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
King Abdullah University of Sciences and
Engineering, Jeddah, Saudi Arabia,
of Gent, Gent, Belgium,
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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