Ghosting artifacts in spectroscopy are problematic since they superimpose with metabolites and lead to inaccurate quantification. Detection of ghosting artifacts using traditional machine learning approaches with feature extraction/selection is difficult since ghosts appear at different frequencies. Here, we used a “Deep Learning” approach, that was trained on a huge database of simulated spectra with and without ghosting artifacts that represent the complex variations of ghost-ridden spectra. The trained model was tested on simulated and in-vivo spectra. The preliminary results are very promising, reaching almost 100% accuracy and further testing on in-vivo spectra will hopefully confirm its ghost busting capacity
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