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Abstract #0799

Non Local Spatial and Angular Matching : a new denoising technique for diffusion MRI

Samuel St-Jean 1 , Pierrick Coup 2 , and Maxime Descoteaux 1

1 Sherbrooke Connectivity Imaging Lab (SCIL), Universit de Sherbrooke, Sherbrooke, Qubec, Canada, 2 Unit Mixte de Recherche CNRS (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, Bordeaux, France

Diffusion Weighted Images datasets suffer from low SNR, especially at high b-values. High noise levels bias the measurements because of the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. We propose to use the redundancy of DWIs as a sparse representation to reduce the noise level and achieve a higher SNR using dictionary learning and sparse coding, without the need for additional acquisition time. We show quantitative results and compare with current state-of-the-art methods using perceptual metrics, diffusion metrics and ODFs reconstruction.

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