Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging.

Journal: European radiology
Published Date: Feb 10, 2024

Abstract

OBJECTIVES: To develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI.

Authors

  • Cheng Xie
  • Rory Zhang
    Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Sebastian Mensink
    Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Rahul Gandharva
    Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Mustafa Awni
    Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Hester Lim
    Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Stefan E Kachel
    Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Ernest Cheung
    Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Richard Crawley
    King's College London, Strand, London, UK.
  • Leonid Churilov
    Florey Institute of Neuroscience and Mental Health, University of Melbourne, Victoria, Australia.
  • Nuno Bettencourt
    Cardiovascular R & D Unit, University of Porto, Porto, Portugal.
  • Amedeo Chiribiri
    School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
  • Cian M Scannell
    School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
  • Ruth P Lim
    Austin Health, Melbourne, Australia. ruthplim74@gmail.com.

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