PuSH - Publication Server of Helmholtz Zentrum München

Hellbach, K.* ; Yaroshenko, A.* ; Willer, K.* ; Conlon, T.M. ; Braunagel, M.B.* ; Auweter, S.* ; Yildirim, A.Ö. ; Eickelberg, O. ; Pfeiffer, F.* ; Reiser, M.F.* ; Meinel, F.G.*

X-ray dark-field radiography facilitates the diagnosis of pulmonary fibrosis in a mouse model.

Sci. Rep. 7:340 (2017)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
The aim of this study was to evaluate whether diagnosing pulmonary fibrosis with projection radiography can be improved by using X-ray dark-field radiograms. Pulmonary X-ray transmission and dark-field images of C57Bl/6N mice, either treated with bleomycin to induce pulmonary fibrosis or PBS to serve as controls, were acquired with a prototype grating-based small-animal scanner. Two blinded readers, both experienced radiologists and familiar with dark-field imaging, had to assess dark-field and transmission images for the absence or presence of fibrosis. Furthermore readers were asked to grade their stage of diagnostic confidence. Histological evaluation of the lungs served as the standard of reference in this study. Both readers showed a notably higher diagnostic confidence when analyzing the dark-field radiographs (p< 0.001). Diagnostic accuracy improved significantly when evaluating the lungs in dark-field images alone (p = 0.02) or in combination with transmission images (p = 0.01) compared to sole analysis of absorption images. Interreader agreement improved from good when assessing only transmission images to excellent when analyzing dark-field images alone or in combination with transmission images. Adding dark-field images to conventional transmission images in a murine model of pulmonary fibrosis leads to an improved diagnosis of this disease on chest radiographs.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
4.259
1.401
21
20
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Scientific Article
Keywords Computed-tomography; Image Quality; Lung-disease; Ct; Interferometer; Statement
Language english
Publication Year 2017
HGF-reported in Year 2017
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Quellenangaben Volume: 7, Issue: , Pages: , Article Number: 340 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
POF-Topic(s) 30202 - Environmental Health
Research field(s) Lung Research
PSP Element(s) G-505000-007
G-501600-001
Scopus ID 85016801769
PubMed ID 28336945
Erfassungsdatum 2018-02-12