| Autor: |
Julien, Songeon, Sébastien, Courvoisier, Lijing, Xin, Thomas, Agius, Oscar, Dabrowski, Alban, Longchamp, François, Lazeyras, Antoine, Klauser |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Magnetic resonance in medicineREFERENCES. 89(1) |
| ISSN: |
1522-2594 |
| Popis: |
We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 (mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:semanticsmml:mrowmml:msupmml:mrow/mml:mrowmml:mn31/mml:mn/mml:mrow/mml:msup/mml:mrowmml:annotation$$ {}^{31} $$/mml:annotation/mml:semantics/mml:mathP) 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 ofmml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:semanticsmml:mrowmml:msupmml:mrow/mml:mrowmml:mn31/mml:mn/mml:mrow/mml:msup/mml:mrowmml:annotation$$ {}^{31} $$/mml:annotation/mml:semantics/mml:mathP-spectroscopy. The generation of training and test data using a fully parameterized model is presented herein. In vivo unlocalized free induction decay and three-dimensionalmml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:semanticsmml:mrowmml:msupmml:mrow/mml:mrowmml:mn31/mml:mn/mml:mrow/mml:msup/mml:mrowmml:annotation$$ {}^{31} $$/mml:annotation/mml:semantics/mml:mathP-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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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