Evaluation of thin-slice abdominal DECT using deep-learning image reconstruction in 74 keV virtual monoenergetic images: an image quality comparison.

Journal: Abdominal radiology (New York)
Published Date: Feb 21, 2023

Abstract

PURPOSE: To compare noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image quality using deep-learning image reconstruction (DLIR) vs. adaptive statistical iterative reconstruction (ASIR-V) in 0.625 and 2.5 mm slice thickness gray scale 74 keV virtual monoenergetic (VM) abdominal dual-energy CT (DECT).

Authors

  • Jack J Xu
    Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark. jack.junchi.xu@regionh.dk.
  • Lars Lönn
    Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
  • Esben Budtz-Jørgensen
    Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Samir Jawad
    Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark.
  • Peter S Ulriksen
    Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
  • Kristoffer L Hansen
    Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.

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