569-05-015 ISMRM Abstract

Deep Learning–based Denoising of Magnetic Resonance Spectroscopy using a Denoising Autoencoder

Primary: Neuro

Secondary: Physics & Engineering

569-05-015 · Spectroscopy and Spectroscopic Imaging: Methods · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row J

Keywords: Deep learning Magnetic Resonance Spectroscopy (MRS) Signal-to-noise ratio (SNR) Denoising autoencoder Dynamic metabolism

Accepted

Kou Kumagai ¹, Hitoshi Kubo², Koshi Shigiyama², Urara Hirano¹, Takashi Iwanaga²

¹School of Graduate Education, Fukushima Medical University, Fukushima, Japan

²Department of Radiological Sciences, School of Health Science, Fukushima Medical University, Fukushima, Japan

Presenting Author: Kou Kumagai

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References

1. Ip IB, Berrington A, Hess AT, et al. Combined fMRI-MRS acquires simultaneous glutamate and BOLD-fMRI signals in the human brain. Neuroimage. 2017;155:113-119. doi:10.1016/j.neuroimage.2017.04.030. [doi]

2. Wang J, Ji B, Lei Y, et al. Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS. Med Phys. 2023;50(12):7955-7966. doi:10.1002/mp.16831. [doi]

3. Dziadosz M, Rizzo R, Kyathanahally SP, et al. Denoising single MR spectra by deep learning: miracle or mirage? Magn Reson Med. 2023;90(5):1-13. doi:10.1002/mrm.29762. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-05-015