Keywords: Spectroscopy, Machine Learning/Artificial Intelligence, BrainEdited Magnetic Resonance Spectroscopy (Edited-MRS) is important for the quantification of ɣ-amino butyric acid (GABA) in vivo. However, during acquisition, data may suffer phase and frequency shifts, which affects the quality of the output spectrum. Frequency and phase correction (FPC) is necessary to account for these shifts, and deep learning models have obtained recent success in this task. Still, current methods do not take into consideration that MRS data is complex-valued. We propose a complex-valued convolutional neural network model for FPC. Our results showed that our model compares favorably against two recently proposed deep learning methods.
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