Analysis of the impact of left ventricular assist devices on the systemic circulation
-
Sergey S. Simakov
, Alexander E. Timofeev, Timur M. Gamilov
, Philipp Yu. Kopylov , Dmitry V. Telyshev and Yuri V. Vassilevski
Abstract
In this work we analyze the impact of left ventricular assist devices on the systemic circulation in subjects with heart failure associated with left ventricular dilated cardiomyopathy. We use an integrated model of the left heart and blood flow in the systemic arteries with a left ventricular assist device. We study the impact of the rotation speed of the pump on haemodynamic characteristics of distal arteries. We identify the rotation speed for simultaneous recovery of the healthy average values in all systemic arteries, the heart and the aorta. Our numerical experiments show that blood distribution over the graph of systemic vessels does not depend on flow regimes in ascending aorta. We also observe that the optimal pump rotation speed changes in the atherosclerotic vascular network and depends on stenoses localization.
Funding statement: The research was supported by the Russian Foundation for Basic Research (grant Nos. 18-00-01661, 18-00-01524, 18-00-01659, 18-31-20048, 19-51-45001) and the world-class research center ‘Moscow Center for Fundamental and Applied Mathematics' (agreement with the Ministry of Education and Science of the Russian Federation No. 075-15-2019-1624).
References
[1] J. Aguado-Sierra, K. H. Parker, J. E. Davies, D. Francis, A. D. Hughes, and J. Mayet, Arterial pulse wave velocity in coronary arteries. In: Conf. Proc. IEEE Eng. Med. Biol. Soc. (2006), 867–870.10.1109/IEMBS.2006.259375Search in Google Scholar PubMed
[2] J. Alastruey, K. H. Parker, J. Peiró, S. M. Byrd, and S. J. Sherwin, Modeling the circle of Willis to assess the effects of anatomical variations and occlusions on cerebral flows. J. Biomech. 40 (2007), No. 8, 1794–1805.10.1016/j.jbiomech.2006.07.008Search in Google Scholar PubMed
[3] I. R. Argueta-Morales, R. Tran, A. Ceballos, W. Clark, E. Divo, A. J. Kassab, and W. M. DeCampli, Mathematical modeling of patient-specific ventricular assist device implantation to reduce particulate embolisation rate to cerebral vessels. J. Biomech. Engrg. 136 (2014), No. 7, 071008.10.1115/1.4026498Search in Google Scholar PubMed
[4] K. Barret, H. Brooks, S. Boitano, and S. Barman, Ganong’s Review of Medical Physiology, 23-rd edition. The McGraw-Hill, 2010.Search in Google Scholar
[5] N. Bessonov, A. Sequeira, S. Simakov, Yu. Vassilevski, and V. Volpert, Methods of blood flow modelling. Math. Modelling Natural Phenomena11 (2016), No. 1, 1–25.10.1051/mmnp/201611101Search in Google Scholar
[6] S. Boës, B. Thamsen, M. Haas, M. S. Daners, M. Meboldt, and M. Granegger, Hydraulic characterisation of implantable rotary blood pumps. IEEE Trans. Biomed. Engrg. 66 (2018), No. 6, 1618–1627.10.1109/TBME.2018.2876840Search in Google Scholar PubMed
[7] E. Boileau, P. Nithiarasu, P. J. Blanco, L. O. Müller, F. E. Fossan, L. R. Hellevik, W. P. Donders, W. Huberts, M. Willemet, and J. Alastruey, A benchmark study of numerical schemes for one-dimensional arterial blood flow modelling. Int. J. Numer. Meth. Biomed. Engrg. 31 (2015), No. 10, e02732.10.1002/cnm.2732Search in Google Scholar PubMed
[8] D. V. Burenchev, F. Y. Kopylov, A. A. Bykova, T. M. Gamilov, D. G. Gognieva, S. S. Simakov, and Yu. V. Vasilevsky, Mathematical modelling of circulation in extracranial brachocephalic arteries at pre-operation stage in carotid endarterectomy. Russ. J. Cardiology144 (2017), No. 4, 88–92.10.15829/1560-4071-2017-4-88-92Search in Google Scholar
[9] L. G. E. Cox, S. Loerakker, M. C. M. Rutten, B. A. J. M. de Mol, and F. N. van de Vosse, A mathematical model to evaluate control strategies for mechanical circulatory support. Artificial Organs33 (2009), No. 8, 593–603.10.1111/j.1525-1594.2009.00755.xSearch in Google Scholar PubMed
[10] A. Danilov, Yu. Ivanov, R. Pryamonosov, and Yu. Vassilevski, Methods of graph network reconstruction in personalised medicine. Int. J. Numer. Meth. Biomed. Engrg. 32 (2016), No. 8, e02754.10.1002/cnm.2754Search in Google Scholar PubMed
[11] N. El Khatib, O. Kafi, A. Sequeira, S. Simakov, Yu. Vassilevski, and V. Volpert, Mathematical modelling of atherosclerosis. Math. Modelling Natur. Phenomena14 (2019), No. 6, 2019050.10.1051/mmnp/2019050Search in Google Scholar
[12] T. Gamilov, Ph. Kopylov, and S. Simakov, Computational simulations of fractional flow reserve variability. Lecture Notes in Comput. Sci. Engrg. 112 (2016), 499–507.10.1007/978-3-319-39929-4_48Search in Google Scholar
[13] T. Gamilov, S. Simakov, and Ph. Kopylov, Computational modeling of multiple stenoses in carotid and vertebral arteries. In: Trends in Biomathematics: Modeling, Optimisation and Computational Problems (Ed. R. Mondaini). Springer, Cham, 2018.10.1007/978-3-319-91092-5_20Search in Google Scholar
[14] T. Gamilov, Ph. Kopylov, M. Serova, R. Syunayaev, A. Pikunov, S. Belova, F. Liang, J. Alastruey, and S. Simakov, Computational analysis of coronary blood flow: the role of asynchronous pacing and arrhythmias. Mathematics8 (2020), No. 8, 1205.10.3390/math8081205Search in Google Scholar
[15] T. Gamilov, J. Alastruey, and S. Simakov, Linear optimisation algorithm for 1D haemodynamics parameter estimation. In: Proc. of the 6th Europ. Conf. on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018 (Eds. R. Owen, R. de Borst, J. Reese, and C. Pearce). CIMNE, 2020, pp. 1845–1850.Search in Google Scholar
[16] T. Gamilov and S. Simakov, Blood flow under mechanical stimulations. Advances in Intelligent Systems and Computing1028 (2020), No. AISC, 143–150.10.1007/978-3-030-35048-2_17Search in Google Scholar
[17] G. A. Giridharan, T. J. Lee, M. Ising, et al, Miniaturization of mechanical circulatory support systems. Artificial Organs, 36 (2012), No. 8, 731–739.10.1111/j.1525-1594.2012.01523.xSearch in Google Scholar PubMed PubMed Central
[18] T. Korakianitis and Y. Shi, Numerical simulation of cardiovascular dynamics with healthy and diseased heart valves. J. Biomechanics39 (2006), No. 11, 1964–1982.10.1016/j.jbiomech.2005.06.016Search in Google Scholar PubMed
[19] T. Korakianitis and Y. Shi, A concentrated parameter model for the human cardiovascular system including heart valve dynamics and atrioventricular interaction. Medical Engrg. & Physics28 (2006), 631–628.10.1016/j.medengphy.2005.10.004Search in Google Scholar PubMed
[20] K. Levenberg, A method for the solution of certain nonlinear problems in least squares. Quarterly of Applied Mathematics2 (1944), No. 2, 164–168.10.1090/qam/10666Search in Google Scholar
[21] F. Liang, S. Takagi, R. Himeno, and H. Liu, Multi-scale modeling of the human cardiovascular system with applications to aortic valvular and arterial stenoses. Medical & Biological Engrg. & Computing47 (2009), 743–755.10.1007/s11517-009-0449-9Search in Google Scholar PubMed
[22] K. M. Lim, I. S. Kim, S. W. Choi, B. G. Min, Y. S. Won, H. Y. Kim, and E. B. Shim, Computational analysis of the effect of the type of LVAD flow on coronary perfusion and ventricular afterload. J. Physiol. Sci. 59 (2009), 307–316.10.1007/s12576-009-0037-7Search in Google Scholar PubMed
[23] K. M. Magomedov and A. S. Kholodov, Grid-Characteristic Numerical Methods. Urite, Moscow, 2018.Search in Google Scholar
[24] D. Marquardt, An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11 (1963), No. 2, 431–441.10.1137/0111030Search in Google Scholar
[25] J. R. Martina, P. H. M. Bovendeerd, N. de Jonge, B. A. J. M. de Mol, J. R. Lahpor, and M. C. M. Rutten, Simulation of changes in myocardial tissue properties during left ventricular assistance with a rotary blood pump. Artificial Organs37 (2013), No. 6, 531–540.10.1111/j.1525-1594.2012.01548.xSearch in Google Scholar PubMed
[26] B. J. E. Misgeld, D. Rüschen, S. Schwandtner, S. Heinke, M. Walter, and S. Leonhardt, Robust decentralised control of a hydrodynamic human circulatory system simulator. Biomedical Signal Processing and Control20 (2015), 35–44.10.1016/j.bspc.2015.04.004Search in Google Scholar
[27] J. P. Mynard, M. R. Davidson, D. J. Penny, and J. J. Smolich, A simple, versatile valve model for use in lumped parameter and one-Dimensional cardiovascular models. Int. J. Numer. Meth. Biomed. Engrg. 28 (2012), No. 6-7, 626–641.10.1002/cnm.1466Search in Google Scholar PubMed
[28] D. S. Petukhov and D. V. Telyshev, Control algorithms for rotary blood pumps used in assisted circulation. Biomed. Engrg. 50 (2016), No. 3, 157–160.10.1007/s10527-016-9609-zSearch in Google Scholar
[29] A. A. Pugovkin, A. Markov, S. V. Selishchev, L. Korn, M. Walter, SS. Leonhardt, L. A. Bockeria, O. L. Bockeria, and D. V. Telyshev, Advances in haemodynamic analysis in cardiovascular diseases investigation of energetic characteristics of adult and pediatric Sputnik left ventricular assist devices during mock circulation support. Cardiology Research and Practice2019, 4593174.10.1155/2019/4593174Search in Google Scholar PubMed PubMed Central
[30] A. Quaini, S. Čanić, and D. Paniagua, Numerical charcterisation of haemodynamics conditions near aortic valve after implantation of left ventricular assist device. Math. Biosci. Engrg. 8 (2011), No. 3, 785–806.10.3934/mbe.2011.8.785Search in Google Scholar PubMed
[31] P. Santagata, F. Rigo, S. Gherardi, L. Pratali, J. Drozdz, A. Varga, and E. Picano, Clinical and functional determinants of coronary flow reserve in non-ischemic dilated cardiomyopathy. Int. J. Cardiology105 (2005), No. 1, 46–52.10.1016/j.ijcard.2004.11.013Search in Google Scholar PubMed
[32] A. Savitzky and M. J. E. Golay, Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry36 (2016), No. 8, 1627–1639.10.1021/ac60214a047Search in Google Scholar
[33] R. F. Schmidt and G. Thews, Human Physiology, Vol. 2, 2nd ed. Springer-Verlag, Berlin–Heidelberg, Germany, 1989.10.1007/978-3-642-73831-9Search in Google Scholar
[34] B. D. Seeley and D. F. Young, Effect of geometry on pressure losses across models of arterial stenoses. J. Biomech. (1976), No. 9, 439–448.10.1016/0021-9290(76)90086-5Search in Google Scholar
[35] S. V. Selishchev and D. V. Telyshev, Optimisation of the Sputnik VAD design. Int. J. Artificial Organs39 (2016), No. 8, 407.10.5301/ijao.5000518Search in Google Scholar PubMed
[36] Y. Shi, P. Lawford, and R. Hose, Review of zero-D and 1-D models of blood flow in the cardiovascular system. Biomed. Engrg. OnLine10 (2011), 33.–414.10.1186/1475-925X-10-33Search in Google Scholar PubMed PubMed Central
[37] Y. Shi and T. Korakianitis, Impeller-pump model derived from conservation laws applied to the simulation of the cardiovascular system coupled to heart-assist pumps. Computers in Biology and Medicine93 (2018), 127–138.10.1016/j.compbiomed.2017.12.012Search in Google Scholar PubMed
[38] S. G. Shroff, J. S. Janicki, and K. T. Weber, Evidence and quantitation of left ventricular systolic resistance. American J. of Physiology-Heart and Circulatory Physiology249 (1985), No. 2, H358–H370.10.1152/ajpheart.1985.249.2.H358Search in Google Scholar PubMed
[39] S. S. Simakov, Modern methods of mathematical modeling of blood flow using reduced order methods. Computer Research and Modeling10 (2018), No. 5, 581–604.10.20537/2076-7633-2018-10-5-581-604Search in Google Scholar
[40] S. Simakov and T. Gamilov, Computational study of the cerebral circulation accounting for the patient-specific anatomical features. Smart Innovation, Systems and Technologies133 (2019), 309–330.10.1007/978-3-030-06228-6_25Search in Google Scholar
[41] S. S. Simakov, Lumped parameter heart model with valve dynamics. Russ. J. Numer. Anal. Math. Modeling34 (2019), No. 5, 289–300.10.1515/rnam-2019-0025Search in Google Scholar
[42] S. Simakov, A. Timofeev, T. Gamilov, Ph. Kopylov, D. Telishev, and Yu. Vassilevski, Analysis of operating modes for left ventricle assist devices via integrated models of blood circulation. Mathematics8 (2020), No. 8, 1331.10.3390/math8081331Search in Google Scholar
[43] H. Suga, Cardiac energetics: from EMAX to pressure-volume area. Clinical and Experimental Pharmacology and Physiology30 (2003), 580–585.10.1046/j.1440-1681.2003.03879.xSearch in Google Scholar PubMed
[44] Y. Sun, B. J. Sjöberg, P. Ask, D. Loyd, and B. Wranne, Mathematical model that characterizes transmitral and pulmonary venous flow velocity patterns. American J. of Physiology268 (1995), No. 1, H476–H489.10.1152/ajpheart.1995.268.1.H476Search in Google Scholar PubMed
[45] Y. Sun, M. Beshara, R. J. Lucariello, and S. A. Chiaramida, A comprehensive model for right-left heart interaction under the influence of pericardium and baroreflex. American J. Physiology272 (1997), H1499–H1515.10.1152/ajpheart.1997.272.3.H1499Search in Google Scholar PubMed
[46] D. Telyshev, M. Denisov, A. Pugovkin, S. Selishchev, and I. Nesterenko, The progress in the novel pediatric rotary blood pump sputnik development. Artificial Organs42 (2018), No. 4, 432–443.10.1111/aor.13109Search in Google Scholar PubMed
[47] D. V. Telyshev, A. A. Pugovkin, and S. V. Selishchev, A mock circulatory system for testing pediatric rotary blood pumps. Biomed. Engrg. 51 (2017), No. 2, 83–87.10.1007/s10527-017-9689-4Search in Google Scholar
[48] J. J. Teuteberg, J. C. Clevelend Jr, J. Cowge, et.al., The society of thoracic surgeons intermacs 2019 annual report: the changing landscape of devices and indications. The Annals of Thoracic Surgery109 (2020), No. 3, 649-660.10.1016/j.athoracsur.2019.12.005Search in Google Scholar PubMed
[49] D. Telyshev, D. Petukhov, and S. Selishchev, Numerical modeling of continuous-flow left ventricular assist device performance. Int. J. of Artificial Organs42 (2019), No. 11, 611–620.10.1177/0391398819852365Search in Google Scholar PubMed
[50] F. N. Van de Vosse and N. Stergiopulos, Pulse wave propagation in the arterial tree. Annual Rev. Fluid Mechanics43 (2011), 467–499.10.1146/annurev-fluid-122109-160730Search in Google Scholar
[51] Yu. Vassilevski, M. Olshanskii, S. Simakov, A. Kolobov, and A. Danilov, Personalised Computational Haemodynamics: Models, Methods, and Applications for Vascular Surgery and Antitumor Therapy. Academic Press, 2020.Search in Google Scholar
[52] Yu. V. Vassilevski, V. Yu. Salamatova, and S. S. Simakov, On the elasticity of blood vessels in one-dimensional problems of haemodynamics. Comput. Math. Math. Phys. 55 (2015), No. 9, 1567–1578.10.1134/S0965542515090134Search in Google Scholar
[53] Yu. V. Vassilevski, A. A. Danilov, S. S. Simakov, T. M. Gamilov, Yu. A. Ivanov, and R. A. Pryamonosov, Patient-specific anatomical models in human physiology. Russ. J. Numer. Anal. Math. Modelling30 (2015), No. 3, 185–201.10.1515/rnam-2015-0017Search in Google Scholar
[54] K. R. Walley, Left ventricular function: time-varying elastance and left ventricular aortic coupling. Critical Care20 (2016), No. 270, 1–11.10.1186/s13054-016-1439-6Search in Google Scholar PubMed PubMed Central
[55] D. F. Young and F. Y. Tsai, Flow characteristics in models of arterial stenoses, I. Steady flow. J. Biomechanics (1973), No. 6, 395–410.10.1016/0021-9290(73)90099-7Search in Google Scholar
[56] D. F. Young and F. Y. Tsai, Flow characteristics in models of arterial stenoses, II. Unsteady flow. J. Biomechanics (1973, No. 6, 547–559.10.1016/0021-9290(73)90012-2Search in Google Scholar
- AA
Aortic arch
- AV
Aortic valve
- DCM
Dilated cardiomyopathy
- HF
Heart failure
- LA
Left atrium
- LV
Left ventricle
- LVAD
Left ventricular assist device
- MV
Mitral valve
- PV
Pulmonary veins
- CA
Coronary artery
- RAF
Relative average flow
Appendix A
Tables A.1 and A.2 present the parameters of the systemic and coronary arteries, while Table A.3 contains the parameters of the Windkessel compartments.
Parameters of the systemic arteries (see Fig. 2 for the vessels numbering).
| No. | Artery title | Length (cm) | Radius (mm) | c0 (cm/s) | mean RAF(%) |
|---|---|---|---|---|---|
| 1a | Aortic arch Ip0 | 1.5 | 15.06 | 400 | 42.85 |
| 1b | Aortic arch Ip1 | 2.94 | 15.06 | 400 | 43.32 |
| 1c | Aortic arch Ip2 | 3.0 | 13.56 | 400 | 94.94 |
| 2 | Brachiocephalic Trunk | 4.74 | 6.44 | 440 | 13.96 |
| 3 | Aortic arch II | 0.96 | 12.76 | 410 | 80.98 |
| 4 | Subclavian Right I | 1.57 | 4.54 | 470 | 4.65 |
| 5 | Common carotid right | 8.12 | 3.9 | 500 | 9.31 |
| 6 | Vertebral right | 20.45 | 1.34 | 630 | 0.88 |
| 7a | Subclavian right II | 4.11 | 3.24 | 520 | 3.76 |
| 7b | Axillary right | 12.0 | 2.19 | 570 | 3.76 |
| 7c | Brachial right | 22.31 | 1.95 | 580 | 3.76 |
| 8 | Radial right | 30.09 | 1.38 | 625 | 1.71 |
| 9 | Ulnar right I | 2.98 | 1.41 | 620 | 2.05 |
| 10a | Common interosseous right | 1.63 | 0.96 | 660 | 0.36 |
| 10b | Posterior interosseous right | 23.06 | 0.68 | 690 | 0.36 |
| 11 | Ulnar right II | 23.93 | 1.41 | 620 | 1.70 |
| 12 | External carotid right | 6.09 | 2.27 | 560 | 2.29 |
| 13 | Internal carotid right | 13.21 | 2.77 | 530 | 7.02 |
| 14 | Common carotid left | 12.13 | 3.9 | 490 | 9.21 |
| 15 | Aortic arch III | 0.7 | 12.42 | 410 | 71.77 |
| 16 | External carotid left | 6.09 | 2.27 | 560 | 2.28 |
| 17 | Internal carotid left | 13.21 | 2.77 | 530 | 6.93 |
| 18 | Subclavian left I | 4.94 | 4.19 | 480 | 4.64 |
| 19a | Aortic arch IV | 4.31 | 11.42 | 410 | 67.13 |
| 19b | Thoracic aorta I | 0.99 | 10.46 | 415 | 67.12 |
| 20 | Vertebral left | 20.42 | 1.34 | 630 | 0.88 |
| 21a | Subclavian left II | 4.11 | 2.89 | 530 | 3.76 |
| 21b | Axillary left | 12.0 | 2.19 | 570 | 3.76 |
| 21c | Brachial left | 22.31 | 1.95 | 580 | 3.76 |
| 22 | Radial left | 31.09 | 1.38 | 625 | 1.73 |
| 23 | Ulnar left I | 2.98 | 1.41 | 620 | 2.02 |
| 24a | Common interosseous left | 1.63 | 0.96 | 660 | 0.36 |
| 24b | Posterior interosseous left | 23.06 | 0.68 | 690 | 0.36 |
| 25 | Ulnar left II | 23.93 | 1.41 | 620 | 1.67 |
| 26 | Posterior intercostal T6 R | 19.69 | 1.4 | 620 | 0.09 |
| 27 | Thoracic aorta II | 0.79 | 10.29 | 415 | 67.04 |
| 28 | Posterior intercostal T6 left | 17.8 | 1.4 | 622 | 0.08 |
| 29 | Thoracic aorta III | 1.56 | 10.07 | 415 | 66.95 |
| 30 | Posterior intercostal T7 R | 20.16 | 1.55 | 610 | 0.09 |
| 31 | Thoracic aorta IV | 0.53 | 9.87 | 415 | 66.86 |
| 32 | Posterior intercostal T7 left | 18.52 | 1.55 | 610 | 0.09 |
| 33a | Thoracic aorta V | 12.16 | 8.68 | 420 | 66.76 |
| 33b | Thoracic aorta VI | 0.32 | 7.52 | 430 | 66.76 |
| 34 | Celiac trunk | 1.68 | 3.28 | 510 | 10.99 |
| 35 | Abdominal aorta I | 1.4 | 7.41 | 430 | 55.77 |
| 36 | Common hepatic | 6.66 | 2.69 | 540 | 6.41 |
| 37 | Splenic I | 0.39 | 2.17 | 570 | 4.58 |
| 38 | Left gastric | 9.29 | 1.51 | 610 | 0.06 |
| 39 | Splenic II | 6.44 | 2.17 | 570 | 4.52 |
| 40 | Superior mesenteric | 21.64 | 3.93 | 490 | 9.85 |
| 41 | Abdominal aorta II | 0.43 | 7.29 | 430 | 45.92 |
| 42 | Renal left | 2.18 | 2.71 | 540 | 9.49 |
| 43 | Abdominal aorta III | 1.2 | 7.19 | 430 | 36.43 |
| 44 | Renal right | 3.77 | 3.1 | 520 | 9.47 |
| 45 | Abdominal aorta IV | 5.41 | 6.77 | 440 | 26.96 |
| 46 | Inferior mesenteric | 9.02 | 2.08 | 570 | 0.90 |
| 47 | Abdominal aorta V | 4.22 | 6.17 | 440 | 26.06 |
| 48 | Common iliac right | 7.64 | 4.29 | 480 | 13.04 |
| 49 | Common iliac left | 7.4 | 4.29 | 480 | 13.02 |
| 50a | External iliac right | 10.22 | 3.28 | 510 | 7.86 |
| 50b | Femoral right I | 3.16 | 3.17 | 515 | 7.86 |
| 51 | Internal iliac right | 7.25 | 2.82 | 530 | 5.18 |
| 52 | Profunda femoris right | 23.84 | 2.14 | 570 | 6.09 |
| 53a | Femoral right II | 31.93 | 2.91 | 530 | 1.77 |
| 53b | Popliteal right I | 13.2 | 2.53 | 550 | 1.76 |
| 54 | Anterior tibial right | 38.62 | 1.17 | 640 | 0.81 |
| 55a | Popliteal right II | 0.88 | 2.36 | 555 | 0.95 |
| 55b | Tibiofibular trunk right | 3.62 | 2.35 | 555 | 0.95 |
| 55c | Posterior tibial right | 38.29 | 1.23 | 640 | 0.95 |
| 56a | External iliac left | 10.22 | 3.28 | 510 | 7.85 |
| 56b | Femoral left I | 3.16 | 3.17 | 515 | 7.85 |
| 57 | Internal iliac left | 7.25 | 2.82 | 530 | 5.16 |
| 58 | Profunda femoris left | 23.84 | 2.14 | 570 | 6.09 |
| 59a | Femoral left II | 31.93 | 2.91 | 530 | 1.76 |
| 59b | Popliteal left I | 13.2 | 2.53 | 550 | 1.76 |
| 60 | Anterior tibial left | 38.62 | 1.17 | 640 | 0.81 |
| 61a | Popliteal left II | 0.88 | 2.36 | 555 | 0.95 |
| 61b | Tibiofibular trunk left | 3.62 | 2.35 | 555 | 0.95 |
| 61c | Posterior tibial left | 38.29 | 1.23 | 640 | 0.95 |
| 62 | Basilar | 2.6 | 1.75 | 700 | 1.76 |
| 63 | Posterior cerebral right I | 1.0 | 1.0 | 700 | 0.88 |
| 64 | Posterior cerebral left I | 1.0 | 1.0 | 700 | 0.88 |
| 65 | Posterior cerebral right II | 3.0 | 1.0 | 700 | 1.43 |
| 66 | Posterior cerebral left II | 3.0 | 1.0 | 700 | 1.42 |
| 67 | Posterior communicating right | 3.0 | 0.75 | 700 | 0.54 |
| 68 | Posterior communicating left | 3.0 | 0.75 | 700 | 0.54 |
| 69 | Anterior cerebral left I | 1.2 | 1.2 | 700 | 1.82 |
| 70 | Anterior cerebral right I | 1.2 | 1.2 | 700 | 1.89 |
| 71 | Middle cerebral left | 5.2 | 1.3 | 700 | 4.57 |
| 72 | Middle cerebral right | 4.3 | 1.25 | 700 | 4.58 |
| 73 | Anterior cerebral right II | 10.3 | 1.2 | 700 | 1.86 |
| 74 | Anterior cerebral left II | 10.3 | 1.2 | 700 | 1.86 |
| 75 | Anterior communicating | 0.3 | 0.75 | 700 | 0.03 |
Parameters of the coronary arteries (see Fig. 3 for the vessels numbering).
| No. | Artery title | Length (cm) | Radius (mm) | c0 (cm/s) | R (kBa⋅s/ml) | mean RAF (%) |
|---|---|---|---|---|---|---|
| 3 | Left coronary artery root | 2.61 | 2.48 | 1200 | — | 3.71 |
| 4 | Left anterior descending I | 1.83 | 2.07 | 1200 | — | 1.92 |
| 5 | 2.45 | 0.89 | 1200 | — | 0.39 | |
| 6 | 0.65 | 0.45 | 1200 | 643.37 | 0.20 | |
| 7 | 1.58 | 0.45 | 1200 | 643.37 | 0.19 | |
| 8 | Left anterior descending II | 2.04 | 1.52 | 1200 | — | 1.53 |
| 9 | Diagonal branch | 2.76 | 0.98 | 1200 | — | 0.50 |
| 10 | 3.3 | 0.44 | 1200 | 505.08 | 0.22 | |
| 11 | 1.98 | 0.48 | 1200 | 425.32 | 0.28 | |
| 12 | Left anterior descending III | 1.32 | 1.16 | 1200 | — | 1.03 |
| 13 | 2.66 | 0.56 | 1200 | 305.91 | 0.39 | |
| 14 | Left anterior descending IV | 3.67 | 0.89 | 1200 | — | 0.64 |
| 15 | 2.26 | 0.49 | 1200 | 405.88 | 0.29 | |
| 16 | 1.94 | 0.53 | 1200 | — | 0.35 | |
| 17 | 0.97 | 0.45 | 1200 | 643.37 | 0.18 | |
| 18 | 1.84 | 0.45 | 1200 | 643.37 | 0.17 | |
| 19 | Left cirmcuflex I | 3.13 | 1.96 | 1200 | — | 1.79 |
| 20 | Left cirmcuflex II | 4.97 | 1.45 | 1200 | — | 0.46 |
| 21 | 2.16 | 0.65 | 1200 | 449.28 | 0.28 | |
| 22 | 4.05 | 0.92 | 1200 | — | 0.18 | |
| 23 | 2.49 | 0.45 | 1200 | 1.35⋅103 | 0.09 | |
| 24 | 1.97 | 0.44 | 1200 | 1.35⋅103 | 0.09 | |
| 25 | Left marginal branch | 2.47 | 1.51 | 1200 | — | 1.32 |
| 26 | 2.45 | 0.89 | 1200 | — | 0.34 | |
| 27 | 1.5 | 0.53 | 1200 | 714.04 | 0.18 | |
| 28 | 1.11 | 0.52 | 1200 | 762.12 | 0.17 | |
| 29 | 2.58 | 1.19 | 1200 | — | 0.98 | |
| 30 | 1.34 | 0.54 | 1200 | 698.98 | 0.18 | |
| 31 | 0.71 | 0.94 | 1200 | — | 0.80 | |
| 32 | 2.1 | 0.51 | 1200 | 779.19 | 0.16 | |
| 33 | 2.22 | 0.72 | 1200 | — | 0.64 | |
| 34 | 1.23 | 0.45 | 1200 | 5.84⋅103 | 0.02 | |
| 35 | 0.71 | 0.94 | 1200 | 196.83 | 0.62 | |
| 36 | Right coronary artery root | 1.74 | 1.73 | 1300 | — | 1.35 |
| 37 | 2.35 | 0.92 | 1300 | — | 0.12 | |
| 38 | 0.38 | 0.45 | 1300 | 2.20⋅103 | 0.06 | |
| 39 | 0.27 | 0.44 | 1300 | 2.20⋅103 | 0.06 | |
| 40 | 2.05 | 0.98 | 1300 | 290.67 | 0.44 | |
| 41 | Right coronary I | 2.42 | 1.63 | 1300 | — | 0.79 |
| 42 | 0.81 | 1.27 | 1300 | — | 0.25 | |
| 43 | 1.86 | 0.78 | 1300 | — | 0.12 | |
| 44 | 0.75 | 0.45 | 1300 | 2.20⋅103 | 0.06 | |
| 45 | 0.62 | 0.44 | 1300 | 2.20⋅103 | 0.06 | |
| 46 | 2.95 | 0.8 | 1300 | — | 0.13 | |
| 47 | 0.47 | 0.46 | 1300 | 1.68⋅103 | 0.08 | |
| 48 | 0.76 | 0.46 | 1300 | 2.20⋅103 | 0.06 | |
| 49 | Right coronary II | 4.53 | 1.29 | 1300 | — | 0.54 |
| 50 | Right marginal branch | 1.84 | 0.99 | 1300 | — | 0.22 |
| 51 | 1.34 | 0.54 | 1300 | 1.14⋅103 | 0.11 | |
| 52 | 2.34 | 0.76 | 1300 | — | 0.11 | |
| 53 | 3.17 | 0.36 | 1300 | 2.20⋅103 | 0.05 | |
| 54 | 1.05 | 0.27 | 1300 | 2.20⋅103 | 0.05 | |
| 55 | Right coronary III | 4.6 | 0.93 | 1300 | — | 0.32 |
| 56 | Posterior descending | 3.37 | 0.7 | 1300 | — | 0.11 |
| 57 | 2.34 | 0.3 | 1300 | 2.20⋅103 | 0.05 | |
| 58 | 1.88 | 0.34 | 1300 | 2.20⋅103 | 0.05 | |
| 59 | 2.42 | 0.75 | 1300 | — | 0.21 | |
| 60 | 3.14 | 0.44 | 1300 | 2.20⋅103 | 0.06 | |
| 61 | 0.66 | 0.67 | 1300 | — | 0.15 | |
| 62 | 1.47 | 0.45 | 1300 | 2.20⋅103 | 0.06 | |
| 63 | 0.87 | 0.58 | 1300 | — | 0.10 | |
| 64 | 2.75 | 0.3 | 1300 | 2.20⋅103 | 0.05 | |
| 65 | 1.23 | 0.21 | 1300 | 2.20⋅103 | 0.05 |
Parameters of the Windkessel compartments (compartments numbering coincides with the terminal arteries numbering in Fig. 2).
| No. | Artery title | R1 (kBa⋅s/ml) | R2 (kBa⋅s/ml) | C (ml/kBa) |
|---|---|---|---|---|
| 8 | Radial right | 16.96 | 67.85 | 4.91⋅10−3 |
| 10b | Posterior interosseous right | 70.28 | 281.14 | 1.18⋅10−3 |
| 11 | Ulnar right II | 17.27 | 69.08 | 4.82⋅10−3 |
| 12 | External carotid right | 13.8 | 55.22 | 6.03⋅10−3 |
| 16 | External carotid left | 13.85 | 55.41 | 6.01⋅10−3 |
| 22 | Radial left | 16.66 | 66.63 | 5.00⋅10−3 |
| 24b | Posterior interosseous left | 70.54 | 282.16 | 1.18⋅10−3 |
| 25 | Ulnar left II | 17.6 | 70.42 | 4.73⋅10−3 |
| 26 | Posterior intercostal T6 right | 366.22 | 1.46⋅103 | 2.27⋅10−4 |
| 28 | Posterior intercostal T6 left | 375.71 | 1.50⋅103 | 2.22⋅10−4 |
| 30 | Posterior intercostal T7 right | 341.68 | 1.37⋅103 | 2.44⋅10−4 |
| 32 | Posterior intercostal T7 left | 344.61 | 1.38⋅103 | 2.42⋅10−4 |
| 36 | Common hepatic | 4.92 | 19.69 | 1.69⋅10−2 |
| 38 | Left gastric | 504.79 | 2.02⋅103 | 1.65⋅10−4 |
| 39 | Splenic II | 6.96 | 27.83 | 1.20⋅10−2 |
| 40 | Superior mesenteric | 3.21 | 12.83 | 2.60⋅10−2 |
| 42 | Renal left | 3.33 | 13.3 | 2.50⋅10−2 |
| 44 | Renal right | 3.34 | 13.35 | 2.49⋅10−2 |
| 46 | Inferior mesenteric | 35.15 | 140.61 | 2.37⋅10−3 |
| 51 | Internal iliac right | 6.09 | 24.38 | 1.37⋅10−2 |
| 52 | Profunda femoris right | 5.04 | 20.15 | 1.65⋅10−2 |
| 54 | Anterior tibial right | 36.05 | 144.21 | 2.31⋅10−3 |
| 55c | Posterior tibial right | 31.1 | 124.4 | 2.68⋅10−3 |
| 57 | Internal iliac left | 6.11 | 24.45 | 1.36⋅10−2 |
| 58 | Profunda femoris left | 5.04 | 20.16 | 1.65⋅10−2 |
| 60 | Anterior tibial left | 36.06 | 144.25 | 2.31⋅10−3 |
| 61c | Posterior tibial left | 31.11 | 124.45 | 2.68⋅10−3 |
| 65 | Posterior cerebral right II | 21.11 | 84.44 | 3.13⋅10−3 |
| 66 | Posterior cerebral left II | 21.26 | 85.02 | 2.94⋅10−3 |
| 71 | Middle cerebral left | 6.38 | 26.37 | 9.80⋅10−3 |
| 72 | Middle cerebral right | 6.36 | 26.29 | 9.83⋅10−3 |
| 73 | Anterior cerebral right II | 16.16 | 64.62 | 4.13⋅10−3 |
| 74 | Anterior cerebral left II | 16.16 | 64.62 | 4.13⋅10−3 |
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Application of mutual information estimation for predicting the structural stability of pentapeptides
- Induced drift of scroll waves in the Aliev–Panfilov model and in an axisymmetric heart left ventricle
- Spatially averaged haemodynamic models for different parts of cardiovascular system
- Analysis of the impact of left ventricular assist devices on the systemic circulation
- A stable method for 4D CT-based CFD simulation in the right ventricle of a TGA patient
Articles in the same Issue
- Frontmatter
- Application of mutual information estimation for predicting the structural stability of pentapeptides
- Induced drift of scroll waves in the Aliev–Panfilov model and in an axisymmetric heart left ventricle
- Spatially averaged haemodynamic models for different parts of cardiovascular system
- Analysis of the impact of left ventricular assist devices on the systemic circulation
- A stable method for 4D CT-based CFD simulation in the right ventricle of a TGA patient