Evaluation of peripheral bronchiole visualization using model-based iterative reconstruction in quarter-detector computed tomography

PLoS One. 2020 Sep 18;15(9):e0239459. doi: 10.1371/journal.pone.0239459. eCollection 2020.

Abstract

This study aimed to evaluate the visualization of peripheral bronchioles in normal lungs via quarter-detector computed tomography (QDCT). Visualization of bronchioles within 10 mm from the pleura is considered a sign of bronchiectasis. However, it is not known peripheral bronchioles how close to the pleura in normal lungs can be tracked using QDCT. This study included 228 parts in 76 lungs from 38 consecutive patients who underwent QDCT. Reconstruction was performed with different thicknesses, increments, and matrix sizes: 0.5-mm thickness and increment with 512 and 1024 matrixes (Group5 and Group10, respectively) and 0.25-mm thickness and increment with 1024 matrix (Group10Thin). The distance between the most peripheral bronchiole visible and the pleura was determined in the three groups. The distance between the peripheral bronchial duct ends and the nearest pleural surface were significantly shorter in the order of Group10Thin, Group10, and Group5, and the mean distances from the pleura in Group10Thin and Group10 were shorter than 10 mm. These findings suggest the visualization of peripheral bronchioles in QDCT was better with a 1024 axial matrix than with a 512 matrix, and with a 0.25-mm slice thickness/increment than with a 0.5-mm slice thickness/increment. Our study also indicates bronchioles within 10 mm of the pleura do not necessarily indicate pathology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Bronchiectasis / diagnostic imaging
  • Bronchioles / diagnostic imaging*
  • Female
  • Humans
  • Lung / diagnostic imaging
  • Male
  • Middle Aged
  • Pleura / diagnostic imaging
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Retrospective Studies
  • Tomography, X-Ray Computed / methods*
  • Tomography, X-Ray Computed / statistics & numerical data

Grants and funding

This is a collaborative research with Canon Medical Systems Corporation. Rumiko Torigoe is an employee of Canon Medical Systems Corporation. The funder provided support in the form of salaries for Rumiko Torigoe, the CT machine used in the present study, and proofreading fee, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section.