Automatic bronchial segmentation on ultra-HRCT scans: advantage of the 1024-matrix size with 0.25-mm slice thickness reconstruction

Abstract

Purpose

The aim of this study was to evaluate the advantages of ultra-high-resolution computed tomography (U-HRCT) for automatic bronchial segmentation.

Materials and methods

This retrospective study was approved by the Institutional Review Board, and written informed consent was waived. Thirty-three consecutive patients who underwent chest CT by a U-HRCT scanner were enrolled. In each patient, CT data were reconstructed by two different protocols: 512 × 512 matrix with 0.5-mm slice thickness (conventional HRCT mode) and 1024 × 1024 matrix with 0.25-mm slice thickness (U-HRCT mode). We used a research workstation to compare the two CT modes with regard to the numbers and total lengths of the automatically segmented bronchi.

Results

Significantly greater numbers and longer lengths of peripheral bronchi were segmented in the U-HRCT mode than in the conventional HRCT mode (P < 0.001, for fifth- to eighth-generation bronchi). For example, the mean numbers and total lengths of the sixth-generation bronchi were 81 and 1048 mm in the U-HRCT mode and 59 and 538 mm in the conventional HRCT mode.

Conclusions

The U-HRCT mode greatly improves automatic airway segmentation for the more peripheral bronchi, compared with the conventional HRCT mode. This advantage can be applied to routine clinical care, such as virtual bronchoscopy and automatic lung segmentation.

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Acknowledgements

The authors thank the engineers and technologists of Ziosoft for their excellent help in developing the research software.

Funding

University of the Ryukyus receives a research grant from Canon Medical Systems and Ziosoft.

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Correspondence to Yuka Morita.

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Morita, Y., Yamashiro, T., Tsuchiya, N. et al. Automatic bronchial segmentation on ultra-HRCT scans: advantage of the 1024-matrix size with 0.25-mm slice thickness reconstruction. Jpn J Radiol (2020). https://doi.org/10.1007/s11604-020-01000-9

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Keywords

  • Ultra-high-resolution computed tomography
  • Bronchus
  • Airway segmentation
  • Quantitative measurement