Literature DB >> 32004765

Effect of scan mode and focal spot size in airway dimension measurements for ultra-high-resolution computed tomography of chronic obstructive pulmonary disease: A COPDGene phantom study.

Ryoji Mikayama1, Takashi Shirasaka2, Hidetake Yabuuchi3, Yuki Sakai4, Tsukasa Kojima5, Masatoshi Kondo6, Hideki Yoshikawa7, Toyoyuki Kato8.   

Abstract

PURPOSE: Quantitative evaluations of airway dimensions through computed tomography (CT) have revealed a good correlation with airflow limitation in chronic obstructive pulmonary disease. However, large inaccuracies have been known to occur in CT airway measurements. Ultra-high-resolution CT (UHRCT) might improve measurement accuracy using precise scan modes with minimal focal spot. We assessed the effects of scan mode and focal spot size on airway measurements in UHRCT.
METHODS: COPDGene Ⅱ phantom, comprising a plastic tube mimicking human airway of inner diameter 3 mm, wall thickness 0.6 mm, and inclination 30 degrees was scanned at super high resolution (SHR, beam collimation of 0.25 mm × 160 rows) and high resolution (HR, beam collimation of 0.5 mm × 80 rows) modes using UHRCT. Each acquisition was performed both with small (0.4 × 0.5 mm) and large (0.6 × 1.3 mm) focal spots. The wall area percentage (WA%) was calculated as the percentage of total airway area occupied by the airway wall. Statistical analysis was performed to compare the WA% measurement errors for each scan mode and focal spot size.
RESULTS: The WA% measurement errors in the SHR mode were 9.8% with a small focal spot and 18.8% with a large one. The measurement errors in the HR mode were 13.3% with a small focal spot and 21.4% with a large one. There were significant differences between each scan mode and focal spot size (p < 0.05).
CONCLUSIONS: The SHR mode with a small focal spot could improve airway measurement accuracy of UHRCT.
Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Chronic obstructive pulmonary disease; Phantom study; Ultra-high-resolution CT; Wall area percentage

Year:  2020        PMID: 32004765     DOI: 10.1016/j.ejmp.2019.12.025

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  2 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.  Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study.

Authors:  Yuki Sakai; Erina Kitamoto; Kazutoshi Okamura; Masato Tatsumi; Takashi Shirasaka; Ryoji Mikayama; Masatoshi Kondo; Hiroshi Hamasaki; Toyoyuki Kato; Kazunori Yoshiura
Journal:  Dentomaxillofac Radiol       Date:  2021-04-29       Impact factor: 3.525

  2 in total

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