Bayesian deep learning–based <scp> <sup>1</sup> H‐MRS </scp> of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout-Reference-Cited by-同舟云学术

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Bayesian deep learning–based ¹ H‐MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout

Published:2022-03-28 Issue:1 Volume:88 Page:38-52
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magnetic Resonance in Med

Author:

Lee Hyeong Hun¹,Kim Hyeonjin²³^ORCID

Affiliation:

1. Department of Biomedical Sciences Seoul National University Seoul Republic of Korea

2. Department of Medical Sciences Seoul National University Seoul Republic of Korea

3. Department of Radiology Seoul National University Hospital Seoul Republic of Korea

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29214

Reference43 articles.

1. Estimation of metabolite concentrations from localizedin vivo proton NMR spectra

2. Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations

3. Time-domain quantitation of 1 H short echo-time signals: background accommodation

4. 1H-MRS processing parameters affect metabolite quantification: The urgent need for uniform and transparent standardization

5. Fitting algorithms and baseline correction influence the results of non-invasive in vivo quantitation of 2-hydroxyglutarate with 1 H-MRS

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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. Denoising Monte Carlo rendering models combined with JDKPNet in artistic design application scenarios;Intelligent Systems with Applications;2024-06

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

5. Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting;Magnetic Resonance in Medicine;2023-05-15

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