Haemodynamic disorders are common clinical findings in hypertension and lead to adverse cardiovascular events. However, the haemodynamic conditions in hypertension models are poorly understood. This study aimed to observe the characteristics of haemodynamics in spontaneously hypertensive rats (SHRs) and antihypertensive-treated SHRs. Twenty-four adult male SHRs and Wistar-Kyoto rats (WKYs) were randomly divided into four groups and treated for 7 days as follows: WKY-CON (WKYs + saline), WKY-NIF (WKYs + nifedipine, 50 mg/kg/day), SHR-CON (SHRs + saline), and SHR-NIF (SHRs + nifedipine). Aortic computational fluid dynamics (CFD) models were simulated to obtain the haemodynamic parameters. We found that in the hypertensive (SHR-CON) and blood pressure-controlled (SHR-NIF) groups, the oscillatory shear index (OSI) and relative residence time (RRT), which are key haemodynamics indices, were markedly elevated. Furthermore, there was a correlation between both the elevated OSI and RRT with the vascular wall thickening in regions near the inner wall of the aortic arch. Our research demonstrates that haemodynamics remains disturbed even if the blood pressure is normalized. In addition, vascular remodelling may play an important role in maintaining elevated OSI and RRT values.
Haemodynamic disorders are common clinical findings in hypertension and lead to adverse cardiovascular events. However, the haemodynamic conditions in hypertension models are poorly understood. This study aimed to observe the characteristics of haemodynamics in spontaneously hypertensive rats (SHRs) and antihypertensive-treated SHRs. Twenty-four adult male SHRs and Wistar-Kyoto rats (WKYs) were randomly divided into four groups and treated for 7 days as follows: WKY-CON (WKYs + saline), WKY-NIF (WKYs + nifedipine, 50 mg/kg/day), SHR-CON (SHRs + saline), and SHR-NIF (SHRs + nifedipine). Aortic computational fluid dynamics (CFD) models were simulated to obtain the haemodynamic parameters. We found that in the hypertensive (SHR-CON) and blood pressure-controlled (SHR-NIF) groups, the oscillatory shear index (OSI) and relative residence time (RRT), which are key haemodynamics indices, were markedly elevated. Furthermore, there was a correlation between both the elevated OSI and RRT with the vascular wall thickening in regions near the inner wall of the aortic arch. Our research demonstrates that haemodynamics remains disturbed even if the blood pressure is normalized. In addition, vascular remodelling may play an important role in maintaining elevated OSI and RRT values.
“…Three-dimensional (3D) phase-contrast magnetic resonance imaging (MRI) with velocity encoding in the three principal directions permits the estimation of WSS in the carotid bifurcation in vivo [ 6 ]. Advances in algorithms to estimate WSS from phase-contrast MRI data now allow the acquisition of full 3D WSS profiles along the entire bifurcation [ 7 – 10 ]. Recently, a method was introduced to create 3D maps that represent the average WSS of a certain cohort [ 11 ].…”
BackgroundTo explore the possibility of creating three-dimensional (3D) estimation models for patient-specific wall thickness (WT) maps using patient-specific and cohort-averaged WT, wall shear stress (WSS), and vessel diameter maps in asymptomatic atherosclerotic carotid bifurcations.MethodsTwenty subjects (aged 75 ± 6 years [mean ± standard deviation], eight women) underwent a 1.5-T MRI examination. Non-gated 3D phase-contrast gradient-echo images and proton density-weighted echo-planar images were retrospectively assessed for WSS, diameter estimation, and WT measurements. Spearman’s ρ and scatter plots were used to determine correlations between individual WT, WSS, and diameter maps. A bootstrapping technique was used to determine correlations between 3D cohort-averaged WT, WSS, and diameter maps. Linear regression between the cohort-averaged WT, WSS, and diameter maps was used to predict individual 3D WT.ResultsSpearman’s ρ averaged over the subjects was − 0.24 ± 0.18 (p < 0.001) and 0.07 ± 0.28 (p = 0.413) for WT versus WSS and for WT versus diameter relations, respectively. Cohort-averaged ρ, averaged over 1000 bootstraps, was − 0.56 (95% confidence interval [− 0.74,− 0.38]) for WT versus WSS and 0.23 (95% confidence interval [− 0.06, 0.52]) for WT versus diameter. Scatter plots did not reveal relationships between individual WT and WSS or between WT and diameter data. Linear relationships between these parameters became apparent after averaging over the cohort. Spearman’s ρ between the original and predicted WT maps was 0.21 ± 0.22 (p < 0.001).ConclusionsWith a combination of bootstrapping and cohort-averaging methods, 3D WT maps can be predicted from the individual 3D WSS and diameter maps. The methodology may help to elucidate pathological processes involving WSS in carotid atherosclerosis.Electronic supplementary materialThe online version of this article (10.1186/s41747-018-0058-1) contains supplementary material, which is available to authorized users.
“…Cibis et al recently found an inverse relationship between wall thickness and time-averaged WSS in patients with asymptomatic plaque, calculated with the algorithm described in Potters et al in the carotid bifurcation (Fig. 8 b) [ 136 ]. Fig.…”
The past decade has witnessed the rapid development of new MRI technology for vessel wall imaging. Today, with advances in MRI hardware and pulse sequences, quantitative MRI of the vessel wall represents a real alternative to conventional qualitative imaging, which is hindered by significant intra- and inter-observer variability. Quantitative MRI can measure several important morphological and functional characteristics of the vessel wall. This review provides a detailed introduction to novel quantitative MRI methods for measuring vessel wall dimensions, plaque composition and permeability, endothelial shear stress and wall stiffness. Together, these methods show the versatility of non-invasive quantitative MRI for probing vascular disease at several stages. These quantitative MRI biomarkers can play an important role in the context of both treatment response monitoring and risk prediction. Given the rapid developments in scan acceleration techniques and novel image reconstruction, we foresee the possibility of integrating the acquisition of multiple quantitative vessel wall parameters within a single scan session.
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