Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.

Journal: European radiology
Published Date: Mar 3, 2022

Abstract

OBJECTIVES: To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR).

Authors

  • Mineka Sato
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Yasutaka Ichikawa
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan. yasutaka@clin.medic.mie-u.ac.jp.
  • Kensuke Domae
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Kazuya Yoshikawa
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Yoshinori Kanii
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Akio Yamazaki
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Naoki Nagasawa
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Motonori Nagata
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Masaki Ishida
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
  • Hajime Sakuma
    Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.

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