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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