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• W4297235512 abstract "Purpose 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. Theory and Methods 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. 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." @default.
• W4297235512 created "2022-09-28" @default.
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• W4297235512 date "2022-09-25" @default.
• W4297235512 modified "2023-10-16" @default.
• W4297235512 title "In vivo magnetic resonance 31P‐Spectral Analysis With Neural Networks: 31P‐SPAWNN" @default.
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• W4297235512 doi "https://doi.org/10.1002/mrm.29446" @default.
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