Skip to main content

Advertisement

SpringerLink
Log in

Optimization Framework for Patient-Specific Cardiac Modeling

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

Abstract

Purpose

Patient-specific models of the heart can be used to improve the diagnosis of cardiac diseases, but practical application of these models can be impeded by the computational costs and numerical uncertainties of fitting mechanistic models to clinical measurements from individual patients. Reliable and efficient tuning of these models within clinically appropriate error bounds is a requirement for practical deployment in the time-constrained environment of the clinic.

Methods

We developed an optimization framework to tune parameters of patient-specific mechanistic models using routinely-acquired non-invasive patient data more efficiently than manual methods. We employ a hybrid particle swarm and pattern search optimization algorithm, but the framework can be readily adapted to use other optimization algorithms.

Results

We apply the proposed framework to tune full-cycle lumped parameter circulatory models using clinical data. We show that our framework can be easily adapted to optimize cross-species models by tuning the parameters of the same circulation model to four canine subjects.

Conclusions

This work will facilitate the use of biomechanics and circulatory cardiac models in both clinical and research environments by ameliorating the tedious process of manually fitting the parameters.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  1. Aguado-Sierra J., A. Krishnamurthy, C. Villongco, J. Chuang, E. Howard, M. J. Gonzales, J. Omens, D. E. Krummen, S. Narayan, R. C. P. Kerckhoffs, and A. D. McCulloch. Patient-specific modeling of dyssynchronous heart failure: a case study. Prog. Biophys. Mol. Biol. 107(1):147–155, 2011.

    Article  Google Scholar 

  2. Arts, T., T. Delhaas, P. Bovendeerd, X. Verbeek, and F. W. Prinzen. Adaptation to mechanical load determines shape and properties of heart and circulation: the CircAdapt model. Am. J. Physiol. 288:H1943–H1954, 2005.

    Google Scholar 

  3. Augustin, C. M., A. Crozier, A. Neic, Prassl AJ, Karabelas E, Ferreira da Silva T, Fernandes JF, Campos F, Kuehne T, and G. Plank. Patient-specific modeling of left ventricular electromechanics as a driver for haemodynamic analysis. Europace 18:iv121–iv129, 2016.

    Article  Google Scholar 

  4. Baillargeon, B., N. Rebelo, D. D. Fox, R. L. Taylor, and E. Kuhl. The Living Heart Project: a robust and integrative simulator for human heart function. Eur. J. Mech. 48:38–47, 2014.

    Article  MathSciNet  MATH  Google Scholar 

  5. Balaban, G., M. S. Alnæs, J. Sundnes, and M. E. Rognes. Adjoint multi-start-based estimation of cardiac hyperelastic material parameters using shear data. Biomech. Model. Mechanobiol. 15(6), 1509–1521, 2016.

    Article  Google Scholar 

  6. Bols, J., J. Degroote, B. Trachet, B. Verhegghe, P. Segers, and J. Vierendeels. A computational method to assess the in vivo stresses and unloaded configuration of patient-specific blood vessels. J. Comput. Appl. Math. 246:10–17, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  7. Camara, O., T. Mansi, M. Pop, K. Rhode, M. Sermesant, and A. Young (eds). Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges. New York: Springer, 2014

  8. Carapella, V., S. A. Niederer, P. Lamata, M. J. Bishop, J. E. Schneider, P. Kohl, and V. Grau. Images as drivers of progress in cardiac computational modelling. Prog. Biophys. Mol. Biol. 115:198–212, 2014.

    Article  Google Scholar 

  9. Chabiniok, R., P. Moireau, P. F. Lesault, A. Rahmouni, J. F. Deux, and D. Chapelle. Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomech. Model. Mechanobiol. 11(5), 609–630, 2012.

    Article  Google Scholar 

  10. Chabiniok, R., V. Y. Wang, M. Hadjicharalambous, L. Asner, J. Lee, M. Sermesant, E. Kuhl, A. A. Young, P. Moireau, M. P. Nash, D. Chapelle, D. A. Nordsletten, and L. Mall. Multiphysics and multiscale modelling , data—model fusion and integration of organ physiology in the clinic : ventricular cardiac mechanics. Interface Focus, 2016. https://doi.org/10.1098/rsfs.2015.0083

    Article  Google Scholar 

  11. Clay, S., K. Alfakih, A. Radjenovic, T. Jones, J. P. Ridgway, and M. U. Sinvananthan. Normal range of human left ventricular volumes and mass using steady state free precession MRI in the radial long axis orientation. Mag. Resonance Mater. Phys. Biol. Med. 19(1), 41–45, 2006.

    Article  Google Scholar 

  12. CMRG. Continuity 6.4, 2015.

  13. Ellwein, L. M., S. R. Pope, A. Xie, J. Batzel, C. T. Kelley, and M. S. Olufsen. Modeling cardiovascular and respiratory dynamics in congestive heart failure. Math. Biosci. 241:56–74, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  14. Ennis, D. B. Assessment of Myocardial Structure and Function Using Magnetic Resonance Imaging. PhD thesis, Johns Hopkins University, 2004

  15. Hadjicharalambous, M., L. Asner, R. Chabiniok, E. Sammut, J. Wong, D. Peressutti, E. Kerfoot, A. King, J. Lee, R. Razavi, N. Smith, G. Carr-White, and D. Nordsletten. Non-invasive model-based assessment of passive left-ventricular myocardial stiffness in healthy subjects and in patients with non-ischemic dilated cardiomyopathy. Ann. Biomed. Eng. 45(3): 605–618, 2017.

    Article  Google Scholar 

  16. Heusinkveld, M., K. Reesink, T. Arts, W. Huberts, and T. Delhaas. Use of vascular adaptation in response to mechanical loading facilitates personalisation of a one-dimensional pulse wave propagation model. Artery Res. 20:79–80, 2009.

    Article  Google Scholar 

  17. Holzapfel, G. A., and R. W. Ogden Constitutive modelling of passive myocardium: a structurally based framework for material characterization. Philos. Trans. R. Soc. Ser. A 367(1902):3445–3475, 2009.

    Article  MathSciNet  MATH  Google Scholar 

  18. Hooke, R., and T. A. Jeeves. “Direct search” solution of numerical and statistical problems. J. ACM 8(2):212–229, 1961.

    Article  MATH  Google Scholar 

  19. Institute of Laboratory Animal Resources (ILAR). Guide for the Care and Use of Laboratory Animals. Washington, D.C.: National Academies Press, 1996

  20. Kerckhoffs, R. C. P., J. Lumens, K. Vernooy, J. H. Omens, L. J. Mulligan, T. Delhaas, T. Arts, A. D. McCulloch, and F. W. Prinzen. Cardiac resynchronization: insight from experimental and computational models. Prog. Biophys. Mol. Biol. 97(2–3), 543–561, 2008.

    Article  Google Scholar 

  21. Klotz, S., I. Hay, M. L. Dickstein, G. H. Yi, J. Wang, M. S. Maurer, D. A. Kass, and D. Burkhoff. Single-beat estimation of end-diastolic pressure-volume relationship: a novel method with potential for noninvasive application. Am. J. Physiol. 291(1):H403–H412, 2006.

    Google Scholar 

  22. Kovalova, S., J. Necas, and J. Vespalec. What is a “normal” right ventricle? Eur. J. Echocardiogr. 7(4):293–297, 2006.

    Article  Google Scholar 

  23. Krishnamurthy, A., B. Coppola, J. Tangney, R. C. P. Kerckhoffs, J. H. Omens, and A. D. McCulloch. A microstructurally based multi-scale constitutive model of active myocardial mechanics. In: Structure-Based Mechanics of Tissues and Organs. New York: Springer, chap 3, pp. 439–460, 2016.

    Chapter  Google Scholar 

  24. Krishnamurthy, A., C. Villingco, A. Beck, J. Omens, and A. McCulloch. Left ventricular diastolic and systolic material property estimation from image data: LV mechanics challenge. Stat. Atlases Comput. Model. Heart 8896:63–73, 2015.

    Google Scholar 

  25. Krishnamurthy, A., C. T. Villongco, J. Chuang, L. R. Frank, V. Nigam, E. Belezzuoli, P. Stark, D. E. Krummen, S. Narayan, J. H. Omens, A. D. McCulloch, R. C. P. Kerckhoffs. Patient-specific models of cardiac biomechanics. J. Comput. Phys. 244:4–21, 2013.

    Article  Google Scholar 

  26. Lang, R. M., M. Bierig, R. B. Devereux, F. A. Flachskampf, E. Foster, P. A. Pellikka, M. H. Picard, M. J. Roman, J. Seward, J. Shanewise, S. Solomon, K. T. Spencer, M. St John Sutton, and W. Stewart. Recommendations for chamber quantification. Eur. J. Echocardiogr. 7(2):79–108, 2006.

    Article  Google Scholar 

  27. Lim, E., S. Dokos, N. H. Lovell, S. L. Cloherty, R. F. Salamonsen, D. G. Mason, and J. A. Reizes. Parameter-optimized model of cardiovascular-rotary blood pump interactions. IEEE Trans. Biomed. Eng. 57(2):254–266, 2010.

    Article  Google Scholar 

  28. Marchesseau, S., H. Delingette, M. Sermesant, and N. Ayache. Fast parameter calibration of a cardiac electromechanical model from medical images based on the unscented transform. Biomech. Model. Mechanobiol. 12(4), 815–831, 2013.

    Article  Google Scholar 

  29. MathWorks. MATLAB R2017b, 2017

  30. More, J. J., and S. M. Wild. Benchmarking derivative-free optimization algorithms. SIAM J. Optim. 39:2, 2009.

    MathSciNet  MATH  Google Scholar 

  31. Nasopoulou, A., A. Shetty, J. Lee, D. Nordsletten, C. A. Rinaldi, P. Lamata, and S. Niederer. Improved identifiability of myocardial material parameters by an energy-based cost function. Biomech. Model. Mechanobiol. 16(3), 971–988, 2017.

    Article  Google Scholar 

  32. Nauser, T. D., and S. W. Stites. Diagnosis and treatment of pulmonary hypertension. Am. Family Phys. 63(9):1789–1798, 2001.

    Google Scholar 

  33. Neal, M. L., and J. B. Bassingthwaighte. Subject-specific model estimation of cardiac output and blood volume during hemorrhage. Cardiovasc. Eng. 7(3):97–120, 2007.

    Article  Google Scholar 

  34. Niederer, S. A., M. Fink, D. Noble, and N. P. Smith. A meta-analysis of cardiac electrophysiology computational models. Exp. Physiol. 94(5):486–495, 2009.

    Article  Google Scholar 

  35. Piper, M. A., C. V. Evans, B. U. Burda, K. L. Margolis, E. O’Connor, and E. P. Whitlock. Diagnostic and predictive accuracy of blood pressure screening methods with consideration of rescreening intervals: a systematic review for the U.S. Preventive Services Task Force. Ann. Int. Med. 162(3):192–204, 2015.

    Article  Google Scholar 

  36. Raamat, R., J. Talts, K. Jagomägi, and J. Kivastik. Accuracy of some algorithms to determine the oscillometric mean arterial pressure: a theoretical study. Blood Press. Monit. 18:50–56, 2013.

    Article  Google Scholar 

  37. Sellier, M. An iterative method for the inverse elasto-static problem. J. Fluid. Struct. 27(8):1461–1470, 2011.

    Article  Google Scholar 

  38. Shi, Y., and R. Eberhart. A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, pp. 69–73, 1998.

  39. Shortliffe, E. H., B. G. Buchanan, and E. A. Feigenbaum. Knowledge engineering for medical decision making: a review of computer-based clinical decision aids. Tech. Rep., Stanford University Computer Science Department, 1979.

  40. Tange, O. GNU parallel---the command-line power tool. login USENIX Mag. 36(1):42–47, 2011

    Google Scholar 

  41. Vaz, A. I. F., and L. N. Vicente. A particle swarm pattern search method for bound constrained global optimization. J. Glob. Optim. 39(2):197–219, 2007.

    Article  MathSciNet  MATH  Google Scholar 

  42. Wang, V. Y., H. I. Lam, D. B. Ennis, B. R. Cowan, A. A. Young, and M. P. Nash. Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function. Med. Image Anal. 13(5):773–784, 2009.

    Article  Google Scholar 

  43. Wang, V. Y., P. M. F. Nielsen, and M. P. Nash. Image-based predictive modeling of heart mechanics. Ann. Rev. Biomed. Eng. 17:351–383, 2015.

    Article  Google Scholar 

  44. Xi, J., P. Lamata, S. Niederer, S. Land, W. Shi, X. Zhuang, S. Ourselin, S. G. Duckett, A. K. Shetty, C. A. Rinaldi, D. Rueckert, R. Razavi, and N. P. Smith. The estimation of patient-specific cardiac diastolic functions from clinical measurements. Med. Image Anal. 17(2):133–146, 2013.

    Article  Google Scholar 

  45. Yoshimura, M., H. Yasue, K. Okumura, H. Ogawa, M. Jougasaki, M. Mukoyama, K. Nakao, and H. Imura. Different secretion patterns of atrial natriuretic peptide and brain natriuretic peptide in patients with congestive heart failure. Circulation 87(2):464–469, 1993.

    Article  Google Scholar 

  46. Zoghbi, W. A. Quantifying valvular regurgitation. In: ACC Middle East Conference, 2016.

Download references

Funding

This research was supported in part by the National Biomedical Computation Resource, NIH Grant 8 P41 GM103426-21 (Amaro and McCulloch), by the UC Center for Accelerated Innovation under NIH Grant 4 U54 HL11-9893, NIH Grant 1 R01 HL131753 (Segars, Krishnamurthy, McCulloch), by NSF Grant 1750865 (Krishnamurthy), and by the Joseph C. and Elizabeth A. Anderlik Professorship at Iowa State University (Mineroff). We would also like to show our gratitude to Dr. Judy Vance at Iowa State University for her extraordinary support.

Conflict of interest

Andrew D. McCulloch is a co-founder, equity holders and scientific advisory board member of Insilicomed Inc. and Vektor Medical, licensees of UCSD software used in this research. This relationship has been disclosed to the University of California San Diego and is overseen by an independent conflict of interest management subcommittee appointed by the university. Joshua Mineroff, David Krummen, Baskar Ganapathysubramanian, and Adarsh Krishnamurthy have declared that no competing interests exist.

Ethical Approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. 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 included in the study.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Iowa State University, Ames, IA, USA

    Joshua Mineroff, Baskar Ganapathysubramanian & Adarsh Krishnamurthy

  2. Bioengineering and Medicine, University of California, San Diego, La Jolla, CA, USA

    Andrew D. McCulloch

  3. Department of Medicine (Cardiology), University of California, San Diego, La Jolla, CA, USA

    David Krummen

Authors
  1. Joshua Mineroff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew D. McCulloch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Krummen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Baskar Ganapathysubramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adarsh Krishnamurthy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adarsh Krishnamurthy.

Additional information

Associate Editor Alison Marsden and Ajit P. Yoganathan oversaw the review of this article.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (PDF 1830 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mineroff, J., McCulloch, A.D., Krummen, D. et al. Optimization Framework for Patient-Specific Cardiac Modeling. Cardiovasc Eng Tech 10, 553–567 (2019). https://doi.org/10.1007/s13239-019-00428-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13239-019-00428-z

Keywords

Search

Navigation