison of treatment plans for peripheral arterial disease made with multi-station contrast medium-enhanced magnetic resonance angiography and duplex ultrasound scanning. J Vasc Surg. 2003;37(6):1255-1262.
Ubbink DT, Fidler M, Legemate DA. Interobserver variability in aortoiliac and femoropopliteal duplex scanning. J Vasc Surg. 2001;33(3):540-545.
Visser K, Hunink MGM. Peripheral arterial disease: gadolinium-enhanced MR angiography versus color-guided duplex US-a meta-analysis. Radiology. 2000;216(1):67-77.
Visser K, Bosch JL, Leiner T, van Engelshoven JMA, Passchier J, Hunink MGM. Patients' preferences for MR angiography and duplex US in the work-up of peripheral arterial disease. Eur J Vasc Endovasc Surg. 2003;26(5):537-543.
Hioki H, Miyashita Y, Miura T, et al. Diagnostic value of peripheral fractional flow reserve in isolated iliac artery stenosis: a comparison with the post-exercise ankle-brachial index. J Endovasc Ther. 2014;21(5):625-632.
Kobayashi N, Hirano K, Nakano M, et al. Measuring procedure and maximal hyperemia in the assessment of fractional flow reserve for superficial femoral artery disease. J Atheroscler Thromb. 2016;23(1):56-66.
Min JK, Taylor CA, Achenbach S, et al. Noninvasive fractional flow reserve derived from coronary CT angiography: clinical data and scientific principles. JACC Cardiovasc Imaging. 2015;8(10):1209-1222.
Zarins CK, Taylor CA, Min JK. Computed fractional flow reserve (FFTCT) derived from coronary CT angiography. J Cardiovasc Transl Res. 2013;6(5):708-714.
Baumann S, Wang R, Schoepf UJ, et al. Coronary CT angiography-derived fractional flow reserve correlated with invasive fractional flow reserve measurements - initial experience with a novel physician-driven algorithm. Eur Radiol. 2015;25(4):1201-1207.
Tröbs M, Achenbach S, Röther J, et al. Comparison of fractional flow reserve based on computational fluid dynamics modeling using coronary angiographic vessel morphology versus invasively measured fractional flow reserve. Am J Cardiol. 2016;117(1):29-35.
Takagi H, Ishikawa Y, Orii M, et al. Optimized interpretation of fractional flow reserve derived from computed tomography: comparison of three interpretation methods. J Cardiovasc Comput Tomogr. 2018;13(2):134-141. https://doi.org/10.1016/j.jcct.2018.10.027.
Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013;61(22):2233-2241.
Sankaran S, Kim HJ, Choi G, Taylor CA. Uncertainty quantification in coronary blood flow simulations: impact of geometry, boundary conditions and blood viscosity. J Biomech. 2016;49(12):2540-2547.
Agujetas R, González-Fernández MR, Nogales-Asensio JM, Montanero JM. Numerical analysis of the pressure drop across highly-eccentric coronary stenoses: application to the calculation of the fractional flow reserve. Biomed Eng Online. 2018;17(1):67.
Heinen SGH, van den Heuvel DAF, Huberts W, et al. In vivo validation of patient-specific pressure gradient calculations for iliac artery stenosis severity assessment. J Am Heart Assoc. 2017;6(12):e007328.
Ward EP, Shiavazzi D, Sood D, et al. Computed tomography fractional flow reserve can identify culprit lesions in aortoiliac occlusive disease using minimally invasive techniques. Ann Vasc Surg. 2017;38:151-157.
Vignon-Clementel IE, Alberto Figueroa C, Jansen KE, Taylor CA. Outflow boundary conditions for three-dimensional finite element modeling of blood flow and pressure in arteries. Comput Methods Appl Mech Eng. 2006;195(29-32):3776-3796.
Alastruey J, Xiao N, Fok H, Schaeffter T, Figueroa CA. On the impact of modelling assumptions in multi-scale, subject-specific models of aortic haemodynamics. J R Soc Interface. 2016;13(119):20160073.
Taylor CA, Figueroa CA. Patient-specific modeling of cardiovascular mechanics. Annu Rev Biomed Eng. 2009;11:109-134.
Malek AM, Alper SL, Izumo S. Hemodynamic shear stress and its role in atherosclerosis. JAMA. 1999;282(21):2035-2042.
Roache PJ. Quantification of uncertainty in computational fluid dynamics. Annu Rev Fluid Mech. 1997;29(1):123-160.
Ku DN. Blood flow in arteries. Annu Rev Fluid Mech. 1997;29(1):399-434.
Lam SK, Fung GSK, Cheng SWK, Chow KW. A computational study on the biomechanical factors related to stent-graft models in the thoracic aorta. Med Biol Eng Comput. 2008;46(11):1129-1138.
Linge F, Hye MA, Paul MC. Pulsatile spiral blood flow through arterial stenosis. Comput Methods Biomech Biomed Eng. 2014;17(15):1727-1737.
Stewart SFC, Paterson EG, Burgreen GW, et al. Assessment of CFD performance in simulations of an idealized medical device: results of FDA's first computational interlaboratory study. Cardiovasc Eng Technol. 2012;3(2):139-160.
Menter FR. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J. 1994;32(8):1598-1605.
Wilcox, D.C., Turbulence modeling for CFD.
Ghalichi F, Ghalichi F, Deng X, et al. Low Reynolds number turbulence modeling of blood flow in arterial stenoses. Biorheology. 1998;35(4):281-294.
Paul MC, Larman A. Investigation of spiral blood flow in a model of arterial stenosis. Med Eng Phys. 2009;31(9):1195-1203.
Lee TS, Liao W, Low HT. Numerical study of physiological turbulent flows through series arterial stenoses. Int J Numer Methods Fluids. 2004;46(3):315-344.
Versteeg HK, Malalasekera W. An Introduction to Computational Fluid Dynamics: the Finite Volume Method. Harlow, UK: Longman Scientific & Technical; 1995.
Westerhof N, Lankhaar J-W, Westerhof BE. The arterial Windkessel. Med Biol Eng Comput. 2009;47(2):131-141.
Katritsis D, Kaiktsis L, Chaniotis A, Pantos J, Efstathopoulos EP, Marmarelis V. Wall shear stress: theoretical considerations and methods of measurement. Prog Cardiovasc Dis. 2007;49(5):307-329.
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(2):149-157.
Tu S, Barbato E, Köszegi 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. Clin Res. 2014;7(7):768-777.
Morris PD, Silva Soto DA, Feher JFA, et al. Fast virtual fractional flow reserve based upon steady-state computational fluid dynamics analysis: results from the VIRTU-fast study. JACC Basic Transl Sci. 2017;2(4):434-446.