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The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

Abstract Computational fluid dynamics has been widely used to study hemodynamics, but accurately determining boundary conditions for turbulent blood flow remains challenging. This study aims to investigate the effect of patient-specific turbulence boundary conditions on the accuracy of turbulent flow simulation. Using a stenosis model with 50% severity in diameter, the post-stenosis turbulence flow region was simulated with different planes to obtain inlet boundary conditions and simulate downstream flows. The errors of simulated flow fields obtained with turbulence kinetic energy (TKE) boundary data and arbitrary turbulence intensity were compared. Additionally, the study tested various TKE data resolutions and noise levels to simulate experimental environments. The mean absolute error of velocity and TKE was investigated with various turbulence intensities and TKE mapping. While voxel size and signal-to-noise ratio of the TKE data affected the results, simulation with SNR > 5 and voxel size

Abstract Hemodynamics in aortic dissection (AD) is closely associated with the risk of aortic aneurysm, rupture, and malperfusion. Altered blood flow in patients with AD can lead to severe complications such as visceral malperfusion. In this study, we aimed to investigate the effect of cannulation flow on hemodynamics in AD using a fluid–structure interaction simulation. We developed a specific-idealized AD model that included an intimal tear in the descending thoracic aorta, a re-entry tear in the left iliac artery, and nine branches. Two different cannulation methods were tested: (1) axillary cannulation (AC) only through the brachiocephalic trunk and (2) combined axillary and femoral cannulation (AFC) through the brachiocephalic trunk and the right common iliac artery. AC was found to result in the development of a pressure difference between the true lumen and false lumen, owing to the difference in the flow rate through each lumen. This pressure difference collapsed the true lumen, disturbing blood flow to the celiac and superior mesenteric arteries. However, in AFC, the pressure levels between the two lumens were similar, and no collapse occurred. Moreover, the visceral flow was higher than that in AC. Lastly, the stiffness of the intimal flap affected the true lumen's collapse.

Introduction: The aim of this study was to characterize the hemodynamics of Fontan patients using both four-dimensional flow magnetic resonance imaging (4D Flow MRI) and computational fluid dynamics (CFD).Methods: Twenty-nine patients (3.5 ± 0.5 years) who had undergone the Fontan procedure were enrolled, and the superior vena cava (SVC), left pulmonary artery (LPA), right pulmonary artery (RPA), and conduit were segmented based on 4D Flow MRI images. Velocity fields from 4D Flow MRI were used as boundary conditions for CFD simulations. Hemodynamic parameters such as peak velocity (Vmax), pulmonary flow distribution (PFD), kinetic energy (KE), and viscous dissipation (VD) were estimated and compared between the two modalities.Results and discussion: The Vmax, KE, VD, PFDTotal to LPA, and PFDTotal to RPA of the Fontan circulation were 0.61 ± 0.18 m/s, 0.15 ± 0.04 mJ, 0.14 ± 0.04 mW, 41.3 ± 15.7%, and 58.7 ± 15.7% from 4D Flow MRI; and 0.42 ± 0.20 m/s, 0.12 ± 0.05 mJ, 0.59 ± 0.30 mW, 40.2 ± 16.4%, and 59.8 ± 16.4% from CFD, respectively. The overall velocity field, KE, and PFD from the SVC were in agreement between modalities. However, PFD from the conduit and VD showed a large discrepancy between 4D Flow MRI and CFD, most likely due to spatial resolution and data noise. This study highlights the necessity for careful consideration when analyzing hemodynamic data from different modalities in Fontan patients.

Although recent advances of four-dimensional (4D) flow magnetic resonance imaging (MRI) has introduced a new way to measure Reynolds stress tensor (RST) in turbulent flows, its measurement accuracy and possible bias have remained to be revealed. The purpose of this study was to compare the turbulent flow measurement of 4D flow MRI and particle image velocimetry (PIV) in terms of velocity and turbulence quantification. Two difference flow rates of 10 and 20 L/min through a 50% stenosis were measured with both PIV and 4D flow MRI. Not only velocity through the stenosis but also the turbulence parameters such as turbulence kinetic energy and turbulence production were quantitatively compared. Results shows that 4D flow MRI velocity measurement well agreed with the that of PIV, showing the linear regression slopes of two methods are 0.94 and 0.89, respectively. Although turbulence mapping of 4D flow MRI was qualitatively agreed with that of PIV, the quantitative comparison shows that the 4D flow MRI overestimates RST showing the linear regression slopes of 1.44 and 1.66, respectively. In this study, we demonstrate that the 4D flow MRI visualize and quantify not only flow velocity and also turbulence tensor. However, further optimization of 4D flow MRI for better accuracy might be remained.