Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-24T04:03:52.275Z Has data issue: false hasContentIssue false
  • English
  • Français

Patient Specific Haemodynamic Modeling after OcclusionTreatment in Leg

Published online by Cambridge University Press:  31 July 2014

T. Gamilov ,
Yu. Ivanov ,
P. Kopylov ,
S. Simakov  and
Yu. Vassilevski
T. Gamilov
Affiliation:
Moscow Institute of Physics and Technology, 141700, Dolgoprudny, 9 Institutskii Lane, Russia
Yu. Ivanov
Affiliation:
Institute of Numerical Mathematics RAS, 119333, Moscow, 8 Gubkina St., Russia
P. Kopylov
Affiliation:
I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya st. 119991 Moscow, Russia
S. Simakov*
Affiliation:
Moscow Institute of Physics and Technology, 141700, Dolgoprudny, 9 Institutskii Lane, Russia
Yu. Vassilevski
Affiliation:
Moscow Institute of Physics and Technology, 141700, Dolgoprudny, 9 Institutskii Lane, Russia Institute of Numerical Mathematics RAS, 119333, Moscow, 8 Gubkina St., Russia
*
⋆Corresponding author. E-mail: [email protected]
Get access

Abstract

In this work we propose a method for analysis of postsurgical haemodynamics after femoralartery treatment of occlusive vascular disease. Patient specific reconstruction algorithmof 1D core network based on MRI data is proposed as a tool for such analysis. Along withpresurgical ultrasound data fitting it provides effective personalizing predictive methodthat is validated with clinical observations.

Keywords

haemodynamicsfemoral artery occlusionmathematical modelingpatient specific1D core network reconstruction algorithmpresurgical and postsurgical modeling
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 9 , Issue 6: Blood flows , 2014 , pp. 85 - 97
Copyright
© EDP Sciences, 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abakumov, N. V., Mukhin, S. I., Favorski, A. P., et al. Strategy of mathematical cardiovascular system modeling. Matem. Mod., 12 (2000), 106–117. Google Scholar
L. Antiga. Patient-specific modeling of geometry and blood-flow in large arteries. PhD thesis. Politecnico di Milano, Milan, 2003.
Antiga, L., Steinman, D. A.. Robust and objective decomposition and mapping of bifurcating vessels. IEEE Transactions on Medical Imaging, 23 (2004), 704–713. CrossRefGoogle ScholarPubMed
Diedrich, K. T., Roberts, J. A., Schmidt, R. H., Parker, D. L.. Comparing performance of centerline algorithms for quantitative assessment of brain vascular anatomy. Anat Rec (Hoboken), 295 (2012), 2179. CrossRefGoogle ScholarPubMed
L. Formaggia, A. Quarteroni, A. Veneziani. Cardiovascular mathematics. DE: Springer, Heidelberg, 2009.
Ganz, V., Hlavova, A., Fronek, A., Linhart, J., Prerovsky, I.. Measurement of blood flow in the femoral artery in man at rest and during exercise by local thermodilution. Circulation, 30 (1964), 86–89. CrossRefGoogle ScholarPubMed
S. Standring. Gray’s Anatomy: The Anatomical Basis of Clinical Practice, 40th ed., Elsevier, Churchill-Livingstone, 2008.
J. Liu, K. Subramanian. Accurate and robust centerline extraction from tubular structures in medical images. In: Advances in Information and Intelligent Systems, part 2, Vol. 251, Springer Berlin Heidelberg, 2009, 139-162.
Müller, L. O., Parés, C., Toro, E. F.. Well-balanced high-order numerical schemes for one-dimensional blood flow in vessels with varying mechanical properties. J. Comp. Phys., 242 (2013), 53–85. CrossRefGoogle Scholar
Mynard, J. P., Nithiarasu, P.. A 1D arterial blood flow model incorporating ventricular pressure, aortic valve and regional coronary flow using the locally conservative Galerkin (LCG) method. Comm. Num. Met. Eng., 24 (2008), 367–417. CrossRefGoogle Scholar
C. G. Caro, T. J. Pedley, R. C. Schroter, W. A. Seed. The Mechanics of the circulation. Oxford University Press, Oxford, New York, 1978.
Sala, R., Rossel, C., Encinas, P., Lahiguera, P.. Continuum of pulse wave velocity from young elite athletes to uncontrolled older patients with resistant hypertension. J. Hypertens, 28 (2010), 19.216. Google Scholar
R. F. Schmidt, G. Thews. Human Physiology, vol.2, 2nd ed., MIR, Moscow, 1996 (In Russian).
Simakov, S., Gamilov, T., Soe, Y.N.. Computational study of blood flow in lower extremities under intense physical load. Russ. J. Numer. Anal. Math. Mod., 28 (2013), 485–504. Google Scholar
Simakov, S., Kholodov, A.. Computational study of oxygen concentration in human blood under low frequency disturbances. Mat. Mod. Comp. Sim., 1 (2009), 3–295. Google Scholar
Vassilevski, Y., Simakov, S., Kapranov, S.. A multi-model approach to intravenous filter optimization. Int. J. Numer. Meth. Biomed. Eng., 26 (2010), 915–925. Google Scholar
Vassilevski, Y., Simakov, S., Dobroserdova, T.. Numerical issues of modelling blood flow in networks of vessels with pathologies. Russ. J. Numer. Anal. Math. Mod., 26 (2011), 605–622. Google Scholar
Wilkinson, I. B., Cockcroft, J. R., Webb, D. J.. Pulse wave analysis and arterial stiffness. J. Cardiovasc. Pharmacol., 32 (1998), Suppl. 3, S33–7. Google ScholarPubMed
http://www.vmtk.org
http://www.amira.com, http://www.vsg3d.com/amira/
http://www.plasticboy.co.uk/