Machine learning-based multi-pool Voigt fitting of CEST, rNOE, and MTC in Z-spectra.

Journal: Magnetic resonance in medicine
PMID: 39963087

Abstract

PURPOSE: Four-pool Voigt (FPV) machine learning (ML)-based fitting for Z-spectra was developed to reduce fitting times for clinical feasibility in terms of on-scanner analysis and to promote larger cohort studies. The approach was compared to four-pool Lorentzian (FPL)-ML-based modeling to empirically verify the advantage of Voigt models for Z-spectra.

Authors

  • Sajad Mohammed Ali
    Department of Medical Radiation Physics, Lund University, Lund, Sweden.
  • Peter C M van Zijl
    Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA.
  • Jannik Prasuhn
    F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA.
  • Ronnie Wirestam
    Department of Medical Radiation Physics, Lund University, Lund, Sweden.
  • Linda Knutsson
    Department of Medical Radiation Physics, Lund University, Lund, Sweden.
  • Nirbhay N Yadav
    The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

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