Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias-Reference-Cited by-同舟云学术

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias

Published:2022-12-19 Issue:5 Volume:89 Page:1707-1727
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magnetic Resonance in Med

Author:

Rizzo Rudy¹²³⁴^ORCID,Dziadosz Martyna¹²³⁴,Kyathanahally Sreenath P.⁵^ORCID,Shamaei Amirmohammad⁶⁷^ORCID,Kreis Roland¹²⁴^ORCID

Affiliation:

1. MR Methodology, Department for Diagnostic and Interventional Neuroradiology University of Bern Bern Switzerland

2. Department for Biomedical Research University of Bern Bern Switzerland

3. Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine Bern Switzerland

4. Graduate School for Cellular and Biomedical Sciences University of Bern Bern Switzerland

5. Department of System Analysis, Integrated Assessment and Modelling, Data Science for Environmental Research Group EAWAG Dübendorf Switzerland

6. Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic Brno Czech Republic

7. Department of Biomedical Engineering Brno University of Technology Brno Czech Republic

Funder

MSCA

Nvidia

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29561

Reference87 articles.

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4. Time-domain semi-parametric estimation based on a metabolite basis set

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

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