Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study.

Journal: Multiple sclerosis and related disorders
Published Date: Apr 21, 2024

Abstract

BACKGROUND: Within the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contrast agents, which brings about associated complexities. This study investigates the potential of deep learning to differentiate between active and inactive lesions in MS using non-contrast FLAIR-type MRI data, presenting a non-invasive alternative to conventional gadolinium-based MRI methods.

Authors

  • AmirAbbas Amini
    School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Azin Shayganfar
    Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Zahra Amini
    Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Leila Ostovar
    Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Somayeh HajiAhmadi
    Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Navid Chitsaz
    Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Masoud Rabbani
    Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Raheleh Kafieh
    Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

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