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Conference papers

DWI denoising using overcomplete Local PCA Decomposition

José Manjón 1 Pierrick Coupé 2 Luis Concha 3 Antoni Buades 4 Louis Collins 5 Montserrat Robles 1
1 ITACA
2 LaBRI - Laboratoire Bordelais de Recherche en Informatique
3 UNAM - Institute of Neurobiology, National Autonomous University of Mexico
4 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
5 MNI - McConnell Brain Imaging Centre
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
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00705129
Contributor : Pierrick Coupé Connect in order to contact the contributor
Submitted on : Wednesday, June 6, 2012 - 9:30:46 PM
Last modification on : Monday, December 20, 2021 - 4:50:10 PM

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  • HAL Id : hal-00705129, version 1

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CNRS | UNIV-BORDEAUX | UNIV-PARIS5 | UNIV-PARIS | UP-SCIENCES | MAP5

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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. ⟨hal-00705129⟩

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