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Abstract #1113

Intact Metabolite Spectrum Mining by Deep Learning in Proton Magnetic Resonance Spectroscopy of the Brain

HyeongHun Lee1 and Hyeonjin Kim1,2

1Department of Biomedical Sciences, Seoul National University, Seoul, Korea, Republic of, 2Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of

1H-MRS can quantify brain metabolites noninvasively. However, in a typical clinical setting, human brain spectra are indispensably degraded due to low SNR, line-broadening, and unknown spectral baseline, and consequently, quantification of brain metabolites is challenging even with the current state-of-the-art software. Given the recent accomplishment of deep-learning in a variety of different tasks, we developed a convolutional-neural-network (CNN) that maps the degraded brain spectra into noise-free, line-narrowed, baseline-removed, metabolite-only spectra. The robust performance of the proposed method as validated on both simulated and in vivo human brain spectra strongly supports the potential of deep learning in 1H-MRS of human brain.

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