McGill University (845, rue Sherbrooke O. Montréal (Québec) Canada H3A 0G4 - Canada)
Abstract : Diffusion Weighted Images normally show a low SNR due to the presence of noise from the measurement process which complicates and potentially bias the estimation of the diffusion parameters. In this paper, a new denosing method is proposed which takes into consideration the multicomponent nature of DW images. This new filter reduces random noise in multicomponent Diffusion MR images by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with similar state of art methods using synthetic and real clinical MR images showing an improved performance in all cases analyzed
Type de document :
Communication dans un congrès
ISMRM 20th Annual Meeting, May 2012, Melbourne, Australia. pp.3580, 2012
https://hal.archives-ouvertes.fr/hal-00705129
Contributeur : Pierrick Coupé
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Soumis le : mercredi 6 juin 2012 - 21:30:46
Dernière modification le : vendredi 13 avril 2018 - 19:54:13
José Manjón, Pierrick Coupé, Luis Concha, Antoni Buades, Louis Collins, et al.. DWI denoising using overcomplete Local PCA Decomposition. ISMRM 20th Annual Meeting, May 2012, Melbourne, Australia. pp.3580, 2012. 〈hal-00705129〉
