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dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.contributor.authorZhang, Jiayu
dc.contributor.authorAchten, Eric
dc.contributor.authorSerrai, Hacene
dc.date.accessioned2017-01-02T08:10:22Z
dc.date.available2017-01-02T08:10:22Z
dc.date.issued2016-09-05
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.
dc.identifier.issn0952-3480
dc.identifier.pmid27593698
dc.identifier.doi10.1002/nbm.3590
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.
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.
dc.publisherWiley-Blackwell
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1002/nbm.3590/abstract
dc.subjectde-noising
dc.subjectMRS
dc.subjectquantification
dc.subjectsemi-classical signal analysis
dc.subjectsignal to noise ratio
dc.titleSpectral data de-noising using semi-classical signal analysis: application to localized MRS
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalNMR in Biomedicine
dc.contributor.institutionInria Centre de recherche Bordeaux Sud-Ouest; Talence France
dc.contributor.institutionUniversiteit Gent; 9000 Ghent Belgium
kaust.personLaleg-Kirati, Taous-Meriem
kaust.personZhang, Jiayu


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