Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy-Reference-Cited by-同舟云学术

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Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy

Published:2020-01-08 Issue:2 Volume:84 Page:559-568
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magn Reson Med

Author:

Lee Hyochul¹,Lee Hyeong Hun¹,Kim Hyeonjin¹²^ORCID

Affiliation:

1. Department of Biomedical Sciences Seoul National University Seoul Korea

2. Department of Radiology Seoul National University Hospital Seoul Korea

Funder

National Science Foundation, United Arab Emirates

Ministry of Education

Seoul National University

Publisher

Wiley

Subject

Radiology Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28164

Reference51 articles.

1. NMR data processing using iterative thresholding and minimum l1-norm reconstruction

2. Maximization of entropy and minimization of area as criteria for NMR signal processing

3. Application of linear prediction and singular value decomposition (LPSVD) to determine NMR frequencies and intensities from the FID

4. Deep learning

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1. Recurrent neural network-aided processing of incomplete free induction decays in 1H-MRS of the brain;Journal of Magnetic Resonance;2024-09

2. Application of a 1H brain MRS benchmark dataset to deep learning for out-of-voxel artifacts;Imaging Neuroscience;2023-11

3. Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy;Magnetic Resonance Materials in Physics, Biology and Medicine;2023-09-15

4. Multi‐echo single‐shot spectroscopy combined with simultaneous 2D model fitting for fast and accurate measurement of metabolite‐specific concentrations and T₂ relaxation times;NMR in Biomedicine;2023-08-16

5. A review of machine learning applications for the proton MR spectroscopy workflow;Magnetic Resonance in Medicine;2023-07-04

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