title: Diffusion weighted image denoising using overcomplete local PCA.
abstract: Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.
authors.affiliation: Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain.
authors.forename: José V
authors.initials: JV
authors.lastname: Manjón
authors.forename: Pierrick
authors.initials: P
authors.lastname: Coupé
authors.forename: Luis
authors.initials: L
authors.lastname: Concha
authors.forename: Antonio
authors.initials: A
authors.lastname: Buades
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Montserrat
authors.initials: M
authors.lastname: Robles
mesh_terms: D001921:Brain / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging; D025341:Principal Component Analysis; D059629:Signal-To-Noise Ratio
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