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Abstract #3633

A denoising method for arterial spin labeling data based on total generalized variation (TGV) with a spatial varying regularization parameter

Stefan Manfred Spann1, Kamil S Kazimierski-Hentschel2, Christoph Stefan Aigner1, and Rudolf Stollberger1,3

1Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 2Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria, 3BioTechMed-Graz, Graz, Austria

Arterial spin labeling perfusion imaging permits a noninvasive approach to measure cerebral blood flow. The poor SNR of this technique makes denoising essential. ASL images are often corrupted with motion, physiological or scanning artifacts or acquired using parallel imaging leading to spatial dependent noise. To account for those artifacts and spatial varying noise we propose a denoising approach based on total generalized variation (TGV) using a spatial dependent regularization parameter. The performance of the proposed technique is evaluated on synthetic and in-vivo data and compared with the non-local means combined dual-tree complex wavelet transform (DT-CWT) denoising method.

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