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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