Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis.

Journal: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Published Date: Aug 1, 2024

Abstract

This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) compared with hybrid iterative reconstruction (HIR). In this retrospective study, 35 consecutive patients suspected of ILD who underwent CT including the chest region were included. High-resolution CT images of the unilateral lung with DLR and HIR were reconstructed for the right and left lungs. A radiologist placed regions of interest on the lung and measured standard deviation of CT attenuation (i.e., quantitative image noise). In the qualitative image analyses, 5 blinded readers assessed the presence of honeycombing and reticulation, qualitative image noise, artifacts, and overall image quality using a 5-point scale (except for artifacts which was evaluated using a 3-point scale). The quantitative and qualitative image noise in DLR was remarkably reduced compared to that in HIR ( < .001). Artifacts and overall DLR quality were significantly improved compared to those of HIR ( < .001 for 4 out of 5 readers). Interobserver agreement in the evaluations of honeycombing and reticulation for DLR (0.557 [0.450-0.693] and 0.525 [0.470-0.541], respectively) were higher than those for HIR (0.321 [0.211-0.520] and 0.470 [0.354-0.533], respectively). A statistically significant difference was found for honeycombing ( = .014). DLR improved interobserver agreement in the evaluation of honeycombing in patients with ILD on CT compared to HIR.

Authors

  • Akiyoshi Hamada
    From the Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Koichiro Yasaka
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Sosuke Hatano
    Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
  • Mariko Kurokawa
    Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
  • Shohei Inui
  • Takatoshi Kubo
    Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
  • Yusuke Watanabe
    From the Departments of Diagnostic and Interventional Radiology (D.U., A.Y., S.L.W., H. Tatekawa, H. Takita, T.H., A.S., Y.M.), Neurosurgery (T. Ichinose, H.A., Y.W., T.G.), and Medical Statistics (D.K.), Graduate School of Medicine, Osaka City University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; and Department of Radiology, Osaka City University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan (Y.K., T. Ichida).
  • Osamu Abe
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.

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