Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.

Journal: Academic radiology
Published Date: Jun 1, 2025

Abstract

RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-based reconstruction (DLR) algorithm called AiCE Inner Ear.

Authors

  • Lavinia Brockstedt
    Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany (S.A., M.A.M., L.B., A.K., M.A.B., A.E.O.).
  • Nils F Grauhan
    Department of Radiology, Charité - Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany. nils-friedrich.grauhan@charite.de.
  • Andrea Kronfeld
    Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany (S.A., M.A.M., L.B., A.K., M.A.B., A.E.O.).
  • Mario Alberto Abello Mercado
    Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany.
  • Julia Döge
    Department of ear, nose, and throat medicine, University Medical Centre Mainz, Johannes Gutenberg University Mainz, Mainz, Germany (J.D.).
  • Antoine Sanner
    Department of Neuroradiology, University Medical Center Mainz, Mainz, Germany.
  • Marc A Brockmann
    Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany (S.A., M.A.M., L.B., A.K., M.A.B., A.E.O.).
  • Ahmed E Othman
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany; Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany. Electronic address: ahmed.e.othman@googlemail.com.

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