Biplane Angiography for Experimental Validation of Computational Fluid Dynamic Models of Blood Flow in Artificial Lungs

Abstract

This article presents an investigation into the validation of velocity fields obtained from computational fluid dynamic (CFD) models of flow through the membrane oxygenators using x-ray digital subtraction angiography (DSA). Computational fluid dynamic is a useful tool in characterizing artificial lung devices, but numerical results must be experimentally validated. We used DSA to visualize flow through a membrane oxygenator at 2 L/min using 37% glycerin at 22°C. A Siemens Artis Zee system acquired biplane x-ray images at 7.5 frames per second, after infusion of an iodinated contrast agent at a rate of 33 ml/s. A maximum cross-correlation (MCC) method was used to track the contrast perfusion through the fiber bundle. For the CFD simulations, the fiber bundle was treated as a single momentum sink according to the Ergun equation. Blood was modeled as a Newtonian fluid, with constant viscosity (3.3 cP) and density (1050 kg/m3). Although CFD results and experimental pressure measurements were in general agreement, the simulated 2 L/min perfusion did not reproduce the flow behavior seen in vitro. Simulated velocities in the fiber bundle were on average 42% lower than experimental values. These results indicate that it is insufficient to use only pressure measurements for validation of the flow field because pressure-validated CFD results can still significantly miscalculate the physical velocity field. We have shown that a clinical x-ray modality, together with a MCC tracking algorithm, can provide a nondestructive technique for acquiring experimental data useful for validation of the velocity field inside membrane oxygenators.

Computed tomography angiography as an adjunct to computational fluid dynamics for prediction of oxygenator thrombus formation

“…Previous imaging analysis of hollow-fiber membrane oxygenators has been performed utilizing biplane angiography. 6 These findings demonstrated the ability to measure flow characteristics within an oxygenator, and found that CFD computed velocity was an underestimate of velocity measured from biplane angiography. Unfortunately, due to the frame rate utilized in our CTA-based technique, velocity could not be directly measured in this study, and we did not include velocity as a covariate of interest.…”

“…To reduce the computational time, the inlet, outlet, and two fiber bundle sections (oxygenation and heat exchange) were modeled with hexahedral elements, and the remaining portion was meshed with tetrahedral cells. Simulation was stopped after it met the following convergence criteria: (1) the residuals of the continuity, momentum, and turbulence equations were below 10 −6 ; (2) the difference between the flow rates at the inlet and outlet was less than 5%; and (3) the pressure at the inlet had reached a constant value. For a better comparison with the CTA experimental results, blood was treated as an incompressible Newtonian fluid with a viscosity of 3.5 MPa s and a density of 1050 kg/m 3 .…”

“…To overcome this technical challenge, X-ray imaging can be utilized, and biplane angiography has been used to describe velocity fields previously. 6 In this study, we describe the application of computed tomography angiography (CTA) techniques to a commonly used oxygenator to measure flow parameters. We correlate the measured flow parameters, as well as CFD modeled flow parameters, to actual clot burden as determined through sectioning and inspection of clinically used oxygenators.…”

Percutaneous double lumen cannula for right ventricle assist device system: A computational fluid dynamics study

“…For drainage lumen, the mesh size variation was from 69K (coarsest) to 2.7M cells (finest). The error between the coarser meshes ( f *) and the finest mesh ( f ) was quantified by the Euclidian norm as [17]: $${\Vert e\Vert }_2=\sqrt{\frac{1}{n}true\sum _{i=1}^n{\left(f_i-f_i^{\ast }\right)}^2}$$ where f is the theoretical exact solution, n is the number of grid cells and f * denotes the grid functions for the coarser grid. This f * ≡ ( p *, u *) T is described by dimensionless pressure drop ( p *) and maximum velocity ( u *) according to: $$p\ast =\frac{p}{\frac{1}{2}\rho U_{\mathit{inlet}}^2}$$ and $$u\ast =\frac{u}{U_{\mathrm{italicinlet}}}$$where $\frac{1}{2}\rho U^2$ is the dynamic pressure and U inlet is the inlet velocity.…”

Numerical investigation of membrane oxygenation using sub-channel analysis

“…Such an exchange is in turn governed by parameters such as fiber bundle porosity (« ), bundle size (L/W), inlet blood flow rate and constitutive relation for the flowing fluid. Several researchers have performed various experimental and numerical investigations (Chan et al, 2006;Dierickx et al, 2001;Evren, 2000;Hormes, et al, 2011;Hewitt et al, 1998;Jones et al, 2013aJones et al, , 2013bMazaheri and Ahmadi, 2006;Mockros and Leonard, 1985;Vaslef et al, 1994;Wickramasinghe et al, 2002;Zierenberg et al, 2008;Zhang et al, 2006Zhang et al, , 2009Zhang et al, , 2013 to analyze the blood flow and oxygenation in an artificial lung. Mockros and Leonard (1985) have performed both in vitro experiments and numerical simulations to study the cross flow in a compact membrane oxygenator.…”