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Diffusion Weighted Image Denoising using overcomplete Local PCA

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Diffusion Weighted Image Denoising using overcomplete Local PCA

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dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Coupé, Pierrick es_ES
dc.contributor.author Concha, Luis es_ES
dc.contributor.author Buades, Antonio es_ES
dc.contributor.author Collins, Louis es_ES
dc.contributor.author Robles Viejo, Montserrat es_ES
dc.date.accessioned 2015-02-09T12:29:38Z
dc.date.available 2015-02-09T12:29:38Z
dc.date.issued 2013-09
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10251/46849
dc.description.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. es_ES
dc.description.sponsorship This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. This work has been also partially supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. en_EN
dc.language Inglés es_ES
dc.publisher Public Library of Science es_ES
dc.relation.ispartof PLoS ONE es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject MRI Denoising PCA es_ES
dc.subject Magnetic-Resonance images es_ES
dc.subject Rician Noise Removal es_ES
dc.subject Tensor MRI es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Diffusion Weighted Image Denoising using overcomplete Local PCA es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1371/journal.pone.0073021
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2011-26727/ES/MEDIDA AUTOMATICA DE ESTRUCTURAS CEREBRALES CORTICALES A PARTIR DE IMAGENES DE RM/
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-LABX-0057/FR/Translational Research and Advanced Imaging Laboratory/TRAIL/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Manjón Herrera, JV.; Coupé, P.; Concha, L.; Buades, A.; Collins, L.; Robles Viejo, M. (2013). Diffusion Weighted Image Denoising using overcomplete Local PCA. PLoS ONE. 8(9):1-12. https://doi.org/10.1371/journal.pone.0073021 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1371/journal.pone.0073021 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 9 es_ES
dc.relation.senia 260578
dc.identifier.pmid 24019889 en_EN
dc.identifier.pmcid PMC3760829 en_EN
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Agence Nationale de la Recherche, Francia
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