title: Subject-specific aortic wall shear stress estimations using semi-automatic segmentation.
abstract: Atherosclerosis development is strongly believed to be influenced by hemodynamic forces such as wall shear stress (WSS). To estimate such an entity in-vivo in humans, image-based computational fluid dynamics (CFD) is a useful tool. In this study, we use a combination of magnetic resonance imaging (MRI) and CFD to estimate WSS. In such method, a number of steps are included. One important step is the interpretation of images into 3D models, named segmentation. The choice of segmentation method can influence the resulting WSS distribution in the human aorta. This is studied by comparing WSS 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 eight healthy male volunteers. The different segmentation methods give slightly different geometrical depictions of the human aorta (difference in the mean thoracic Aorta lumen diameter were 0·7% P< 0·86). However, there is a very good agreement between the resulting WSS 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 point indicating that the WSS is more sensitive to local geometric differences in these parts of the cardiac cycle (correlation coefficient is 0·96 at peak systole and 0·68 at early diastole). We can conclude that the results show that the semi-automatic segmentation method can be used in future to estimate relevant aortic WSS.
authors.affiliation: Department of Mechanical Engineering, Linköping University, Linköping, Sweden. johan.renner@liu.se
authors.forename: J
authors.initials: J
authors.lastname: Renner
authors.forename: H
authors.initials: H
authors.lastname: Nadali Najafabadi
authors.forename: D
authors.initials: D
authors.lastname: Modin
authors.forename: T
authors.initials: T
authors.lastname: Länne
authors.forename: M
authors.initials: M
authors.lastname: Karlsson
mesh_terms: D000328:Adult; D000465:Algorithms; D001011:Aorta / Q000473:pathology* / Q000503:physiopathology; D001018:Aortic Diseases / Q000473:pathology* / Q000503:physiopathology; D050197:Atherosclerosis / Q000473:pathology* / Q000503:physiopathology; D057205:Automation, Laboratory; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D006439:Hemodynamics; D006801:Humans; D057446:Hydrodynamics; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008955:Models, Cardiovascular; D011237:Predictive Value of Tests; D012016:Reference Values; D012039:Regional Blood Flow; D013314:Stress, Mechanical; D013997:Time Factors; D055815:Young Adult