Abstract #3073

Automatic DWI denoising using TGV with local dependent noise estimate

Gernot Reishofer¹, Kristian Bredies², Karl Koschutnig³, Christian Langkammer⁴, Margit Jehna¹, and Hannes Deutschmann¹

¹Neuroradiology, Medical University of Graz, Graz, Austria, ²Mathematics and Scientific Computing, University of Graz, Graz, Austria, ³Psychology, University of Graz, Graz, Austria, ⁴Neurology, Medical University of Graz, Graz, Austria

High-resolution diffusion weighted imaging (DWI) with reduced susceptibility artifacts can be acquired using readout-segmented echo planar imaging (rs-EPI). The poor SNR that limits the applicability of this technique increases the need for denoising strategies. We introduce a novel user independent algorithm for denoising DWI data utilizing total generalized variation (TGV)regularization under consideration of the spatial dependent noise distribution. The feasibility of the proposed method was tested on synthetic DWI data at different noise levels and compared with non-local-means (NLM) filtering.

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