Spectral data de-noising using semi-classical signal analysis: application to localized MRS

Handle URI:
http://hdl.handle.net/10754/622154
Title:
Spectral data de-noising using semi-classical signal analysis: application to localized MRS
Authors:
Laleg-Kirati, Taous-Meriem ( 0000-0001-5944-0121 ) ; Zhang, Jiayu; Achten, Eric; Serrai, Hacene
Abstract:
In this paper, we propose a new post-processing technique called semi-classical signal analysis (SCSA) for MRS data de-noising. Similar to Fourier transformation, SCSA decomposes the input real positive MR spectrum into a set of linear combinations of squared eigenfunctions equivalently represented by localized functions with shape derived from the potential function of the Schrodinger operator. In this manner, the MRS spectral peaks represented as a sum of these 'shaped like' functions are efficiently separated from noise and accurately analyzed. The performance of the method is tested by analyzing simulated and real MRS data. The results obtained demonstrate that the SCSA method is highly efficient in localized MRS data de-noising and allows for an accurate data quantification.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Computational Bioscience Research Center (CBRC)
Citation:
Laleg-Kirati T-M, Zhang J, Achten E, Serrai H (2016) Spectral data de-noising using semi-classical signal analysis: application to localized MRS. NMR in Biomedicine 29: 1477–1485. Available: http://dx.doi.org/10.1002/nbm.3590.
Publisher:
Wiley-Blackwell
Journal:
NMR in Biomedicine
Issue Date:
5-Sep-2016
DOI:
10.1002/nbm.3590
Type:
Article
ISSN:
0952-3480
Sponsors:
The first and second authors would like to thank King Abdullah University of Science and Technology (KAUST) for its financial support and Dr S. Van Huell from University of Leuven for the use of the SVD software.
Additional Links:
http://onlinelibrary.wiley.com/doi/10.1002/nbm.3590/abstract
Appears in Collections:
Articles; Electrical Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLaleg-Kirati, Taous-Meriemen
dc.contributor.authorZhang, Jiayuen
dc.contributor.authorAchten, Ericen
dc.contributor.authorSerrai, Haceneen
dc.date.accessioned2017-01-02T08:10:22Z-
dc.date.available2017-01-02T08:10:22Z-
dc.date.issued2016-09-05en
dc.identifier.citationLaleg-Kirati T-M, Zhang J, Achten E, Serrai H (2016) Spectral data de-noising using semi-classical signal analysis: application to localized MRS. NMR in Biomedicine 29: 1477–1485. Available: http://dx.doi.org/10.1002/nbm.3590.en
dc.identifier.issn0952-3480en
dc.identifier.doi10.1002/nbm.3590en
dc.identifier.urihttp://hdl.handle.net/10754/622154-
dc.description.abstractIn this paper, we propose a new post-processing technique called semi-classical signal analysis (SCSA) for MRS data de-noising. Similar to Fourier transformation, SCSA decomposes the input real positive MR spectrum into a set of linear combinations of squared eigenfunctions equivalently represented by localized functions with shape derived from the potential function of the Schrodinger operator. In this manner, the MRS spectral peaks represented as a sum of these 'shaped like' functions are efficiently separated from noise and accurately analyzed. The performance of the method is tested by analyzing simulated and real MRS data. The results obtained demonstrate that the SCSA method is highly efficient in localized MRS data de-noising and allows for an accurate data quantification.en
dc.description.sponsorshipThe first and second authors would like to thank King Abdullah University of Science and Technology (KAUST) for its financial support and Dr S. Van Huell from University of Leuven for the use of the SVD software.en
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1002/nbm.3590/abstracten
dc.subjectde-noisingen
dc.subjectMRSen
dc.subjectquantificationen
dc.subjectsemi-classical signal analysisen
dc.subjectsignal to noise ratioen
dc.titleSpectral data de-noising using semi-classical signal analysis: application to localized MRSen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalNMR in Biomedicineen
dc.contributor.institutionInria Centre de recherche Bordeaux Sud-Ouest; Talence Franceen
dc.contributor.institutionUniversiteit Gent; 9000 Ghent Belgiumen
kaust.authorLaleg-Kirati, Taous-Meriemen
kaust.authorZhang, Jiayuen
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