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Subject‐specific aortic wall shear stress estimations using semi‐automatic segmentation
Author(s) -
Renner J.,
Nadali Najafabadi H.,
Modin D.,
Länne T.,
Karlsson M.
Publication year - 2012
Publication title -
clinical physiology and functional imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.608
H-Index - 67
eISSN - 1475-097X
pISSN - 1475-0961
DOI - 10.1111/j.1475-097x.2012.01146.x
Subject(s) - segmentation , cardiac cycle , aorta , systole , shear stress , diastole , medicine , magnetic resonance imaging , hemodynamics , image segmentation , biomedical engineering , cardiology , artificial intelligence , computer science , radiology , physics , mechanics , blood pressure
Summary 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 3 D 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 A orta 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 .