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Spectral data de‐noising using semi‐classical signal analysis: application to localized MRS
Author(s) -
LalegKirati TaousMeriem,
Zhang Jiayu,
Achten Eric,
Serrai Hacene
Publication year - 2016
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.3590
Subject(s) - eigenfunction , algorithm , operator (biology) , transformation (genetics) , function (biology) , fourier transform , linear map , set (abstract data type) , noise (video) , signal (programming language) , signal processing , computer science , data set , spectral analysis , spectrum (functional analysis) , physics , mathematics , mathematical analysis , artificial intelligence , eigenvalues and eigenvectors , digital signal processing , pure mathematics , chemistry , spectroscopy , repressor , image (mathematics) , biology , biochemistry , quantum mechanics , evolutionary biology , transcription factor , programming language , gene , computer hardware
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 Schrödinger 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.