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 Grabner^1,5

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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