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Abstract #4324

A Shearlet-based whole brain vein segmentation algorithm and its application for the detection of regional differences in venous oxygenation

Sina Straub1, Janis Stiegeler1,2, Edris El-Sanosy3, and Till M. Schneider4
1Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 2Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany, 3Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 44Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany

Generating whole-brain vein segmentations can be very time-consuming. In this abstract, a method is proposed that can segment brain veins from a single-echo or multi-echo gradient echo scan. The segmentation algorithm combines classical vessel enhancement filtering and local thresholding methods with a shearlet-based multi-scale approach. Compared with a ground truth, the algorithm performs better for multi-echo data when R2* information is included in the segmentation. Moreover, the combination of venous segmentation with masks for deep and superficial venous territories yields higher susceptibility values for the superficial venous vasculature which is in accordance with a higher oxygen consumption of the cortex.

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