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Proceedings Paper

Vessel centerline extraction in phase-contrast MR images using vector flow information
Author(s): Yoo-Jin Jeong; Sebastian Ley; Rüdiger Dillmann; Roland Unterhinninghofen
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Paper Abstract

To obtain hemodynamic-relevant parameters in case of cardiovascular diseases the velocity-encoded magnetic resonance imaging (PC-MRI) is used for the non-invasive measurement of the blood flow in terms of 3D velocity fields. During the segmentation of the vessel lumen in those datasets conventional segmentation methods often fail due to reduced image quality. In this paper we present a method for the centerline extraction of great vessels in PC-MR images using additional features extracted from vector flow information. The proposed algorithm can be divided in the following steps: the propagation along the vessel course by using streamlines and the largest eigenvector, the radial search for the vessel boundary, the determination of the center position in the cross-sectional plane of the vessel and the adjustment of the propagation step size subject to the vessel curvature. This is done by using a combination of morphology and flow information: the Sobel filtered and the threshold filtered image as morphologic features as well as the coherence values of the flow vectors and the behaviour of the blood flow streamlines within the vessel and around the borders as flow features. The developed algorithm was evaluated on clinical PC-MRI datasets with encouraging results. The centerline points of the entire aorta as well as corresponding border points were successfully extracted for 16 out of 17 examined datasets. For the detection of the vessel boundary the features extracted from flow information showed to yield more reliable results than morphology features.

Paper Details

Date Published: 14 February 2012
PDF: 7 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143H (14 February 2012); doi: 10.1117/12.911511
Show Author Affiliations
Yoo-Jin Jeong, Karlsruher Institute of Technology (Germany)
Sebastian Ley, Univ. Hospital Heidelberg (Germany)
Univ. of Toronto (Canada)
Rüdiger Dillmann, Karlsruher Institute of Technology (Germany)
Roland Unterhinninghofen, Karlsruher Institute of Technology (Germany)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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