Songeon, JulienCourvoisier, SebastienXin, LijingAgius, ThomasDabrowski, OscarLongchamp, AlbanLazeyras, FrancoisKlauser, Antoine2022-10-102022-10-102022-10-102022-09-2510.1002/mrm.29446https://infoscience.epfl.ch/handle/20.500.14299/191379WOS:000859031700001Purpose: We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 (P-31) 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-31-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-31-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.Radiology, Nuclear Medicine & Medical ImagingRadiology, Nuclear Medicine & Medical Imagingconvolutional neural networkdeep learningin vivolcmodelphosphorus magnetic resonance spectroscopymetabolite concentrationshuman brainnmrresolutionquantificationspectroscopyp-31-mrsspectraatptext::journal::journal article::research article