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Proceedings Paper

Tensor-based tracking of the aorta in phase-contrast MR images
Author(s): Yoo-Jin Azad; Anton Malsam; Sebastian Ley; Fabian Rengier; Rüdiger Dillmann; Roland Unterhinninghofen
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Paper Abstract

The velocity-encoded magnetic resonance imaging (PC-MRI) is a valuable technique to measure the blood flow velocity in terms of time-resolved 3D vector fields. For diagnosis, presurgical planning and therapy control monitoring the patient’s hemodynamic situation is crucial. Hence, an accurate and robust segmentation of the diseased vessel is the basis for further methods like the computation of the blood pressure. In the literature, there exist some approaches to transfer the methods of processing DT-MR images to PC-MR data, but the potential of this approach is not fully exploited yet. In this paper, we present a method to extract the centerline of the aorta in PC-MR images by applying methods from the DT-MRI. On account of this, in the first step the velocity vector fields are converted into tensor fields. In the next step tensor-based features are derived and by applying a modified tensorline algorithm the tracking of the vessel course is accomplished. The method only uses features derived from the tensor imaging without the use of additional morphology information. For evaluation purposes we applied our method to 4 volunteer as well as 26 clinical patient datasets with good results. In 29 of 30 cases our algorithm accomplished to extract the vessel centerline.

Paper Details

Date Published: 21 March 2014
PDF: 7 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340L (21 March 2014); doi: 10.1117/12.2043503
Show Author Affiliations
Yoo-Jin Azad, Karlsruhe Institute of Technology (Germany)
Anton Malsam, Karlsruhe Institute of Technology (Germany)
Sebastian Ley, Univ. Hospital Heidelberg (Germany)
Surgical Hospital Dr. Rinecker (Germany)
Fabian Rengier, Univ. Hospital Heidelberg (Germany)
German Cancer Research Ctr. (Germany)
Rüdiger Dillmann, Karlsruhe Institute of Technology (Germany)
Roland Unterhinninghofen, Karlsruhe Institute of Technology (Germany)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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