Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.

Journal: The British journal of radiology
PMID: 34908478

Abstract

OBJECTIVES: The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting.

Authors

  • Ryoji Mikayama
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Takashi Shirasaka
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Tsukasa Kojima
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan; Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. Electronic address: tukasa@med.kyushu-u.ac.jp.
  • Yuki Sakai
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Hidetake Yabuuchi
    Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Masatoshi Kondo
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Toyoyuki Kato
    Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.

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