Spectral data de-noising using semi-classical signal analysis: application to localized MRS.
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.
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Inria Centre de recherche Bordeaux Sud-Ouest, Talence, France.
Taous-Meriem
TM
Laleg-Kirati
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Jiayu
J
Zhang
Universiteit Gent, 9000, Ghent, Belgium.
Eric
E
Achten
Universiteit Gent, 9000, Ghent, Belgium. hacene.serrai@ugent.be.
Hacene
H
Serrai
D000465:Algorithms; D016477:Artifacts; D001923:Brain Chemistry; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
MRS; de-noising; quantification; semi-classical signal analysis; signal to noise ratio