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A phantom and in vivo simulation of coronary flow to calculate fractional flow reserve using a mesh-free model

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Abstract

Moving particle semi-implicit (MPS) method is a mesh-free method to perform computational fluid dynamics (CFD). The purpose of this study was to calculate the simulated fractional flow reserve (sFFR) using a coronary stenosis model, and to validate the MPS-derived sFFR against invasive FFR using clinical coronary CT data. Coronary flow simulation included 21 stenosis models with stenosis ranging 30–70%. Patient coronary analysis was performed in 76 consecutive patients (100 vessels) who underwent coronary CT angiography and subsequent invasive FFR between November 2016 and March 2020. Accuracy of sFFR and CT angiography for diagnosis of invasive FFR ≤ 0.80 was compared. Quantitative morphological stenosis data of CT angiography were also obtained. Area stenosis showed a good correlation to sFFR (R2 = 0.996, p < 0.001) in coronary stenosis models. In the patient study, the mean FFR value was 0.82 ± 0.10, and 37 out of 100 vessels showed FFR ≤ 0.80. FFR and sFFR values showed a good correlation (R2 = 0.59, p < 0.001) with a slight underestimation of sFFR as compared with FFR (mean difference − 0.015 ± 0.096, p = 0.12). The sensitivity, specificity, positive predictive value, and negative predictive value of sFFR to predict FFR ≤ 0.80 was 86%, 89%, 82%, 92%, respectively. The accuracy to predict FFR ≤ 0.80 using sFFR was greater than using diameter stenosis and minimum lumen area (88% vs. 74%, p = 0.008). CFD using the MPS method showed feasible results validated against invasive FFR. The accuracy to predict significant stenosis was higher than morphological stenosis.

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Data availability

Data available on request due to privacy/ethical restrictions.

Code availability

Coding for the MPS method is available at GutHub (https://github.com/OpenMps/openmps).

Abbreviations

AUC:

Area under the curve

bpm:

Beats per minute

CI:

Confidence interval

CTA:

Computed tomography angiography

CFD:

Computational fluid dynamics

DS:

Diameter stenosis

LL:

Lesion length

MLA:

Minimum lumen area

MPS:

Moving particle semi-implicit

OR:

Odds ratio

ROC:

Receiver-operating characteristics

sFFR:

Simulated fractional flow reserve

References

  1. De Bruyne B, Pijls NHJ, Kalesan B et al (2012) Fractional flow reserve–guided PCI versus medical therapy in stable coronary disease. N Engl J Med 367:991–1001. https://doi.org/10.1056/NEJMoa1205361

    Article  CAS  PubMed  Google Scholar 

  2. Kitabata H, Leipsic J, Patel MR et al (2018) Incidence and predictors of lesion-specific ischemia by FFR CT: learnings from the international ADVANCE registry. J Cardiovasc Comput Tomogr 12:95–100. https://doi.org/10.1016/j.jcct.2018.01.008

    Article  PubMed  Google Scholar 

  3. Ko BS, Cameron JD, Munnur RK et al (2017) Noninvasive CT-derived FFR based on structural and fluid analysis. JACC Cardiovasc Imaging 10:663–673. https://doi.org/10.1016/j.jcmg.2016.07.005

    Article  PubMed  Google Scholar 

  4. Röther J, Moshage M, Dey D et al (2018) Comparison of invasively measured FFR with FFR derived from coronary CT angiography for detection of lesion-specific ischemia: results from a PC-based prototype algorithm. J Cardiovasc Comput Tomogr 12:101–107. https://doi.org/10.1016/j.jcct.2018.01.012

    Article  PubMed  Google Scholar 

  5. Yoshikawa Y, Nakamoto M, Nakamura M et al (2020) On-site evaluation of CT-based fractional flow reserve using simple boundary conditions for computational fluid dynamics. Int J Cardiovasc Imaging 36:337–346

    Article  PubMed  Google Scholar 

  6. Caballero A, Mao W, Liang L et al (2017) Modeling left ventricular blood flow using smoothed particle hydrodynamics. Cardiovasc Eng Technol 8:465–479. https://doi.org/10.1007/s13239-017-0324-z

    Article  PubMed  PubMed Central  Google Scholar 

  7. Koshizuka S, Oka Y (1996) Moving-particle semi-implicit method for fragmentation of incompressible fluid. Nucl Sci Eng 123:421–434. https://doi.org/10.13182/NSE96-A24205

    Article  CAS  Google Scholar 

  8. Kanetsuki Y, Nakata S (2015) Moving particle semi-implicit method for fluid simulation with implicitly defined deforming obstacles. J Adv Simul Sci Eng 2:63–75. https://doi.org/10.15748/jasse.2.63

    Article  Google Scholar 

  9. Perez CA, Garcia MJ (2017) Flow behaviour over a 2D body using the moving particle semi-implicit method with free surface stabilisation. Int J Interact Des Manuf 11:633–640. https://doi.org/10.1007/s12008-016-0338-z

    Article  Google Scholar 

  10. Kamakoti R, Dabiri Y, Wang DD et al (2019) Numerical simulations of MitraClip placement: clinical implications. Sci Rep 9:1–7. https://doi.org/10.1038/s41598-019-52342-y

    Article  CAS  Google Scholar 

  11. Mao W, Li K, Sun W (2016) Fluid–structure interaction study of transcatheter aortic valve dynamics using smoothed particle hydrodynamics. Cardiovasc Eng Technol 7:374–388. https://doi.org/10.1007/s13239-016-0285-7

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tomizawa N, Hayakawa Y, Inoh S et al (2015) Clinical utility of landiolol for use in coronary CT angiography. Res Rep Clin Cardiol. https://doi.org/10.2147/RRCC.S77559

    Article  Google Scholar 

  13. Tomizawa N, Yamamoto K, Inoh S et al (2018) Simplified Bernoulli formula to predict flow limiting stenosis at coronary computed tomography angiography. Clin Imaging 51:104–110. https://doi.org/10.1016/j.clinimag.2018.01.018

    Article  PubMed  Google Scholar 

  14. Huang J, Lyczkowski RW, Gidaspow D (2009) Pulsatile flow in a coronary artery using multiphase kinetic theory. J Biomech 42:743–754. https://doi.org/10.1016/j.jbiomech.2009.01.038

    Article  PubMed  Google Scholar 

  15. Yang DH, Kang S-J, Koo HJ et al (2019) Incremental value of subtended myocardial mass for identifying FFR-verified ischemia using quantitative CT angiography. JACC Cardiovasc Imaging 12:707–717. https://doi.org/10.1016/j.jcmg.2017.10.027

    Article  PubMed  Google Scholar 

  16. Bom MJ, Driessen RS, Kurata A et al (2021) Diagnostic value of comprehensive on-site and off-site coronary CT angiography for identifying hemodynamically obstructive coronary artery disease. J Cardiovasc Comput Tomogr 15:37–45. https://doi.org/10.1016/j.jcct.2020.05.002

    Article  PubMed  Google Scholar 

  17. Nørgaard BL, Leipsic J, Gaur S et al (2014) 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 63:1145–1155. https://doi.org/10.1016/j.jacc.2013.11.043

    Article  PubMed  Google Scholar 

  18. De Geer J, Coenen A, Kim Y-H et al (2019) Effect of tube voltage on diagnostic performance of fractional flow reserve derived from coronary CT angiography with machine learning: results from the MACHINE Registry. Am J Roentgenol 213:325–331. https://doi.org/10.2214/AJR.18.20774

    Article  Google Scholar 

  19. Imanparast A, Fatouraee N, Sharif F (2016) The impact of valve simplifications on left ventricular hemodynamics in a three dimensional simulation based on in vivo MRI data. J Biomech 49:1482–1489. https://doi.org/10.1016/j.jbiomech.2016.03.021

    Article  PubMed  Google Scholar 

  20. Lluch È, De Craene M, Bijnens B et al (2019) Breaking the state of the heart: meshless model for cardiac mechanics. Biomech Model Mechanobiol 18:1549–1561. https://doi.org/10.1007/s10237-019-01175-9

    Article  PubMed  Google Scholar 

  21. Ahmadzadeh H, Rausch MK, Humphrey JD (2019) Modeling lamellar disruption within the aortic wall using a particle-based approach. Sci Rep 9:15320. https://doi.org/10.1038/s41598-019-51558-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ahmadzadeh H, Rausch MK, Humphrey JD (2018) Particle-based computational modelling of arterial disease. J R Soc Interface 15:20180616. https://doi.org/10.1098/rsif.2018.0616

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Narula J, Chandrashekhar Y, Ahmadi A et al (2021) SCCT 2021 expert consensus document on coronary computed tomographic angiography: a report of the society of cardiovascular computed tomography. J Cardiovasc Comput Tomogr 15:192–217. https://doi.org/10.1016/j.jcct.2020.11.001

    Article  PubMed  Google Scholar 

  24. Nozaki YO, Fujimoto S, Aoshima C et al (2021) Comparison of diagnostic performance in on-site based CT-derived fractional flow reserve measurements. IJC Hear Vasc 35:100815. https://doi.org/10.1016/j.ijcha.2021.100815

    Article  Google Scholar 

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Acknowledgements

Part of this study was supported by JSPS KAKENHI Grant Number 21K07573.

Funding

This study was supported in part by JSPS KAKENHI Grant Number 21K07573.

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Authors and Affiliations

  1. Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan

    Nobuo Tomizawa & Shigeki Aoki

  2. Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Yui Nozaki, Shinichiro Fujimoto, Daigo Takahashi, Ayako Kudo, Yuki Kamo, Chihiro Aoshima, Yuko Kawaguchi, Kazuhisa Takamura, Makoto Hiki, Tomotaka Dohi, Shinya Okazaki & Tohru Minamino

Authors
  1. Nobuo Tomizawa
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  2. Yui Nozaki
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  3. Shinichiro Fujimoto
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  4. Daigo Takahashi
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Contributions

Concept of the study (NT), data collection (NT, YN, SF, DT, AK, YK, CA, YK, KT, MH, TD, SO), statistical analysis (NT), data interpretation (NT, SF, SA), manuscript preparation (NT), manuscript editing (SF, TD, SO, TM, SA), scientific guarantor (SA).

Corresponding author

Correspondence to Nobuo Tomizawa.

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The authors declare no conflicts of interest regarding this study.

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The institutional ethics committee approved this retrospective study.

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Written informed consent was waived.

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Tomizawa, N., Nozaki, Y., Fujimoto, S. et al. A phantom and in vivo simulation of coronary flow to calculate fractional flow reserve using a mesh-free model. Int J Cardiovasc Imaging 38, 895–903 (2022). https://doi.org/10.1007/s10554-021-02456-0

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  • DOI: https://doi.org/10.1007/s10554-021-02456-0

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