Meeting Banner
Abstract #2869

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

Sofie Tapper1,2, Mark Mikkelsen1,2, Blake E. Dewey2,3, and Richard A. E. Edden1,2
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here