Wall Shear Stress Estimations using Semi-Automatic Segmentation
(English)Manuscript (preprint) (Other academic)
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.
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:liu:diva-65906OAI: oai:DiVA.org:liu-65906DiVA: diva2:400269