Diffusion weighted image denoising using overcomplete local PCA.

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 considera...

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Autores principales:	José V Manjón, Pierrick Coupé, Luis Concha, Antonio Buades, D Louis Collins, Montserrat Robles
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e17bd996a8974884b00dc4fae7b67b1b
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spelling	oai:doaj.org-article:e17bd996a8974884b00dc4fae7b67b1b2021-11-18T08:57:14ZDiffusion weighted image denoising using overcomplete local PCA.1932-620310.1371/journal.pone.0073021https://doaj.org/article/e17bd996a8974884b00dc4fae7b67b1b2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24019889/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Diffusion 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.José V ManjónPierrick CoupéLuis ConchaAntonio BuadesD Louis CollinsMontserrat RoblesPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e73021 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q José V Manjón Pierrick Coupé Luis Concha Antonio Buades D Louis Collins Montserrat Robles Diffusion weighted image denoising using overcomplete local PCA.
description	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.
format	article
author	José V Manjón Pierrick Coupé Luis Concha Antonio Buades D Louis Collins Montserrat Robles
author_facet	José V Manjón Pierrick Coupé Luis Concha Antonio Buades D Louis Collins Montserrat Robles
author_sort	José V Manjón
title	Diffusion weighted image denoising using overcomplete local PCA.
title_short	Diffusion weighted image denoising using overcomplete local PCA.
title_full	Diffusion weighted image denoising using overcomplete local PCA.
title_fullStr	Diffusion weighted image denoising using overcomplete local PCA.
title_full_unstemmed	Diffusion weighted image denoising using overcomplete local PCA.
title_sort	diffusion weighted image denoising using overcomplete local pca.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/e17bd996a8974884b00dc4fae7b67b1b
work_keys_str_mv	AT josevmanjon diffusionweightedimagedenoisingusingovercompletelocalpca AT pierrickcoupe diffusionweightedimagedenoisingusingovercompletelocalpca AT luisconcha diffusionweightedimagedenoisingusingovercompletelocalpca AT antoniobuades diffusionweightedimagedenoisingusingovercompletelocalpca AT dlouiscollins diffusionweightedimagedenoisingusingovercompletelocalpca AT montserratrobles diffusionweightedimagedenoisingusingovercompletelocalpca
_version_	1718421114385858560

Diffusion weighted image denoising using overcomplete local PCA.

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