Analysis of thoracic aorta hemodynamics using 3D particle tracking velocimetry and computational fluid dynamics.

Parallel to the massive use of image-based computational hemodynamics to study the complex flow establishing in the human aorta, the need for suitable experimental techniques and ad hoc cases for the validation and benchmarking of numerical codes has grown more and more. Here we present a study where the 3D pulsatile flow in an anatomically realistic phantom of human ascending aorta is investigated both experimentally and computationally. The experimental study uses 3D particle tracking velocimetry (PTV) to characterize the flow field in vitro, while finite volume method is applied to numerically solve the governing equations of motion in the same domain, under the same conditions. Our findings show that there is an excellent agreement between computational and measured flow fields during the forward flow phase, while the agreement is poorer during the reverse flow phase. In conclusion, here we demonstrate that 3D PTV is very suitable for a detailed study of complex unsteady flows as in aorta and for validating computational models of aortic hemodynamics. In a future step, it will be possible to take advantage from the ability of 3D PTV to evaluate velocity fluctuations and, for this reason, to gain further knowledge on the process of transition to turbulence occurring in the thoracic aorta.

[1]  Yiannis Ventikos,et al.  CFD and PTV steady flow investigation in an anatomically accurate abdominal aortic aneurysm. , 2009, Journal of biomechanical engineering.

[2]  Prahlad G. Menon,et al.  Effects of intraluminal thrombus on patient-specific abdominal aortic aneurysm hemodynamics via stereoscopic particle image velocity and computational fluid dynamics modeling. , 2014, Journal of biomechanical engineering.

[3]  Jan Engvall,et al.  Numerical and experimental assessment of turbulent kinetic energy in an aortic coarctation. , 2013, Journal of biomechanics.

[4]  D. O’Regan,et al.  A numerical study of aortic flow stability and comparison with in vivo flow measurements. , 2013, Journal of biomechanical engineering.

[5]  An-Shik Yang,et al.  Investigation of Pulsatile Flowfield in Healthy Thoracic Aorta Models , 2010, Annals of Biomedical Engineering.

[6]  M. Debakey,et al.  Patterns of Atherosclerosis and their Surgical Significance , 1985, Annals of surgery.

[7]  Daniel Edelhoff,et al.  High-resolution MRI velocimetry compared with numerical simulations. , 2013, Journal of magnetic resonance.

[8]  C. Kähler,et al.  On the uncertainty of digital PIV and PTV near walls , 2012 .

[9]  P. Dyverfeldt,et al.  Cardiothoracic Magnetic Resonance Flow Imaging , 2013, Journal of thoracic imaging.

[10]  D. Gallo,et al.  Inflow boundary conditions for image-based computational hemodynamics: impact of idealized versus measured velocity profiles in the human aorta. , 2013, Journal of biomechanics.

[11]  Markus Holzner,et al.  Experimental study of aortic flow in the ascending aorta via Particle Tracking Velocimetry , 2012, Experiments in Fluids.

[12]  Michele Guala,et al.  3D scanning particle tracking velocimetry , 2005 .

[13]  D. Gallo,et al.  A Survey of Quantitative Descriptors of Arterial Flows , 2014 .

[14]  D. Steinman,et al.  Mind the Gap: Impact of Computational Fluid Dynamics Solution Strategy on Prediction of Intracranial Aneurysm Hemodynamics and Rupture Status Indicators , 2014, American Journal of Neuroradiology.

[15]  A. Cenedese,et al.  A laboratory investigation of the flow in the left ventricle of a human heart with prosthetic, tilting-disk valves , 2005 .

[16]  Thomas A Hope,et al.  Imaging of the thoracic aorta with time-resolved three-dimensional phase-contrast MRI: a review. , 2008, Seminars in thoracic and cardiovascular surgery.

[17]  Philipp Beerbaum,et al.  Accuracy vs. computational time: translating aortic simulations to the clinic. , 2012, Journal of biomechanics.

[18]  Charles A. Taylor,et al.  In vitro validation of finite-element model of AAA hemodynamics incorporating realistic outlet boundary conditions. , 2011, Journal of biomechanical engineering.

[19]  Armin Gruen,et al.  Particle tracking velocimetry in three-dimensional flows , 1993, Experiments in Fluids.

[20]  N. Malik,et al.  Particle tracking velocimetry in three-dimensional flows , 1993 .

[21]  C. Medaglia,et al.  A Numerical Study , 2005 .

[22]  Yubo Fan,et al.  Effect of non-Newtonian and pulsatile blood flow on mass transport in the human aorta. , 2011, Journal of Biomechanics.

[23]  D. Gallo,et al.  On the Use of In Vivo Measured Flow Rates as Boundary Conditions for Image-Based Hemodynamic Models of the Human Aorta: Implications for Indicators of Abnormal Flow , 2012, Annals of Biomedical Engineering.

[24]  P. Turski,et al.  Four-dimensional phase contrast magnetic resonance angiography: potential clinical applications. , 2011, European journal of radiology.

[25]  David A. Steinman,et al.  An image-based modeling framework for patient-specific computational hemodynamics , 2008, Medical & Biological Engineering & Computing.

[26]  Santiago Laín,et al.  A Review on Computational Fluid Dynamics Modelling in Human Thoracic Aorta , 2013 .

[27]  I. Marshall,et al.  MRI and CFD studies of pulsatile flow in healthy and stenosed carotid bifurcation models. , 2004, Journal of biomechanics.

[28]  Jonas Lantz,et al.  Large eddy simulation of LDL surface concentration in a subject specific human aorta. , 2012, Journal of biomechanics.

[29]  Steven H Frankel,et al.  Modeling transition to turbulence in eccentric stenotic flows. , 2008, Journal of biomechanical engineering.

[30]  Wolfgang Kinzelbach,et al.  Lagrangian measurement of vorticity dynamics in turbulent flow , 2005, Journal of Fluid Mechanics.

[31]  L. Antiga,et al.  Quantitative Analysis of Bulk Flow in Image-Based Hemodynamic Models of the Carotid Bifurcation: The Influence of Outflow Conditions as Test Case , 2010, Annals of Biomedical Engineering.

[32]  M. Grigioni,et al.  Investigation of the flow field downstream of an artificial heart valve by means of PIV and PTV , 2004 .

[33]  T. Dracos,et al.  Particle Tracking Velocimetry (PTV) , 1996 .

[34]  D. Vorp,et al.  3D reconstruction and manufacture of real abdominal aortic aneurysms: from CT scan to silicone model. , 2008, Journal of biomechanical engineering.

[35]  M. Cadioli,et al.  Mechanistic insight into the physiological relevance of helical blood flow in the human aorta: an in vivo study , 2011, Biomechanics and modeling in mechanobiology.

[36]  Markus Holzner,et al.  A Lagrangian investigation of the small-scale features of turbulent entrainment through particle tracking and direct numerical simulation , 2008, Journal of Fluid Mechanics.

[37]  Guang-Zhong Yang,et al.  Helical and Retrograde Secondary Flow Patterns in the Aortic Arch Studied by Three‐Directional Magnetic Resonance Velocity Mapping , 1993, Circulation.

[38]  L. Antiga,et al.  On the importance of blood rheology for bulk flow in hemodynamic models of the carotid bifurcation. , 2011, Journal of biomechanics.

[39]  S. Kozerke,et al.  Mapping mean and fluctuating velocities by Bayesian multipoint MR velocity encoding‐validation against 3D particle tracking velocimetry , 2014, Magnetic resonance in medicine.

[40]  Hélène A. Simon,et al.  Vorticity dynamics of a bileaflet mechanical heart valve in an axisymmetric aorta , 2007 .

[41]  Ajit P. Yoganathan,et al.  Experimental Investigation of the Steady Flow Downstream of the St. Jude Bileaflet Heart Valve: A Comparison Between Laser Doppler Velocimetry and Particle Image Velocimetry Techniques , 2004, Annals of Biomedical Engineering.