Enhancing Hemodynamic Parameter Estimations: Nonlinear Blood Behavior in 4D Flow MRI

TL;DR

This study demonstrates that accounting for blood's shear-thinning non-Newtonian behavior significantly affects the accuracy of hemodynamic parameter estimations in 4D Flow MRI, with implications for cardiovascular disease assessment.

Contribution

It introduces a hematocrit-dependent power-law non-Newtonian model for blood rheology, improving the accuracy of WSS, energy loss, and OSI estimations over traditional Newtonian assumptions.

Findings

Differences in WSS and energy loss can reach over 190% in simulations.

Blood rheology significantly influences hemodynamic parameter estimates.

In-vivo data show up to 73% variation in WSS due to rheology.

Abstract

Hemodynamic parameters are often estimated assuming a constant Newtonian viscosity, even though blood exhibits shear-thinning behavior. This article investigates the influence of blood rheology and hematocrit (Hct) percentage on the estimation of Wall Shear Stress (WSS), rate of viscous Energy Loss ($\dot{E}_L$) at different points in the cardiac cycle, and the Oscillatory Shear Index (OSI). We focus on a hematocrit-dependent power-law non-Newtonian model, considering a wide range of Hct values at physiological temperature, with rheological parameters obtained from previously reported experimental data. In all cases, we systematically compared WSS, $\dot{E}_L$, and OSI using both Newtonian and power-law models, underscoring the crucial role of blood rheology in accurately assessing cardiovascular diseases. Our results show that, in in-silico experiments, differences in WSS and $\dot{E}_L$ across a wide range of Hct values can reach as high as 190\% and 113\% at systole, and as low as -72\% and -74\% at diastole, respectively. In in-vivo data, differences in WSS and $\dot{E}_L$ can reach up to -45\% and -60\% at systole, and range from -69\% to 73\% at diastole. This study enhances our understanding of the impact of blood rheology on hemodynamic parameter estimations using both in-silico and in-vivo aortic 4D Flow MRI data.