Tensor-based tracking of the aorta in phase-contrast MR images

Yoo-Jin Azad; Anton Malsam; Sebastian Ley; Fabian Rengier; Rüdiger Dillmann; Roland Unterhinninghofen

doi:10.1117/12.2043503

21 March 2014 Tensor-based tracking of the aorta in phase-contrast MR images

Yoo-Jin Azad, Anton Malsam, Sebastian Ley, Fabian Rengier, Rüdiger Dillmann, Roland Unterhinninghofen

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90340L (2014) https://doi.org/10.1117/12.2043503
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Yoo-Jin Azad, Anton Malsam, Sebastian Ley, Fabian Rengier, Rüdiger Dillmann, and Roland Unterhinninghofen "Tensor-based tracking of the aorta in phase-contrast MR images", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340L (21 March 2014); https://doi.org/10.1117/12.2043503

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