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Abstract #1963

Magnetic Resonance Spectroscopy data de-noising using Semi-Classical Signal Analysis approach: Application to in-vitro MRS data.

Meriem Taous Laleg 1 , Zineb Kaisserli 1 , Rick Achten 2,3 , and Hacene Serrai 2,3

1 King Abdullah University of Sciences and Engineering, Jeddah, Saudi Arabia, 2 University of Gent, Gent, Belgium, 3 universitair Ziukenhuis Gent, Gent, Belgium

The semi-classical signal analysis method (SCSA) is a powerful post-processing technique, which uses the discrete spectrum of the Schrdinger operator where the signal is considered as potential of this operator. It is used to separate between the useful signal and noise by means of selecting eigenfunctions belonging to the signal and discarding the noise ones. Applied here, the method is able to differentiate between the eigenfunctions of the magnetic resonance spectroscopy (MRS) signal and noise. As a result, the SNR of the MRS data is improved allowing for accurate data quantification.

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