CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning-based reconstruction.

Journal: European radiology
PMID: 34327575

Abstract

OBJECTIVES: The aim of this study was to evaluate the sensitivity of CT-based thermometry for clinical applications regarding a three-component tissue phantom of fat, muscle and bone. Virtual monoenergetic images (VMI) by dual-energy measurements and conventional polychromatic 120-kVp images with modern reconstruction algorithms adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning image reconstruction (DLIR) were compared.

Authors

  • Andreas Heinrich
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
  • Sebastian Schenkl
    Institute of Forensic Medicine, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
  • David Buckreus
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
  • Felix V Güttler
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
  • Ulf K-M Teichgräber
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.

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