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Abstract #1787

Improved deep gray matter segmentation using anatomical information from quantitative susceptibility maps

Ferdinand Schweser 1 , Xiang Feng 1 , Rosa Mach Batlle 1 , Daniel Gllmar 1 , Andreas Deistung 1 , Michael G Dwyer 2 , Robert Zivadinov 2,3 , and Jrgen R Reichenbach 1

1 Medical Physics Group, Institute of Diagnostic and Interventional Radiology I, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany, 2 Buffalo Neuroimaging Analysis Center, University at Buffalo SUNY, Buffalo, New York, United States, 3 Jacobs Neurological Institute, University at Buffalo SUNY, Buffalo, New York, United States

Brain image segmentation followed by region-of-interest (ROI)-based analyses is a way to quantify subtle variations of MR image intensity. In this contribution we present an approach to improve the automated segmentation of deep gray matter with FIRST that relies on the incorporation of prior anatomical information from secondary image contrasts with high-contrast in the critical brain regions. The proposed technique is solely pre-processing-based and, thus, does not require modification of the actual segmentation algorithm.

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