In vivo magnetic resonance

LCModel convolutional neural network deep learning in vivo phosphorus magnetic resonance spectroscopy

Journal

Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245

Informations de publication

Date de publication:
Jan 2023
Historique:
revised: 16 08 2022
received: 20 04 2022
accepted: 18 08 2022
pubmed: 27 9 2022
medline: 2 11 2022
entrez: 26 9 2022
Statut: ppublish

Résumé

We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( Convolutional neural network architectures have been proposed for the analysis and quantification of The presented experiment has demonstrated both the reliability and accuracy of 31P-SPAWNN for estimating metabolite concentrations and spectral parameters. Simulated test data showed improved quantification using 31P-SPAWNN compared with LCModel. In vivo data analysis revealed higher accuracy at low signal-to-noise ratio using 31P-SPAWNN, yet with equivalent precision. Processing time using 31P-SPAWNN can be further shortened up to two orders of magnitude. The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting.

Identifiants

pubmed: 36161342
doi: 10.1002/mrm.29446
pmc: PMC9828468
doi:

Substances chimiques

Phosphorus 27YLU75U4W

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

40-53

Informations de copyright

© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

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Auteurs

Julien Songeon (J)

Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

Sébastien Courvoisier (S)

Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
CIBM Center for Biomedical Imaging, Geneva, Switzerland.

Lijing Xin (L)

CIBM Center for Biomedical Imaging, Geneva, Switzerland.
Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Thomas Agius (T)

Department of Vascular Surgery, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.

Oscar Dabrowski (O)

Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

Alban Longchamp (A)

Department of Vascular Surgery, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.

François Lazeyras (F)

Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
CIBM Center for Biomedical Imaging, Geneva, Switzerland.

Antoine Klauser (A)

Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
CIBM Center for Biomedical Imaging, Geneva, Switzerland.

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Classifications MeSH