Literature DB >> 32562178

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

Yuka Morita1, Tsuneo Yamashiro2, Nanae Tsuchiya2, Maho Tsubakimoto2, Sadayuki Murayama2.   

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.

Entities:  

Keywords:  Airway segmentation; Bronchus; Quantitative measurement; Ultra-high-resolution computed tomography

Mesh:

Year:  2020        PMID: 32562178     DOI: 10.1007/s11604-020-01000-9

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN: 1867-1071            Impact factor:   2.374


  4 in total

1.  Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study.

Authors:  Yoshiharu Ohno; Naruomi Akino; Yasuko Fujisawa; Hirona Kimata; Yuya Ito; Kenji Fujii; Yumi Kataoka; Yoshihiro Ida; Yuka Oshima; Nayu Hamabuchi; Chika Shigemura; Ayumi Watanabe; Yuki Obama; Satomu Hanamatsu; Takahiro Ueda; Hirotaka Ikeda; Kazuhiro Murayama; Hiroshi Toyama
Journal:  Eur Radiol       Date:  2022-07-16       Impact factor: 7.034

2.  Pulmonary vascular enlargement and lesion extent on computed tomography are correlated with COVID-19 disease severity.

Authors:  Ryo Aoki; Tae Iwasawa; Eri Hagiwara; Shigeru Komatsu; Daisuke Utsunomiya; Takashi Ogura
Journal:  Jpn J Radiol       Date:  2021-01-27       Impact factor: 2.374

3.  A Pilot Study to Estimate the Impact of High Matrix Image Reconstruction on Chest Computed Tomography.

Authors:  Akitoshi Inoue; Tucker F Johnson; Benjamin A Voss; Yong S Lee; Shuai Leng; Chi Wan Koo; Brian D McCollough; Jayse M Weaver; Hao Gong; Rickey E Carter; Cynthia H McCollough; Joel G Fletcher
Journal:  J Clin Imaging Sci       Date:  2021-09-30

4.  Analysis and classification of radiological results and epidemiology of patients with COVID-19 pneumonia.

Authors:  Mustafa Fayadoglu; İlksen Berfin Ekinci; Elif Fayadoglu; Hüseyin Arslan; Tülin Uzunkulaoğlu
Journal:  Medicine (Baltimore)       Date:  2021-12-23       Impact factor: 1.817

  4 in total

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