Centerlines of blood vessels are useful tools to make important anatomical measurements (length, diameter, area), which cannot be accurately obtained using 2D images. In this paper a brand new method for centerline extraction of vascular trees is presented. By using computational fluid dynamics (CFD) we are able to obtain a robust and purely functional centerline allowing us to support better measurements than classic purely geometrical-based centerlines. We show that the CFD-based centerline is within a few pixels from the geometrical centerline where the latter is defined (far away from inlet/outlets and from the branches). We show that the centerline computed with our method is not affected by traditional errors of other classical volume-based algorithms such as topo- logical thinning, and could be a potential alternative to be considered for future studies.

A robust construction algorithm of the centerline skeleton for complex aortic vascular structure using computational fluid dynamics

BOLOGNA, MARCO;MIGLIAVACCA, FRANCESCO;
2017-01-01

Abstract

Centerlines of blood vessels are useful tools to make important anatomical measurements (length, diameter, area), which cannot be accurately obtained using 2D images. In this paper a brand new method for centerline extraction of vascular trees is presented. By using computational fluid dynamics (CFD) we are able to obtain a robust and purely functional centerline allowing us to support better measurements than classic purely geometrical-based centerlines. We show that the CFD-based centerline is within a few pixels from the geometrical centerline where the latter is defined (far away from inlet/outlets and from the branches). We show that the centerline computed with our method is not affected by traditional errors of other classical volume-based algorithms such as topo- logical thinning, and could be a potential alternative to be considered for future studies.
2017
Abdominal aortic aneurysm, Centerline, Computational fliuid dynamics, Computed tomography, Image segmentation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1031710
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