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Abstract #2541

Denoising MR spectra by deep learning: miracle or mirage?

Martyna Dziadosz1,2, Rudy Rizzo1,2, Sreenath P Kyathanahally3, and Roland Kreis1,2
1Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland, 2Translational Imaging Center, sitem-insel, Bern, Switzerland, 3Department Systems Analysis, Integrated Assessment and Modelling, Data Science for Environmental Research group, Dübendorf, Switzerland


The main limitation of MRS is low SNR. Several approaches for denoising have been proposed. However, it is debatable, whether denoising can reduce estimate uncertainties. In this work, we investigate denoising using deep learning (DL) in time-frequency representations. The results were assessed using two methods: first, an adjusted noise score was used and second, the outcome of traditional fitting was evaluated. We found that time-frequency domain denoising through DL produces a visually appealing spectrum but mean residuals for the relevant spectral regions and variance in fit results remained as high as without denoising.

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