In vivo magnetic resonance P-Spectral Analysis With Neural Networks: 31P-SPAWNN.

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Version: Final published version
License: CC BY-NC 4.0
Serval ID
serval:BIB_CF38A99CA851
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
In vivo magnetic resonance P-Spectral Analysis With Neural Networks: 31P-SPAWNN.
Journal
Magnetic resonance in medicine
Author(s)
Songeon J., Courvoisier S., Xin L., Agius T., Dabrowski O., Longchamp A., Lazeyras F., Klauser A.
ISSN
1522-2594 (Electronic)
ISSN-L
0740-3194
Publication state
Published
Issued date
01/2023
Peer-reviewed
Oui
Volume
89
Number
1
Pages
40-53
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with the performance of the two approaches, are compared in this work.
Convolutional neural network architectures have been proposed for the analysis and quantification of P-spectroscopy. The generation of training and test data using a fully parameterized model is presented herein. In vivo unlocalized free induction decay and three-dimensional P-magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P-SPAWNN or traditional least-square fitting techniques.
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.
Keywords
Humans, Phosphorus, Reproducibility of Results, Artificial Intelligence, Magnetic Resonance Spectroscopy/methods, Neural Networks, Computer, LCModel, convolutional neural network, deep learning, in vivo, phosphorus magnetic resonance spectroscopy
Pubmed
Web of science
Open Access
Yes
Create date
03/10/2022 14:37
Last modification date
25/01/2024 8:44
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