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. 2023 Apr 21;4(4):283-290.
doi: 10.1093/ehjdh/ztad028. eCollection 2023 Aug.

Rapid virtual fractional flow reserve using 3D computational fluid dynamics

Thomas Newman  1   2 , Raunak Borker  3 , Louise Aubiniere-Robb  1 , Justin Hendrickson  3 , Dipankar Choudhury  3 , Ian Halliday  1   4 , John Fenner  1   4 , Andrew Narracott  1   4 , D Rodney Hose  1   4 , Rebecca Gosling  1   2   4 , Julian P Gunn  1   2   4 , Paul D Morris  1   2   4
Affiliations

Rapid virtual fractional flow reserve using 3D computational fluid dynamics

Thomas Newman et al. Eur Heart J Digit Health. .

Abstract

Aims: Over the last ten years, virtual Fractional Flow Reserve (vFFR) has improved the utility of Fractional Flow Reserve (FFR), a globally recommended assessment to guide coronary interventions. Although the speed of vFFR computation has accelerated, techniques utilising full 3D computational fluid dynamics (CFD) solutions rather than simplified analytical solutions still require significant time to compute.

Methods and results: This study investigated the speed, accuracy and cost of a novel 3D-CFD software method based upon a graphic processing unit (GPU) computation, compared with the existing fastest central processing unit (CPU)-based 3D-CFD technique, on 40 angiographic cases. The novel GPU simulation was significantly faster than the CPU method (median 31.7 s (Interquartile Range (IQR) 24.0-44.4s) vs. 607.5 s (490-964 s), P < 0.0001). The novel GPU technique was 99.6% (IQR 99.3-99.9) accurate relative to the CPU method. The initial cost of the GPU hardware was greater than the CPU (£4080 vs. £2876), but the median energy consumption per case was significantly less using the GPU method (8.44 (6.80-13.39) Wh vs. 2.60 (2.16-3.12) Wh, P < 0.0001).

Conclusion: This study demonstrates that vFFR can be computed using 3D-CFD with up to 28-fold acceleration than previous techniques with no clinically significant sacrifice in accuracy.

Keywords: Computational fluid dynamics; Computer modelling; Fractional flow reserve.

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Conflict of interest statement

Conflict of interest: R.B., J.H., and D.C. are employees of Ansys Inc. This is independent research funded by The Wellcome Trust and carried out at the National Institute for Health and Care Research (NIHR) Sheffield Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily of the Wellcome Trust, the NIHR or the Department of Health and Social Care.

Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
Simulation duration of different simulation methods. Dots represent each case simulated, horizontal bars the median value of that method, and vertical bars the interquartile range. Note logarithmic y axis.
Figure 2
Figure 2
The impact of varying fidelity on simulation accuracy and simulation duration. Plots of median +/− IQR.
Figure 3
Figure 3
Bland–Altman plots of mean virtual fractional flow reserve (x axis) vs. difference in virtual fractional flow reserve between central processing unit and graphics processing unit methods (y axis) at 10% fidelity (left) and 100% fidelity (right). Difference was calculated as central processing unit virtual fractional flow reserve minus graphics processing unit virtual fractional flow reserve at a given fidelity. vFFR, virtual fractional flow reserve; CPU, central processing unit; GPU, graphics processing unit.
Figure 4
Figure 4
Plot of accuracy of each simulation relative to the central processing unit virtual fractional flow reserve for each graphics processing unit fidelity. Lines represent the simple linear regression model for each graphics processing unit fidelity. Symbols represent individual simulation values for 10, 50 and 100% graphics processing unit fidelity. CPU, central processing unit; vFFR, virtual fractional flow reserve; GPU, graphics processing unit.
Figure 5
Figure 5
Plot of median +/− IQR power consumed per simulation for different computation methods.
See this image and copyright information in PMC

References

    1. Neumann FJ, Sousa-Uva M, Ahlsson A, Alfonso F, Banning AP, Benedetto U, et al. . 2018 ESC/EACTS guidelines on myocardial revascularization. Eur Heart J 2019;40:87–165. - PubMed
    1. Lawton JS, Tamis-Holland JE, Bangalore S, Bates ER, Beckie TM, Bischoff JM, et al. . 2021 ACC/AHA/SCAI guideline for coronary artery revascularization: a report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines. J Am Coll Cardiol 2022;79:e21–e129. - PubMed
    1. Demir OM, Schrieken C, Curio J, Rahman H. Behavioural determinants impacting the adoption rate of coronary physiology. Int J Cardiol [Internet] 2021;330:12–14. - PubMed
    1. Parikh R V, Liu G, Plomondon ME, Sehested TSG, Hlatky MA, Waldo SW, et al. . Utilization and outcomes of measuring fractional flow reserve in patients with stable ischemic heart disease. J Am Coll Cardiol 2020;75:409–419. - PubMed
    1. Morris PD, Ryan D, Morton AC, Lycett R, Lawford P V, Hose DR, 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 [Internet] 2013;6:149–157. - PubMed

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