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Abstract #1923

Graph-based segmentation of signal voids in time series of diffusion-weighted images of musculature in the human lower leg

Martin Schwartz1,2, G√ľnter Steidle1, Petros Martirosian1, Bin Yang2, and Fritz Schick1

1Section on Experimental Radiology, Department of Radiology, University of Tuebingen, Tuebingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

The segmentation of signal voids, which occur in time-series of single-shot diffusion-weighted images, is important for an accelerated evaluation providing larger studies on this phenomenon. The proposed segmentation is based on a two-stage detection and segmentation approach, which utilizes a graph-based representation with random walker optimization. It was demonstrated that the presented method enables a fast and accurate segmentation of signal voids in time-series of diffusion-weighted images.

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