Numerous studies have shown a correlation between Wall Shear Stress (WSS) and atherosclerosis, but few have evaluated the reliability of estimation methods and measures used to assessWSS, which is the subject of this work. A subject specific vessel model of the aortic arch and thoracic aorta is created fromMRI images and used for CFD simulations with MRI velocity measurements as inlet boundary condition. WSS is computed from the simulation results. Aortic WSS shows significant spatial as well as temporal variation during a cardiac cycle, which makes circumferential values very uninformative, and approximate estimates using Hagen-Poiseuille fails predict the averageWSS. Highly asymmetric flow, especially in the arch, causes the spatial WSS variations.

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.

The development of atherosclerotic plaques has been associated with the patterns of wall shear stress (WSS). However, much is still uncertain with the methods used to calculate WSS. Correct vessel geometries are mandatory to get reliable estimations and the purpose of this study was to evaluate an in vivo method for creating aortic 3D geometry in man based on data from magnetic resonance imaging (MRI) with ultrasound as reference.

Methods: The aortas of ten healthy males, 23.4 ± 1.6 years of age, were examined with MRI, and 3D geometries were created with manual segmentation of the images. Lumen diameters (LD) were measured in the abdominal aorta (AA) and the thoracic aorta (TA) with non-invasive B-mode ultrasound as a reference.

Results: The anteroposterior diameter of the AA was 13.6 ± 1.1 mm for the MRI and 13.8 ± 1.3 mm for the ultrasound (NS). Intraobserver variability (CV) for MRI and ultrasound was <0.92% and <0.40% respectively . Interobserver variability MRI and ultrasound was 0.96% and 0.56% respectively. The diameter of the TA was 19.2 ± 1.4 mm for the MRI, and the intraobserver variability (CV) were <0.78% and interobserver variability (CV) were 0.92%.

Conclusion: Specific arterial geometries can be constructed with a high degree of accuracy using MRI. This indicate that the MRI geometries may be used to create realistic and correct geometries in the calculation of WSS in the aorta of man.

Atherosclerosis development is strongly believed to be influenced by hemodynamic forces such as wall shear stress (WSS). To estimate such entity in-vivo in humans, is image based computational fluid dynamics (CFD) a powerful tool. In this paper we use a combination of magnetic resonance imaging (MRI) and CFD to estimate WSS. In such method a number of steps is included. One important step is the image interpretation into 3D models, named segmentation. The choice of segmentation method can influence the resulting WSS distribution in the human aorta. This is studied by comparingWSS results gained from the use of two different segmentation approaches: manual and semi-automatic, where the manual approach is considered to be the reference method. The investigation is performed on a group of 8 healthymale volunteers. The different segmentation methods give slightly different geometrical descriptions of the human aorta. However there is a very good agreement between the resultingWSS distribution for the two segmentation approaches. The small differences in WSS between the methods increase in the late systole and early diastolic cardiac cycle time position indicating that theWSS is more sensitive to local geometry differences in these parts of the cardiac cycle. We can conclude that the results show that the semi-automatic segmentation method can be used in the future to estimate WSS with relevant accuracy.

Wall shear stress (WSS) distribution in the human aorta is a highly interesting hemodynamic factor for atherosclerosis development. We present a magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) based subject specific WSS estimation method and demonstrate it on a group of nine healthy volunteers (males age 23.6 ± 1.3 years). In all nine subjects, the aortic blood flow was simulated in a subject specific way, where the 3D segmented geometries and inflow profiles were obtained using MRI. No parameter settings were tailored using data from the nine subjects. Validation was performed by comparing CFD gained velocity with magnetic resonance imaging (MRI) velocity measurements. CFD and MRI velocity profiles were comparable, but the temporal variations of the differences during the cardiac cycle were significant. Spatio-temporal analyzes on the WSS distribution showed a strong subject specific influence. Subject specific models are decisive to estimate WSS distribution.

Atherosclerosis is one of the main reasons for cardivascular disease which cause many deaths every year especially in the Western world. The development of atherosclerosis is strongly believed to be influenced by hemodynamic forces in the arteries e.g. wall shear stress (WSS). Estimations of WSS are therefore very important. By combining magnetic resonance imaging (MRI), image processing and computational fluid dynamic (CFD) simulations, it is possible to estimate subject specific WSS in the human arteries. The framework for performing such work includes i.e. using inlet boundary conditions which, however, will influence the final result i.e. the WSS distribution. This paper aims to investigate the influence of the inflow boundary condition in the human aorta with comparing two settings for the inflow: 1) subject specific inlet profile measured with MRI and 2) uniform profile with the subject specific mass flow rate. The analysis of WSS will be performed both on spatial location along the artery as well as on the temporal location in the cardiac cycle. Subject specific data have been used for geometry, inflow velocity profile and blood viscosity. The recommendation due to our findings from nine healthy subjects, is that a measured subject specific inlet boundary condition must be used in order to get a subject specific WSS distribution; the difference in WSS is 8-34% compared to using a mass-flow correct uniform profile. Temporal variations were clearly seen in the WSS differences due to the different inflow velocity profiles used. The lowest influence of the inlet boundary condition was found at peak velocity in the cardiac cycle. The aortic geometry does not form the flow in such extent (compared to the influence by inlet boundary condition) to obtain a more correct WSS distribution further away from the inlet at the systolic parts of the cardiac cycle. The shape of the vessel has only a significant influence at low velocities i.e. the diastolic phase of the cardiac cycle.