Personalized 0D models of normal and stenosed carotid arteries

BACKGROUND AND OBJECTIVE Recent advances in medical imaging like MRI, CT-Scan, Doppler ultrasound, etc. have made it possible to study the hemodynamics of cardiovascular system having different levels of vessel abnormalities. METHODS Within this work, we have developed two different personalized lumped-parameter models of the human carotid arteries having elastic and viscoelastic vessel wall behaviors. The data used in developing the models of the carotid arteries is taken from a healthy subject and a patient having mild carotid stenosis (55%) near a bifurcation using doppler ultrasound. The data consists measurements of blood flow velocities and geometrical parameters at selected locations. Prior to the measurements, the key measurable geometrical parameters are identified by normalized local sensitivity analysis. RESULTS Finally, both developed and personalized models of carotid arteries are validated against the blood flow measurements obtained near carotid bifurcation. We observe a good agreement between model simulations and blood flow measurements taken near the bifurcation i.e. (r=0.94) for the healthy subject and (r=0.96) for the patient having a stenosis near the bifurcation. CONCLUSIONS This work provides further evidence, that the hemodynamics near a bifurcation can be modelled well with a 0D approach, even with different levels of stenosis.

[1]  G. Fragomeni,et al.  Mathematical Model of Blood Flow in Carotid Bifurcation , 2009 .

[2]  Christof Schütte,et al.  Simulation, identification and statistical variation in cardiovascular analysis (SISCA) - A software framework for multi-compartment lumped modeling , 2017, Comput. Biol. Medicine.

[3]  R. Gul,et al.  Beat-to-beat sensitivity analysis of human systemic circulation coupled with the left ventricle model of the heart: A simulation-based study , 2019, The European Physical Journal Plus.

[4]  A. Shahzad,et al.  Personalized mathematical model of human arm arteries with inflow boundary condition , 2020 .

[5]  A. Shahzad,et al.  Application of 0D model of blood flow to study vessel abnormalities in the human systemic circulation: An in-silico study , 2018, International Journal of Biomathematics.

[6]  A. Shahzad,et al.  Early detection of carotid stenosis using sensitivity analysis and parameter estimation , 2021, The European Physical Journal Plus.

[7]  Y. Vassilevski,et al.  Mathematical modelling of atherosclerosis , 2019, Mathematical Modelling of Natural Phenomena.

[8]  J-F Gerbeau,et al.  A methodological paradigm for patient‐specific multi‐scale CFD simulations: from clinical measurements to parameter estimates for individual analysis , 2014, International journal for numerical methods in biomedical engineering.

[9]  Wouter Huberts,et al.  What is needed to make cardiovascular models suitable for clinical decision support? A viewpoint paper , 2017, J. Comput. Sci..

[10]  Alfio Quarteroni,et al.  Geometric multiscale modeling of the cardiovascular system, between theory and practice , 2016 .

[11]  F. Auricchio,et al.  Carotid artery stenting simulation: from patient-specific images to finite element analysis. , 2011, Medical engineering & physics.

[12]  Simone Manini,et al.  Patient-Specific Model of Arterial Circulation for Surgical Planning of Vascular Access , 2013, The journal of vascular access.

[13]  Xueling Fan,et al.  A patient-specific lumped-parameter model of coronary circulation , 2018, Scientific Reports.

[14]  Alfio Quarteroni,et al.  Multiscale modelling of the circulatory system: a preliminary analysis , 1999 .

[15]  S. Bernhard,et al.  Parametric uncertainty and global sensitivity analysis in a model of the carotid bifurcation: Identification and ranking of most sensitive model parameters. , 2015, Mathematical biosciences.

[16]  Irina-Andra Tache,et al.  Patient specific modeling of the cardiovascular system , 2013, 2nd International Conference on Systems and Computer Science.

[17]  Charles A. Taylor,et al.  Patient-specific modeling of cardiovascular mechanics. , 2009, Annual review of biomedical engineering.

[18]  P. Pathmanathan,et al.  Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges , 2018, Journal of Cardiovascular Translational Research.

[19]  R. Gul,et al.  Mathematical modeling and sensitivity analysis of arterial anastomosis in the arm , 2016 .

[20]  Charles A. Taylor,et al.  Comparative study of viscoelastic arterial wall models in nonlinear one-dimensional finite element simulations of blood flow. , 2011, Journal of biomechanical engineering.

[21]  Jordi Alastruey,et al.  Reducing the number of parameters in 1D arterial blood flow modeling: less is more for patient-specific simulations , 2015, American journal of physiology. Heart and circulatory physiology.

[22]  Vincent C. Rideout,et al.  Mathematical and Computer Modeling of Physiological Systems , 1991 .