Abstract #3251

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

Roberto Souza^1,2, Jordan McEwen³, Carissa Chung³, and Ashley D. Harris^2,4

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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