Spectral data de-noising using semi-classical signal analysis: application to localized MRS
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Computational Bioscience Research Center (CBRC)
Permanent link to this recordhttp://hdl.handle.net/10754/622154
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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.
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.
SponsorsThe 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.
JournalNMR in Biomedicine
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