Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.

Journal: Magma (New York, N.Y.)
PMID: 39212832

Abstract

OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed.

Authors

  • Luka C Liebrand
    Department of Biomedical Engineering & Physics, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
  • Dimitrios Karkalousos
    Department of Biomedical Engineering & Physics, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
  • Emilie Poirion
    Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris F-75013, France.
  • Bart J Emmer
    Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
  • Stefan D Roosendaal
    Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands.
  • Henk A Marquering
    Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam, The Netherlands.
  • Charles B L M Majoie
    Department of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, The Netherlands.
  • Julien Savatovsky
  • Matthan W A Caan
    Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands.

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