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

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Názov: In vivo magnetic resonance 31P‐Spectral Analysis With Neural Networks: 31P‐SPAWNN.
Autori: Songeon, Julien, Courvoisier, Sébastien, Xin, Lijing, Agius, Thomas, Dabrowski, Oscar, Longchamp, Alban, Lazeyras, François, Klauser, Antoine
Zdroj: Magnetic Resonance in Medicine; Jan2023, Vol. 89 Issue 1, p40-53, 14p
Predmety: MAGNETIC resonance, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, SPECTRAL imaging, NUCLEAR magnetic resonance spectroscopy
Abstrakt: Purpose: We have introduced an artificial intelligence framework, 31P‐SPAWNN, in order to fully analyze phosphorus‐31 (31$$ {}^{31} $$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. Theory and Methods: Convolutional neural network architectures have been proposed for the analysis and quantification of 31$$ {}^{31} $$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 31$$ {}^{31} $$P‐magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P‐SPAWNN or traditional least‐square fitting techniques. Results: 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. Conclusion: The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting. [ABSTRACT FROM AUTHOR]
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Databáza: Complementary Index
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Abstrakt:Purpose: We have introduced an artificial intelligence framework, 31P‐SPAWNN, in order to fully analyze phosphorus‐31 (31$$ {}^{31} $$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. Theory and Methods: Convolutional neural network architectures have been proposed for the analysis and quantification of 31$$ {}^{31} $$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 31$$ {}^{31} $$P‐magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P‐SPAWNN or traditional least‐square fitting techniques. Results: 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. Conclusion: The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting. [ABSTRACT FROM AUTHOR]
ISSN:07403194
DOI:10.1002/mrm.29446