Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results.

Journal: Clinical radiology
PMID: 34782114

Abstract

AIM: To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (DLIR) and compare them with hybrid iterative reconstruction (IR).

Authors

  • Y Noda
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan. Electronic address: noda1031@gifu-u.ac.jp.
  • F Nakamura
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • T Kawamura
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • N Kawai
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • T Kaga
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • T Miyoshi
    Department of Radiology Services, Gifu University Hospital, Gifu, Japan.
  • H Kato
    Department of Transfusion Medicine, Aichi Medical University, Nagakute, Aichi 480-1195, Japan.
  • F Hyodo
    Department of Radiology, Frontier Science for Imaging, Gifu University, Gifu, Japan.
  • M Matsuo
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.

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