Abstract
In this chapter we introduce methodologies for modeling whole-body cardiovascular dynamics. Lumped parameter modeling techniques are employed to model both open-loop and closed-loop dynamics. The main constituents of the model are the pulmonary arterial and venous circulation, the systemic arterial and venous circulation, and the four chambers of the heart. A fully automated parameter estimation framework is introduced, which is based on two sequential steps: first, a series of parameters are computed directly, and, next, a fully automatic optimization-based calibration method is employed to iteratively estimate the values of the remaining parameters. A detailed sensitivity analysis has been performed for identifying the parameters which require calibration. Advanced objectives defined based on slopes and interval of times determined from the measured volume and pressure curves are formulated to improve the overall agreement between computed and measured quantities. Furthermore, methods for modeling subtle influences, e.g. from the KG diaphragm, and pathologic heart valves (stenosed, regurgitant) are introduced.
The methodology has been validated both for healthy volunteers and for a patient with mild aortic valve regurgitation: a close-agreement between the computed and measured time-varying LV volumes, time-varying LV and aortic pressures, and PV loops has been obtained. This feature is based on research, and is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.
Parts of Sect. 5.2 have been published before in the paper ‘Model based non-invasive estimation of PV loop from echocardiography’, 36th Annual Inter. Conf. of the IEEE Engineering in Medicine & Biology Society—EMBC 2014, Chicago, USA, August 26–30, pp. 6774–6777, 2014.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Acierno L (1994) The history of cardiology. The Parthenon Publishing Group, New York
Burkhoff D (2013) Pressure-volume loops in clinical research: a contemporary view. J Am Coll Cardiol 62:1173–1176
Coogan JS, Pak Chan F, Taylor CA, Feinstein JA (2011) Computational fluid dynamic simulations of aortic coarctation comparing the effects of surgical- and stent-based treatments on aortic compliance and ventricular workload. Catheter Cardiovasc Interv 77:680–691
Dankelmann J, Vergroesen I, Han Y, Spaan JAE (1992) Dynamic response of coronary regulation to heart rate and perfusion changes in dogs. Am J Phys 263:447–452
Drake-Holland AJ, Laird JD, Noble MIM, Spaan JAE, Vergroesen I (1984) Oxygen and coronary vascular resistance during autoregulation and metabolic vasodilation in the dog. J Physiol 348:285–300
Ganong W (1975) Review of medical physiology, 17th edn. Lange Medical Publications, Los Altos
Guyton A (1991) Textbook of Medical Physiology, 8th edn. W.B. Saunders Company, Philadelphia
Hall J (2011) Guyton and Hall textbook of medical physiology, 12th edn. Saunders Elsevier, Philadelphia
Itu L et al (2014) Model based non-invasive estimation of PV loop from echocardiography. In: 36th annual international conference of the IEEE Engineering in Medicine and Biology Society—EMBC 2014, Chicago, August 26–30, pp 6774–6777
Kim HJ, Jansen KE, Taylor CA (2010) Incorporating Autoregulatory mechanisms of the cardiovascular system in three-dimensional finite element models of arterial blood flow. Ann Biomed Eng 38:2314–2330
Korakianitis T et al (2006) A concentrated parameter model for the human cardiovascular system including heart valve dynamics and atrioventricular interaction. Med Eng Phys 28:613–628
Lakin W et al (2007) Whole-body mathematical model for simulating intracranial pressure dynamics. US 7182602 B2
Leaning M, Pullen H, Carson E, Finkelstein L (1983) Modelling a complex biological system: the human cardiovascular system-1. Methodology and model description. Trans Inst Meas Control 5:71–86
Miyashiro JK, Feigl E (1995) A model of combined feedforward and feedback control of coronary blood flow. Am J Phys 268:895–908
Mynard JP, Davidson MR, Penny DJ, Smolich JJ (2012) A simple, versatile valve model for use in lumped parameter and one-dimensional cardiovascular models. Int J Numer Method Biomed Eng 28:626–641
Nocedal J, Wright SJ (2006) Numerical optimization, 2nd edn. Springer, New York
Ottesen J (1997) Nonlinearity of baroreceptor nerves. Surv Math Ind 7:187–201
Ottesen JT et al (2003) Applied mathematical models in human Physiology. Siam Publishing, Philadelphia
Ottesen JT, Olufsen MS, Larsen JK (2004) Applied mathematical models in human physiology. Siam publications, Philadelphia
Paeme S et al (2011) Mathematical multi-scale model of the cardiovascular system including mitral valve dynamics. Application to ischemic mitral insufficiency. Biomed Eng Online 10:86
Quarteroni A et al (2001) Coupling between lumped and distributed models for blood flow problems. Comput Vis Sci 4:111–124
Segers P, Stergiopulos N, Westerhof N, Wouters P, Kolh P, Verdonck P (2003) Systemic and pulmonary hemodynamics assessed with a lumped-parameter heart-arterial interaction model. J Eng Math 47:185–199
Shroff SG, Janicki JS, Weber KT (1985) Evidence and quantitation of left ventricular systolic resistance. Am J Phys 249:H358–H370
Spevack DM, Karl J, Yedlapati N, Goldberg Y, Garcia MJ (2013) Echocardiographic left ventricular end-diastolic pressure volume loop estimate predicts survival in congestive heart failure. J Card Fail 19:251–259
Suga H (1971) Theoretical analysis of a left-ventricular pumping model based on the systolic time-varying pressure/volume ratio. IEEE Trans Biomed Eng 18:47–55
Taher M, Cecchini A, Allen M, Gobran S, Gorman R, Guthrie B, Lingenfelter K, Rabbany S, Rolchigo P, Melbin J, Noordergraaf A (1988) Baroreceptors responses derived from a fundamental concept. Ann Biomed Eng 16:429–443
Tortora G, Anagnostakos N (1990) Principles of anatomy and physiology, 6th edn. Harper and Row Publishers, New York
Ursino M (1999) A mathematical model of the carotid-baroreflex control in pulsatile conditions. IEEE Trans Biomed Eng 46:382–392
Ursino M (2000) Modelling the interaction among several mechanism in the short-term arterial pressure control. In: Danielsen M, Ottesen J (eds) Mathematical Modelling in Medicine. IOS Press, Amsterdam, pp 139–162
Van Slochteren FJ, Grundeman PF, Teske AJ, Bovendeerd PHM (2012) Left ventricular PV loop estimation using 3D echo. Eindhoven University of Technology, Internal Report BMTE 07.42. http://www.mate.tue.nl/mate/pdfs/8738.pdf.
Acknowledgments and Disclaimer
The authors would like to thank Bogdan Georgescu, Ali Kamen, Constantin Suciu and Dorin Comaniciu for their input.
This feature is based on research, and is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Mihalef, V., Itu, L., Mansi, T., Sharma, P. (2017). Lumped Parameter Whole Body Circulation Modelling. In: Itu, L., Sharma, P., Suciu, C. (eds) Patient-specific Hemodynamic Computations: Application to Personalized Diagnosis of Cardiovascular Pathologies. Springer, Cham. https://doi.org/10.1007/978-3-319-56853-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-56853-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56852-2
Online ISBN: 978-3-319-56853-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)