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Abstract #5479

Ghostbusters for MRS: Automatic Detection of Ghosting Artifacts using Deep Learning

Sreenath P Kyathanahally1, Andre Doering1, and Roland Kreis1

1Depts. Radiology and Clinical Research, University of Bern, Bern, Switzerland

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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