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Abstract #2548

Noise reduction with TGV, Gaussian and Wiener filtering methods in FLAIR² images

René Schranzer1, Alexander Rauscher2, Evelin Haimburger1, Kristian Bredies3, Gernot Reishofer4, and Günther Grabner1,5

1Department of Radiologic Technology, Carinthia University of Applied Sciences, Klagenfurt, Austria, 2UBC MRI Research Centre, University of British Columbia, Vancouver, Canada, 3Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria, 4Department of Radiology, Medical University of Graz, Graz, Austria, 5Institute for Applied Research on Ageing, Carinthia University of Applied Sciences, Klagenfurt, Austria

The reduction of noise is of high value for FLAIR² images because the multiplication of FLAIR and T2 images will always result in an image with a reduced Signal-to-noise-ratio. Here different filter methods, like Gaussian, Wiener and Total Generalized Variation were used to demonstrate noise reduction. The drawback of noise reduction is a blurring effect of anatomical structures. In this study we demonstrate that TGV filtering has certain advantages compared to Wiener and Gaussian techniques in research and clinical applications.

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