The cardiovascular system is an important part of the human body since it transports both energy and oxygen to all cells throughout the body. Diseases in this system are often dangerous and cardiovascular diseases are the number one killer in the western world. Common cardiovascular diseases are heart attack and stroke, which origins from obstructed blood flow. It is generally important to understand the causes for these cardiovascular diseases. The main causes for these diseases are atherosclerosis development in the arteries (hardening and abnormal growth). This transform of the arterial wall is believed to be influenced by the mechanical load from the flowing blood on the artery and especially the tangential force the wall shear stress. To retrieve wall shear stress information in arteries invivo is highly interesting due to the coupling to atherosclerosis and indeed a challenge. The goal of this thesis is to develop, describe and evaluate an in-vivo method for subject specific wall shear stress estimations in the human aorta, the largest artery in the human body. The method uses an image based computational fluid dynamics approach in order to estimate the wall shear stress. To retrieve in-vivo geometrical descriptions of the aorta magnetic resonance imaging capabilities is used which creates image material describing the subject specific geometry of the aorta. Magnetic resonance imaging is also used to retrieve subject specific blood velocity information in the aorta. Both aortic geometry and velocity is gained at the same time. Thereafter the image material is interpreted using level-set segmentation in order to get a three-dimensional description of the aorta. Computational fluid dynamics simulations is applied on the subject specific aorta in order to calculate time resolved wall shear stress distribution at the entire aortic wall included in the actual model.

This work shows that it is possible to estimate subject specific wall shear stress in the human aorta. The results from a group of healthy volunteers revealed that the arterial geometry is very subject specific and the different wall shear stress distributions have general similarities but the level and local distribution are clearly different. Sensitivity (on wall shear stress) to image modality, the different segmentation methods and different inlet velocity profiles have been tested, which resulted in these general conclusions:

• The aortic diameter from magnetic resonance imaging became similar to the reference diameter measurement method.
• The fast semi-automatic level-set segmentation method gave similar geometry and wall shear stress results when compared to a reference segmentation method.
• Wall shear stress distribution became different when comparing a simplified uniform velocity profile inlet boundary condition with a measured velocity profile.

The method proposed in this thesis has the possibility to produce subject specific wall shear stress distribution in the human aorta. The method can be used for further medical research regarding atherosclerosis development and has the possibility for usage in clinical work.

The genesis of atherosclerosis has previously been shown to be affected by the frictional load from the blood on the vessel wall, called the wall shear stress (WSS). Assessment of WSS can therefore provide important information for diagnoses, intervention planning, and follow‐up. Calculation of WSS requires high‐resolved velocity data from the vessel, which in turn can be obtained using computational fluid dynamics (CFD). In this work large eddy simulation LES was successfully used to simulate transitional flow in idealized as well as subject specific vessel models. It was shown that a scale resolving technique is to prefer for this application, since much valuable information otherwise is lost. Besides, Reynolds‐Averaged Navier‐Stokes (RANS) models have generally failed to predict this type of flow.

Non‐pulsating flows of Reynolds numbers up to 2 000 in a circular constricted pipe showed that turbulence is likely to occur in the post‐stenotic region, which resulted in a complex WSS pattern characterized by large spatial as well temporal fluctuations in all directions along the wall. Time averaged streamwise WSS was relatively high, while time averaged circumferential WSS was low, meaning that endothelial cells in that region would be exposed to oscillations in a stretched state in the streamwise direction and in a relaxed state in the circumferential direction.

Since every vessel is unique, so is also its WSS pattern. Hence the CFD simulations must be done in subject specific vessel models. Such can be created from anatomical information acquired with magnetic resonance imaging (MRI). MRI can also be used to obtain velocity boundary conditions for the simulation. This technique was used to investigate pulsating flow in a subject specific normal human aorta. It was shown that even the flow in healthy vessels can be very disturbed and turbulence like, and even for this case large WSS variations were seen. It was also shown that regions around branches from the aorta, known to be susceptible for atherosclerosis, were characterized by high time averaged WSS and high oscillatory shear index.

Finally, the predictive capability of CFD was investigated. An idealized model of a human aorta with a coarctation and post‐stenotic dilatation was studied before and after a possible repair of the constriction. The results suggested that small remaining abnormalities in the geometry may deteriorate the chances for a successful treatment. Also, high values of shear rate and Reynolds stresses were found in the dilatation after the constriction, which previous works have shown means increased risk for thrombus formation and hemolysis.

This thesis describes a workflow to perform in-vivo wall shear stress (WSS) estimations in the human aorta using computational fluid dynamics (CFD) methods. An abnormal WSS distribution is believed to influence the development of many cardiovascular diseases, e.g. atherosclerosis. The focus in this thesis is on geometrical influence on the WSS results and interpretation methods tor non-stationary results. The work shows that results are sensitive to the choice of segmentation method (the process from medical images to a geometrical model) and a correct geometrical description of the artery is crucial in making WSS estimations. A new parameter for non-stationary WSS results has been proposed; Wall Shear Stress Angular Amplitude (WSSAA), making the analysis of non-stationary results more straight-forward. It has been shown that the workfiow can be used with confidence and that WSS can be estimated in-vivo. using the combination of MRI-based geometry definition and CFD.