High-Resolution Measurement of the Unsteady Velocity Field to Evaluate Blood Damage Induced by a Mechanical Heart Valve

Flow-induced hemolysis is a crucial issue for many biomedical applications; in particular, it is an essential issue for the development of blood-transporting devices such as left ventricular assist devices, and other types of blood pumps. In order to estimate red blood cell (RBC) damage in blood flows, many models have been proposed in the past. Most models have been validated by their respective authors. However, the accuracy and the validity range of these models remains unclear. In this work, the most established hemolysis models compatible with computational fluid dynamics of full-scale devices are described and assessed by comparing two selected reference experiments: a simple rheometric flow and a more complex hemodialytic flow through a needle. The quantitative comparisons show very large deviations concerning hemolysis predictions, depending on the model and model parameter. In light of the current results, two simple power-law models deliver the best compromise between computational efficiency and obtained accuracy. Finally, hemolysis has been computed in an axial blood pump. The reconstructed geometry of a HeartMate II shows that hemolysis occurs mainly at the tip and leading edge of the rotor blades, as well as at the leading edge of the diffusor vanes.

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“…An alternative method to accumulate blood damage has been derived based on a mechanical dose

D_{b}

closely related to Eq. , and computed as a function of the lysis induced in each grid cell over a pathline :

D_{b} = {\overset{τ}{true}}^{\frac{α}{β}} t

…”

Section: A‐the Power‐law Equation Modelmentioning

confidence: 99%

A Review of Hemolysis Prediction Models for Computational Fluid Dynamics

et al. 2017

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“…Based on in-vitro blood experiments with rotational viscometers, multiple power-law equations and BDIs have been suggested for modeling hemodynamic blood damage and corresponding hemoglobin loss with respect to the magnitude and exposure time of shear stress78333435. However, it has been difficult to obtain in-vivo evidence supporting these models due to the lack of methodology for measuring velocity fields and hemodynamic stresses non-invasively.…”

Section: Discussionmentioning

confidence: 99%

Assessment of turbulent viscous stress using ICOSA 4D Flow MRI for prediction of hemodynamic blood damage

Lantz

Haraldsson

et al. 2016

Sci Rep

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Flow-induced blood damage plays an important role in determining the hemodynamic impact of abnormal blood flow, but quantifying of these effects, which are dominated by shear stresses in highly fluctuating turbulent flow, has not been feasible. This study evaluated the novel application of turbulence tensor measurements using simulated 4D Flow MRI data with six-directional velocity encoding for assessing hemodynamic stresses and corresponding blood damage index (BDI) in stenotic turbulent blood flow. The results showed that 4D Flow MRI underestimates the maximum principal shear stress of laminar viscous stress (PLVS), and overestimates the maximum principal shear stress of Reynolds stress (PRSS) with increasing voxel size. PLVS and PRSS were also overestimated by about 1.2 and 4.6 times at medium signal to noise ratio (SNR) = 20. In contrast, the square sum of the turbulent viscous shear stress (TVSS), which is used for blood damage index (BDI) estimation, was not severely affected by SNR and voxel size. The square sum of TVSS and the BDI at SNR >20 were underestimated by less than 1% and 10%, respectively. In conclusion, this study demonstrated the feasibility of 4D Flow MRI based quantification of TVSS and BDI which are closely linked to blood damage.

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“…It should be noted however that for incompressible flow,

σ_{ν} = \sqrt{2} μ e_{F}

, and the two measures provide equivalent results. Some experimental studies have suggested that stress and exposure time should not be “weighted equally,” but AP is better expressed as a power law of stress and exposure time [53, 2, 55, 106], which has been applied in several computational studies [9, 1, 102, 153, 132, 133, 12, 93]. …”

Section: Tracking Cellular Damagementioning

confidence: 99%

“…PIV is another method to obtain highly resolved velocity data to calculate AP[12], and has been compared to CFD predictions of AP giving good agreement [117]. The influence of helical flow on platelet activation has been studied in a stenosed carotid bifurcation [93].…”

Section: Tracking Cellular Damagementioning

confidence: 99%

Lagrangian Postprocessing of Computational Hemodynamics

Shadden

Arzani

2014

Ann Biomed Eng

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Recent advances in imaging, modeling and computing have rapidly expanded our capabilities to model hemodynamics in the large vessels (heart, arteries and veins). This data encodes a wealth of information that is often under-utilized. Modeling (and measuring) blood flow in the large vessels typically amounts to solving for the time-varying velocity field in a region of interest. Flow in the heart and larger arteries is often complex, and velocity field data provides a starting point for investigating the hemodynamics. This data can be used to perform Lagrangian particle tracking, and other Lagrangian-based postprocessing. As described herein, Lagrangian methods are necessary to understand inherently transient hemodynamic conditions from the fluid mechanics perspective, and to properly understand the biomechanical factors that lead to acute and gradual changes of vascular function and health. The goal of the present paper is to review Lagrangian methods that have been used in post-processing velocity data of cardiovascular flows.

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High-Resolution Measurement of the Unsteady Velocity Field to Evaluate Blood Damage Induced by a Mechanical Heart Valve

Cited by 19 publications

References 39 publications

A Review of Hemolysis Prediction Models for Computational Fluid Dynamics

A Review of Hemolysis Prediction Models for Computational Fluid Dynamics

Assessment of turbulent viscous stress using ICOSA 4D Flow MRI for prediction of hemodynamic blood damage

Lagrangian Postprocessing of Computational Hemodynamics

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