In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 2 ( 2023-2-9), p. e0281452-
Kurzfassung:
The advent of micro-computed tomography (microCT) has provided significant advancement in our ability to generate clinically relevant assessments of lung health and disease in small animal models. As microCT use to generate outcomes analysis in pulmonary preclinical models has increased there have been substantial improvements in image quality and resolution, and data analysis software. However, there are limited published methods for standardized imaging and automated analysis available for investigators. Manual quantitative analysis of microCT images is complicated by the presence of inflammation and parenchymal disease. To improve the efficiency and limit user-associated bias, we have developed an automated pulmonary air and tissue segmentation (PATS) task list to segment lung air volume and lung tissue volume for quantitative analysis. We demonstrate the effective use of the PATS task list using four distinct methods for imaging, 1) in vivo respiration controlled scanning using a flexi Vent, 2) longitudinal breath-gated in vivo scanning in resolving and non-resolving pulmonary disease initiated by lipopolysaccharide-, bleomycin-, and silica-exposure, 3) post-mortem imaging, and 4) ex vivo high-resolution scanning. The accuracy of the PATS task list was compared to manual segmentation. The use of these imaging techniques and automated quantification methodology across multiple models of lung injury and fibrosis demonstrates the broad applicability and adaptability of microCT to various lung diseases and small animal models and presents a significant advance in efficiency and standardization of preclinical microCT imaging and analysis for the field of pulmonary research.
Materialart:
Online-Ressource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0281452
DOI:
10.1371/journal.pone.0281452.g001
DOI:
10.1371/journal.pone.0281452.g002
DOI:
10.1371/journal.pone.0281452.g003
DOI:
10.1371/journal.pone.0281452.g004
DOI:
10.1371/journal.pone.0281452.g005
DOI:
10.1371/journal.pone.0281452.g006
DOI:
10.1371/journal.pone.0281452.t001
DOI:
10.1371/journal.pone.0281452.t002
DOI:
10.1371/journal.pone.0281452.s001
DOI:
10.1371/journal.pone.0281452.s002
DOI:
10.1371/journal.pone.0281452.s003
Sprache:
Englisch
Verlag:
Public Library of Science (PLoS)
Publikationsdatum:
2023
ZDB Id:
2267670-3