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Vectorial wall shear stress calculations in vessel structures using 4D PC-MRI

Authors
Journal
Journal of Cardiovascular Magnetic Resonance
1097-6647
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
14
Identifiers
DOI: 10.1186/1532-429x-14-s1-w5
Keywords
  • Workshop Presentation
Disciplines
  • Computer Science

Abstract

Vectorial wall shear stress calculations in vessel structures using 4D PC-MRI WORKSHOP PRESENTATION Open Access Vectorial wall shear stress calculations in vessel structures using 4D PC-MRI Wouter V Potters1,2*, Pim van Ooij1,2, Ed vanBavel2, Aart Nederveen1 From 15th Annual SCMR Scientific Sessions Orlando, FL, USA. 2-5 February 2012 Summary We propose a fully automated method for calculating vectorial wall shear stress (WSS) in-vivo based on 4D PC-MRI data. Background Wall shear stress (WSS) is the tangential force of flow- ing blood on the vessel wall. WSS directly influences remodeling of the vessel wall. Methods Velocity data were corrected for aliasing and phase off- sets and subsequently filtered using a median filter. Inward unit normal vectors were determined on the wall, after which a coordinate transformation was per- formed for each point at the wall such that the normal vector coincided with the z-axis of the transformed coordinate system. Velocities at fixed points along the normal were calculated using natural 3D interpolation in the original data. Any perpendicular velocity compo- nents were removed as only tangential velocity compo- nents contribute to the viscous forces at the wall. Smoothing splines were then fitted to the x- and y- velocity components along the inward unit normals. The x- and y-derivatives at the vessel wall were derived analytically and multiplied with the viscosity, which resulted in the WSS. Finally, all WSS vectors were trans- formed back to the original coordinate system. This method was validated using a synthetic dataset of a rigid straight tube (diameter 6mm) with parabolic flow, in which the theoretical WSS could be derived analytically (Poiseuille). Effects of resolution, segmenta- tion errors and noise were assessed using this phantom data. Secondly the algorithm was tested in in-vivo PC- MRI data. In vivo PC-MRI of the common carotid artery was performed on a 3T MRI system (Philips Healthcare, Best, The Netherlands) using a

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