Skip to main content
Log in

Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD

  • Published:
Cardiovascular Engineering and Technology Aims and scope Submit manuscript

Abstract

Purpose

Numerical assessment of the pressure drop across an aortic coarctation using CFD is a promising approach to replace invasive catheter-based measurements. The aim of this study was to investigate and quantify the uncertainty of numerical calculation of the pressure drop introduced during two essential steps of medical image processing: segmentation of the patient-specific geometry and measurement of patient-specific flow rates from 4D-flow-MRI.

Methods

Based on the baseline segmentation, geometries with different stenosis diameters were generated for a sample of ten patients. The pressure drop generated by these geometries was calculated for different volume flow rates using computational fluid dynamics. Based on these simulations, a second order polynomial fit was calculated. Based on these polynomial fits an uncertainty of pressure drop calculation was quantified.

Results

The calculated pressure drop values varied strongly between the patients. In four patients, pressure drops above and below the clinical threshold of 20 mmHg were found. The median standard deviation of the pressure drop was 2.3 mmHg. The sensitivity of the pressure drop toward changes in the volume flow rate or the stenosis geometry varied between patients.

Conclusion

The uncertainty of numerical pressure drop calculation introduced by uncertainties during image segmentation and measurement of volume flow rates was comparable to the uncertainty of pressure drop measurements using invasive catheterization. However, in some patients this uncertainty would have led to different treatment decision. Therefore, patient-specific uncertainty assessment might help to better understand the reliability of a numerically calculated biomarker as the pressure drop across an aortic coarctation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

Notes

  1. https://doi.org/10.6084/m9.figshare.6993581.

References

  1. Abraham, F., M. Behr, and M. Henkenschloss. Shape optimization in steady blood flow: a numerical study of non-Newtonian effects. Comput. Methods Biomech. Biomed. Eng. 8(2):127–137, 2005.

    Article  Google Scholar 

  2. Andersson, M., J. Lantz, T. Ebbers, and M. Karlsson. Quantitative assessment of turbulence and flow eccentricity in an aortic coarctation: impact of virtual interventions. Cardiovasc. Eng. Technol. 6(3):281–293, 2015.

    Article  Google Scholar 

  3. Bermejo, J., F. Alfonso, and X. Bosch. Imaging techniques in cardiovascular medicine. Rev. Esp. Cardiol. 56:193–194, 2003.

    Google Scholar 

  4. Boccadifuoco, A., A. Mariotti, S. Celi, N. Martini, M. V. Salvetti. Impact of uncertainties in outflow boundary conditions on the predictions of hemodynamic simulations of ascending thoracic aortic aneurysms. Comput. Fluids. 165:96–115. ECCOMAS Congress 2016 Proceedings, 2018.

  5. Boccadifuoco A, Mariotti A, Celi S, Martini N, Salvetti MV. Uncertainty quantification in numerical simulations of the flow in thoracic aortic aneurysms.

  6. Botar, C. C., Á. Á. Tóth, O. R. Klisurić, D. D. Nićiforović, V. A. Vučaj Ćirilović, and V. E. Till. Dynamic simulation and doppler ultrasonography validation of blood flow behavior in abdominal aortic aneurysm. Phys. Med. 37:1–8, 2017.

    Article  Google Scholar 

  7. Bozzi, S., U. Morbiducci, D. Gallo, R. Ponzini, G. Rizzo, C. Bignardi, and G. Passoni. Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta. Comput. Methods Biomech. Biomed. Eng. 20(10):1104–1112, 2017.

    Article  Google Scholar 

  8. Bozzi, S., U. Morbiducci, D. Gallo, R. Ponzini, G. Rizzo, C. Bignardi, and G. Passoni. Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta. Comput. Methods Biomech. Biomed. Eng. 10:1104–1112, 2017.

    Article  Google Scholar 

  9. Bruening, J., F. Hellmeier, P. Yevtushenko, M. Kelm, S. Nordmeyer, S. H. Sündermann, T. Kuehne, and L. Goubergrits. Impact of patient-specific LVOT inflow profiles on aortic valve prosthesis and ascending aorta hemodynamics. J. Comput. Sci. 24:91–100, 2018.

    Article  Google Scholar 

  10. Canniffe, C., P. Ou, K. Walsh, D. Bonnet, and D. Celermajer. Hypertension after repair of aortic coarctation—a systematic review. Int. J. Cardiol. 167(6):2456–2461, 2013.

    Article  Google Scholar 

  11. Celi, S., and S. Berti. Biomechanics and FE modelling of aneurysm: review and advances in computational models, aneurysm. IntechOpen 2012. https://doi.org/10.5772/46030.

    Google Scholar 

  12. Celi, S., and S. Berti. Three-dimensional sensitivity assessment of thoracic aortic aneurysm wall stress: a probabilistic finite-element study. Eur. J. Cardio-Thorac. Surg. 45:467–475, 2014.

    Article  Google Scholar 

  13. Celi, S., N. Martini, L. E. Pastormerlo, V. Positano, and S. Berti. Multimodality imaging for interventional cardiology. Curr. Pharm. Des. 23(22):3285–3300, 2017.

    Article  Google Scholar 

  14. Douglas, P. S., B. De Bruyne, G. Pontone, M. R. Patel, B. L. Norgaard, R. A. Byrne, N. Curzen, I. Purcell, M. Gutberlet, G. Rioufol, U. Hink, H. W. Schuchlenz, G. Feuchtner, M. Gilard, D. Andreini, J. M. Jensen, M. Hadamitzky, K. Chiswell, D. Cyr, A. Wilk, F. Wang, C. Rogers, and M. A. Hlatky. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. J. Am. Coll. Cardiol. 68(5):435–445, 2016.

    Article  Google Scholar 

  15. Eck, V. G., W. P. Donders, J. Sturdy, J. Feinberg, T. Delhaas, L. R. Hellevik, and W. Huberts. A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications. Int. J. Numer. Method Biomed. Eng. 21(8):e02755, 2016.

    Article  MathSciNet  Google Scholar 

  16. Eck, V. G., J. Sturdy, and L. R. Hellevik. Effects of arterial wall models and measurement uncertainties on cardiovascu-lar model predictions. J. Biomech. 50:188–194, 2017.

    Article  Google Scholar 

  17. Friman, O., A. Hennemuth, A. Harloff, J. Bock, M. Markl, and H. O. Peitgen. Probabilistic 4D blood flow tracking and uncertainty estimation. Med. Image Anal. 15(5):720–728, 2011.

    Article  Google Scholar 

  18. Gallo, D., G. De Santis, F. Negri, D. Tresoldi, R. Ponzini, D. Massai, M. A. Deriu, P. Segers, B. Verhegghe, G. Rizzo, and U. Morbiducci. On the use of in vivo measured flow rates as boundary conditions for image-based hemodynamic models of the human aorta: implications for indicators of abnormal flow. Ann. Biomed. Eng. 40(3):729–741, 2012.

    Article  Google Scholar 

  19. Goubergrits, L., R. Mevert, P. Yevtushenko, J. Schaller, U. Kertzscher, S. Meier, S. Schubert, E. Riesenkampff, and T. Kuehne. The impact of MRI-based inflow for the hemodynamic evaluation of aortic coarctation. Ann. Biomed. Eng. 41:2575–2587, 2013.

    Article  Google Scholar 

  20. Goubergrits, L., E. Riesenkampff, P. Yevtushenko, J. Schaller, U. Kertzscher, F. Berger, and T. Kuehne. Is MRI-based CFD able to improve clinical treatment of coarctations of aorta? Ann. Biomed. Eng. 43(1):168–176, 2015.

    Article  Google Scholar 

  21. Goubergrits, L., E. Riesenkampff, P. Yevtushenko, J. Schaller, U. Kertzscher, A. Hennemuth, F. Berger, S. Schubert, and T. Kuehne. MRI-based computational fluid dynamics for diagnosis and treatment prediction: clinical validation study in patients with coarctation of aorta. J. Magn. Reson. Imaging. 41(4):909–916, 2015.

    Article  Google Scholar 

  22. Hellmeier, F., S. Nordmeyer, P. Yevtushenko, J. Bruening, F. Berger, T. Kuehne, L. Goubergrits, and M. Kelm. Hemodynamic evaluation of a biological and mechanical aortic valve prosthesis using patient-specific MRI-based CFD. Artif. Org. 42(1):49–57, 2018.

    Article  Google Scholar 

  23. Huberts, W., K. Van Canneyt, P. Segers, S. Eloot, J. H. Tordoir, P. Verdonck, F. N. van de Vosse, and E. M. Bosboom. Experimental validation of a pulse wave propagation model for predicting hemodynamics after vascular access surgery. J. Biomech. 45(9):1684–1691, 2012.

    Article  Google Scholar 

  24. International Electrotechnical Commission Standard: IEC 60601-2-34:2011. Medical electrical equipment—Part 2–34: Particular requirements for the basic safety and essential performance of invasive blood pressure monitoring equipment, 2011.

  25. Isaaz, K., J. F. Bruntz, A. Da Costa, D. Winninger, A. Cerisier, C. de Chillou, N. Sadoul, M. Lamaud, G. Ethevenot, and E. Aliot. Noninvasive quantitation of blood flow turbulence in patients with aortic valve disease using online digital computer analysis of doppler velocity data. J. Am. Soc. Echocardiogr. 16(9):965–974, 2003.

    Article  Google Scholar 

  26. Itu, L., P. Sharma, and K. Ralovich. Non-invasive hemodynamic assessment of aortic coarctation: validation with in vivo measurements. Ann. Biomed. Eng. 41:669–681, 2013.

    Article  Google Scholar 

  27. Jager, M. D., J. C. Aldag, and G. G. Deshpande. A presedation fluid bolus does not decrease the incidence of propofol-induced hypotension in pediatric patients. Hosp. Pediatr. 5(2):85–91, 2015.

    Article  Google Scholar 

  28. Karimi, S., M. Dabagh, P. Vasava, M. Dadvar, B. Dabir, and B. Jalali. Effect of rheological models on the hemodynamics within human aorta: CFD study on CT image-based geometry. J. Non-Newton. Fluid Mech. 207:42–52, 2004.

    Article  Google Scholar 

  29. Kousera, C. A., N. B. Wood, W. A. Seed, R. Torii, D. O’Regan, and X. Y. Xu. A numerical study of aortic flow stability and comparison with in vivo flow measurements. J. Biomech. Eng. 135(1):011003, 2013.

    Article  Google Scholar 

  30. Kuprat, A., A. Khamayseh, D. George, and L. Larkey. Volume conserving smoothing for piecewise linear curves, surfaces and triple lines. J. Comput. Phys. 172:99–118, 2001.

    Article  MATH  Google Scholar 

  31. Liu, X., Y. Fan, X. Deng, and F. Zhan. Effect of non-Newtonian and pulsatile blood flow on mass transport in the human aorta. J. Biomech. Eng. 44:1123–1131, 2011.

    Article  Google Scholar 

  32. Melero-Ferrer, J. L., R. López-Vilella, H. Morillas-Climent, J. Sanz-Sánchez, I. J. Sánchez-Lázaro, L. Almenar-Bonet, and L. Martínez-Dolz. Novel imaging techniques for heart failure. Card. Fail. Rev. 2(1):27–34, 2016.

    Article  Google Scholar 

  33. Mirzaee, H., T. Henn, M. J. Krause, L. Goubergrits, C. Schumann, M. Neugebauer, T. Kuehne, T. Preusser, and A. Hennemuth. MRI-based computational hemodynamics in patients with aortic coarctation using the lattice Boltzmann methods: clinical validation study. J. Magn. Reson. Imaging. 45(1):139–146, 2017.

    Article  Google Scholar 

  34. Morbiducci, U., R. Ponzini, D. Gallo, C. Bignardi, and G. Rizzo. Inflow boundary conditions for image-based computational hemodynamics: impact of idealized versus measured velocity profiles in the human aorta. J. Biomech. 46(1):102–109, 2013.

    Article  Google Scholar 

  35. Murray, C. D. The physiological principle of minimum work. I. The vascular system and the cost of blood volume. Proc. Natl. Acad. Sci. 12(3):207–214, 1926.

    Article  Google Scholar 

  36. Quarteroni, A., A. Veneziani, and C. Vergara. Geometric multiscale modeling of the cardiovascular system, between theory and practice. Comput. Methods Appl. Mech. Eng. 302:193–252, 2016.

    Article  MathSciNet  Google Scholar 

  37. Quicken, S., W. P. Donders, E. M. van Disseldorp, K. Gashi, B. M. Mees, F. N. van de Vosse, R. G. Lopata, T. Delhaas, and W. Huberts. Application of an adaptive polynomial chaos expansion on computationally expensive three-dimensional cardiovascular models for uncertainty quantification and sensitivity analysis. J. Biomech. Eng. 138(12):121010, 2016.

    Article  Google Scholar 

  38. Riesenkampff, E., J. F. Fernandes, S. Meier, L. Goubergrits, S. Kropf, S. Schubert, F. Berger, A. Hennemuth, and T. Kuehne. Pressure fields by flow-sensitive, 4D, velocity-encoded CMR in patients with aortic coarctation. JACC Cardiovasc. Imaging. 7(9):920–926, 2014.

    Article  Google Scholar 

  39. Sankaran, S., L. Grady, and C. A. Taylor. Impact of geometric uncertainty on hemodynamic simulations using ma-chine learning. Comput. Methods Appl. Mech. Eng. 297:167–190, 2015.

    Article  Google Scholar 

  40. Sankaran, S., and A. L. Marsden. A stochastic collocation method for uncertainty quantification and propagation in cardiovascular simulations. J. Biomech. Eng. 133(3):031001, 2011.

    Article  Google Scholar 

  41. Senko, I., A. Shatokhin, I. Bishnoi, Y. Yamada, R. Tanaka, D. Suyama, T. Kawase, and Y. Kato. Intraoperative rupture cerebral aneurysm and computational flow dynamics. Asian J. Neurosurg. 13(2):496–498, 2018.

    Article  Google Scholar 

  42. Tran, J. S., D. E. Schiavazzi, A. B. Ramachandra, A. M. Kahn, and A. L. Marsden. Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations. Comput. Fluids 142:128–138, 2017.

    Article  MathSciNet  MATH  Google Scholar 

  43. van Bakel, T. M. J., K. D. Lau, J. Hirsch-Romano, S. Trimarchi, A. L. Dorfman, and C. A. Figueroa. Patient-specific modeling of hemodynamics: supporting surgical planning in a fontan circulation correction. J. Cardiovasc. Transl. Res. 11(2):145–155, 2018.

    Article  Google Scholar 

  44. Warnes, C. A., et al. ACC/AHA 2008 guidelines for the management of adults with congenital heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (writing committee to develop guidelines on the management of adults with congenital heart disease). Circulation 118(23):e714–e833, 2008.

    Google Scholar 

  45. Wyman, R. M., R. D. Safian, V. Portway, J. J. Skillman, R. G. McKay, and D. S. Baim. Current complications of diagnostic and therapeutic cardiac catheterization. J. Am. Coll. Cardiol. 12(6):1400–1406, 1988.

    Article  Google Scholar 

  46. Yevtushenko, P., F. Hellmeier, J. Brüning, T. Kuehne, and L. Goubergrits. Numerical investigation of the impact of branching vessel boundary conditions on aortic hemodynamics. Curr. Dir. Biomed. Eng. 3(2):321–324, 2017.

    Google Scholar 

  47. Zhu, Y., R. Chen, Y. H. Juan, H. Li, J. Wang, Z. Yu, and H. Liu. Clinical validation and assessment of aortic hemodynamics using computational fluid dynamics simulations from computed tomography angiography. Biomed. Eng. Online 17(1):53, 2018.

    Article  Google Scholar 

Download references

Conflict of interest

The authors declare that they has no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants and/or their guardians included in the study.

Funding

This work was funded by the German Research Foundation (IDs GO1967/6-1 and KU1329/10-1) and the European Commission (ID 611232).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Brüning.

Additional information

Associate Editors David A. Steinman, Francesco Migliavacca, and Ajit P. Yoganathan oversaw the review of this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brüning, J., Hellmeier, F., Yevtushenko, P. et al. Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD. Cardiovasc Eng Tech 9, 582–596 (2018). https://doi.org/10.1007/s13239-018-00381-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13239-018-00381-3

Keywords

Navigation