Adaptive wavelet noise suppression (AWNS) is a software package that can suppress spectral noise in magnetic resonance spectroscopy (MRS).
Current development has allowed AWNS process human brain cancer proton MRS with the support from TARQUIN (Wilson 2019) package.
Philosophy
AWNS combines the concepts of MRS theory, data mining and wavelet analysis.
According to MRS theory (Kreis 2021), the acquired MRS spectrum contains both meaningful components that can be quantified into metabolite concentrations and meaningless components which can be treated as noise.
Meanwhile, it is possible to decompose a time series with flexible time-frequency resolution through Heisenberg uncertainty principle (Stéphane 2009), which allows decomposing MRS spectra according to line-shapes.
However, we do not know how to distinguish noise ones and metabolite-related ones in the decomposed spectral components?
AWNS does this through a data mining method, which is to use quantification performance to determine the outstanding wavelet variation.
Features
In comparison to other tools, AWNS features:
No dependence on "good quality" MRS as training data.
A universal solution to different noise levels.
Support for real world clinical or scientific acquisition and diverse metabolite profiles.
Has the potential of providing more precise metabolite profiles.
Download
Please download AWNS from here.
Please note AWNS has not been developed into the stage of clinical translation.