Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction - A retrospective observational study.

Journal: European journal of radiology
PMID: 39500043

Abstract

PURPOSE: To compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT scans from a conventional single-energy protocol.

Authors

  • Florian Bürckenmeyer
    Department of Radiology, Jena University Hospital, Jena, Germany.
  • Stephanie Gräger
    Friedrich-Schiller-University Jena, Jena University Hospital, Department of Diagnostic and Interventional Radiology, Jena, Germany. Electronic address: stephanie.graeger@med.uni-jena.de.
  • Lucja Mlynska
    Friedrich-Schiller-University Jena, Jena University Hospital, Department of Diagnostic and Interventional Radiology, Jena, Germany. Electronic address: lucja.mlynska@med.uni-jena.de.
  • Felix Güttler
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
  • Maja Ingwersen
    Friedrich-Schiller-University Jena, Jena University Hospital, Department of Diagnostic and Interventional Radiology, Jena, Germany. Electronic address: maja.ingwersen@gmx.de.
  • Ulf Teichgräber
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
  • Martin Krämer
    Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany.

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