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Abstract #4109

Signal-to-noise Ratio Enhancement of 31P Magnetic Resonance Spectroscopy using a Pre-trained Deep Learning Model

Yeong-Jae Jeon1,2, Kyung Min Nam3,4,5, Alex Bhogal3, and Hyeon-Man Baek1,2
1Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, Korea, Republic of, 2Lee Gil Ya Cancer & Diabetes Institute, Gachon University, Incheon, Korea, Republic of, 3Department of Radiology, University Medical Centre Utrecht, Utrecht, Netherlands, 4Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Swaziland, 5Translational Imaging Ceter, sitem-insel AG, Bern, Switzerland

Synopsis

Keywords: Data Processing, Spectroscopy, Non-Proton, Animals, Brain, Precision & AccuracyWe demonstrate the feasibility of a novel denoising approach utilizing a pretrained deep learning model with multiscale local polynomial smoothing for single voxel 31P MRS data in the mice brain at 9.4T. We evaluated the low-rank denoising, one of the popular methods and the proposed method using LCModel to compare their performance. Both methods resulted in improved signal-to-noise ratio and decreased uncertainty (Cramer-Rao Lower Bounds). In this work, the suggested method outperformed in signal-to-noise ratio enhancement.

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