Spectral data de-noising using semi-classical signal analysis: application to localized MRS
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Computational Bioscience Research Center (CBRC)
Date
2016-09-05Online Publication Date
2016-09-05Print Publication Date
2016-10Permanent link to this record
http://hdl.handle.net/10754/622154
Metadata
Show full item recordAbstract
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.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.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.Publisher
WileyJournal
NMR in BiomedicineDOI
10.1002/nbm.3590PubMed ID
27593698Additional Links
http://onlinelibrary.wiley.com/doi/10.1002/nbm.3590/abstractae974a485f413a2113503eed53cd6c53
10.1002/nbm.3590
Scopus Count
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