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Abstract #3251

Reduction of J-difference Edited Magnetic Resonance Spectroscopy Acquisition Times Using Deep Learning

Roberto Souza1,2, Jordan McEwen3, Carissa Chung3, and Ashley D. Harris2,4
1Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada, 2Hotchkiss Brain Institute, Calgary, AB, Canada, 3Biomedical Engineering, University of Calgary, Calgary, AB, Canada, 4Radiology, University of Calgary, Calgary, AB, Canada

Magnetic Resonance Spectroscopy (MRS) non-invasively acquires in-vivo data on the chemical composition of localized tissue samples. MRS acquisitions are lengthy because they often require the acquisition of several averages to obtain a spectrum with a sufficient signal to noise ratio (SNR). This issue is augmented in J-difference edited MRS in which the analyzed spectrum is generated as the difference between sub-spectra in which editing pulses have been applied to selectively refocus the coupling of the target metabolite. In this work, we investigate the reduction of J-difference edited MRS acquisition times using deep learning.

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