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

Journal: Magnetic resonance in medicine
PMID: 36161342

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.

Authors

  • Julien Songeon
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
  • Sébastien Courvoisier
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
  • Lijing Xin
    CIBM Center for Biomedical Imaging, Geneva, Switzerland.
  • Thomas Agius
    Department of Vascular Surgery, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.
  • Oscar Dabrowski
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
  • Alban Longchamp
    Department of Vascular Surgery, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.
  • François Lazeyras
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
  • Antoine Klauser
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

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