Abstract

Imaging hemodynamics play an important role in the diagnosis of abnormal blood flow due to vascular and valvular diseases as well as in monitoring the recovery of normal blood flow after surgical or interventional treatment. Recently, characterization of turbulent blood flow using 4D flow magnetic resonance imaging (MRI) has been demonstrated by utilizing the changes in signal magnitude depending on intravoxel spin distribution. The imaging sequence was extended with a six-directional icosahedral (ICOSA6) flow-encoding to characterize all elements of the Reynolds stress tensor (RST) in turbulent blood flow. In the present study, we aimed to demonstrate the feasibility of full RST analysis using ICOSA6 4D flow MRI under physiological conditions. First, the turbulence analysis was performed through in vitro experiments with a physiological pulsatile flow condition. Second, a total of 12 normal subjects and one patient with severe aortic stenosis were analyzed using the same sequence. The in-vitro study showed that total turbulent kinetic energy (TKE) was less affected by the signal-to-noise ratio (SNR), however, maximum principal turbulence shear stress (MPTSS) and total turbulence production (TP) had a noise-induced bias. Smaller degree of the bias was observed for TP compared to MPTSS. In-vivo study showed that the subject-variability on turbulence quantification was relatively low for the consistent scan protocol. The in vivo demonstration of the stenosis patient showed that the turbulence analysis could clearly distinguish the difference in all turbulence parameters as they were at least an order of magnitude larger than those from the normal subjects.

Highlights

  • Imaging hemodynamics plays an important role in the diagnosis of abnormal blood flow due to vascular and valvular diseases and monitoring the recovery of normal blood flow after surgical or interventional treatment (Ragosta, 2017; Members et al, 2021)

  • This study aimed to investigate the performance of full Reynolds stress tensor (RST) analysis using ICOSA6 4D flow magnetic resonance imaging (MRI) under physiological conditions

  • The quality of turbulence quantification was dependent on the velocity encoding (Venc) parameter, which determines the signal-to-noise ratio (SNR) of the measurement (Figures 5, 6)

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Summary

Introduction

Imaging hemodynamics plays an important role in the diagnosis of abnormal blood flow due to vascular and valvular diseases and monitoring the recovery of normal blood flow after surgical or interventional treatment (Ragosta, 2017; Members et al, 2021). Non-invasive measurement of hemodynamic parameters, such as velocity, pressure loss, and perfusion, has been an important marker for the management and therapy of patients with vascular diseases (Ragosta, 2017; Members et al, 2021). Characterization of turbulent blood flow in the circulation system has received attention from researchers as it provides additional insights into the extent of spatiotemporal velocity fluctuation and the corresponding stress and energy. The development of turbulent flow dissipates kinetic energy into internal energy by viscous shear stress, which elevates the energy and pressure loss (Pope, 2001). As the mechanical stimuli of turbulent flow are detected and transduced into endothelial cells, the pathophysiology of turbulence on the progression of atherosclerosis and vascular remodeling has been investigated (Davies et al, 1986; Davies, 1989; Prado et al, 2006)

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