Abstract #2869

Frequency and Phase Correction of J-Difference Edited Spectra using Deep Learning

Sofie Tapper^1,2, Mark Mikkelsen^1,2, Blake E. Dewey^2,3, and Richard A. E. Edden^1,2

¹Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States

Frequency-and-phase correction is an important step in the processing of single-voxel magnetic resonance spectroscopy data, and is required for J-difference editing, which relies on subtraction to reveal a low-SNR signal. We investigated an approach for frequency-and-phase correction using deep learning. Our networks were trained using simulated spectra manipulated with different frequency-and-phase offsets. During validation, the network returned spectra that were corrected to within 1.76 ± 1.19 degrees of phase and 0.09 ± 0.05 Hz of frequency, giving a difference spectrum very similar to the true unmanipulated spectrum. Frequency-and-phase correction is a promising application for deep learning in in vivo MRS.

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