Turbulent flow, characterized by velocity fluctuations, accompanies many forms of cardiovascular disease and may contribute to their progression and hemodynamic consequences. Several studies have investigated the effects of turbulence on the magnetic resonance imaging (MRI) signal. Quantitative MRI turbulence measurements have recently been shown to have great potential for application both in human cardiovascular flow and in engineering flow. In this article, potential pitfalls and sources of error in MRI turbulence measurements are theoretically and numerically investigated. Data acquisition strategies suitable for turbulence quantification are outlined. The results show that the sensitivity of MRI turbulence measurements to intravoxel mean velocity variations is negligible, but that noise may degrade the estimates if the turbulence encoding parameter is set improperly. Different approaches for utilizing a given amount of scan time were shown to influence the dynamic range and the uncertainty in the turbulence estimates due to noise. The findings reported in this work may be valuable for both in vitro and in vivo studies employing MRI methods for turbulence quantification.

Quantifying turbulent wall shear stress in a subject specific human aorta using large eddy simulation, 2012, Medical Engineering and Physics, (34), 8, 1139-1148

Phase contrast MRI is a powerful tool for the assessment of blood flow. However, especially in the highly complex and turbulent flow that accompanies many cardiovascular diseases, phase contrast MRI may suffer from artifacts. Simulation of phase contrast MRI of turbulent flow could increase our understanding of phase contrast MRI artifacts in turbulent flows and facilitate the development of phase contrast MRI methods for the assessment of turbulent blood flow. We present a method for the simulation of phase contrast MRI measurements of turbulent flow. The method uses an EulerianLagrangian approach, in which spin particle trajectories are computed from time-resolved large eddy simulations. The Bloch equations are solved for each spin for a frame of reference moving along the spins trajectory. The method was validated by comparison with phase contrast MRI measurements of velocity and intravoxel velocity standard deviation (IVSD) on a flow phantom consisting of a straight rigid pipe with a stenosis. Turbulence related artifacts, such as signal drop and ghosting, could be recognized in the measurements as well as in the simulations. The velocity and the IVSD obtained from the magnitude of the phase contrast MRI simulations agreed well with the measurements. Magn Reson Med 64:1039-1046,

Large eddy simulation was applied for flow of Re=2000 in a stenosed pipe in order to undertake a thorough investigation of the wall shear stress (WSS) in turbulent flow. A decomposition of the WSS into time averaged and fluctuating components is proposed. It was concluded that a scale resolving technique is required to completely describe the WSS pattern in a subject specific vessel model, since the poststenotic region was dominated by large axial and circumferential fluctuations. Three poststenotic regions of different WSS characteristics were identified. The recirculation zone was subject to a time averaged WSS in the retrograde direction and large fluctuations. After reattachment there was an antegrade shear and smaller fluctuations than in the recirculation zone. At the reattachment the fluctuations were the largest, but no direction dominated over time. Due to symmetry the circumferential time average was always zero. Thus, in a blood vessel, the axial fluctuations would affect endothelial cells in a stretched state, whereas the circumferential fluctuations would act in a relaxed direction.

Abstract. Patient specific modelling of the blood flow through the human aorta is performed using computational fluid dynamics (CFD) and magnetic resonance imaging (MRI). Velocity patterns are compared between computer simulations and measurements. The workflow includes several steps: MRI measurement to obtain both geometry and velocity, an automatic levelset segmentation followed by meshing of the geometrical model and CFD setup to perform the simulations follwed by the actual simulations. The computational results agree well with the measured data.