Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
661-03-009 ISMRM Abstract

Noise-Aware Fast Magnetic Resonance Spectroscopy Reconstruction with Complex U-Net

Primary: Acquisition & Reconstruction - Artifacts and Correction Strategies
Secondary: Acquisition & Reconstruction - Signal Modeling
661-03-009 · Noise and Artifact Mitigation in MRI · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row B
Keywords: Machine learning Denoising UNet
Accepted
Huaizhi Liu1, Christopher J Wu2, Lawrence S Kegeles3,4, Jia Guo1,3
1Biomedical Engineering, Columbia University, New York, United States of America
2Taub Institute, Columbia University, New York, United States of America
3Psychiatry, Columbia University, New York, United States of America
4MRI Research Program, New York State Psychiatric Institute, New York, United States of America
Presenting Author: Zongyu Li

Synopsis

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References

1. Chen D, Hu W, Liu H, et al. Magnetic resonance spectroscopy deep learning denoising using few in vivo data. IEEE Trans Comput Imaging. 2023;9:448-458. https://doi.org/10.1109/TCI.2023.3267623 [doi]
2. Wang J, Ji B, Lei Y, Liu T, Mao H, Yang X. 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, Kreis R. Denoising single MR spectra by deep learning: Miracle or mirage? Magn Reson Med. 2023;90:1749-1761. doi:10.1002/mrm.29762 [doi]
4. Mikkelsen M, Barker PB, Bhattacharyya PK, et al. Big GABA: Edited MR spectroscopy at 24 research sites. Neuroimage. 2017;159:32-45. doi:10.1016/j.neuroimage.2017.07.021 [doi]
5. Yang Y, Zhang Y, Li Z, Tian JS, Dagommer M, Guo J. Deep learning-based MRI reconstruction with Artificial Fourier Transform Network (AFTNet). Comput Biol Med. 2025;192:110224. doi:10.1016/j.compbiomed.2025.110224 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/661-03-009