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DWI denoising using overcomplete Local PCA Decomposition

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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https://hal.archives-ouvertes.fr/hal-00705129
Contributor : Pierrick Coupé <>
Submitted on : Wednesday, June 6, 2012 - 9:30:46 PM
Last modification on : Friday, April 10, 2020 - 5:24:13 PM

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  • HAL Id : hal-00705129, version 1

Citation

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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