Depiction of pneumothoraces in a large animal model using x-ray dark-field radiography

Katharina Hellbach, Andrea Baehr, Fabio De Marco, Konstantin Willer, Lukas B. Gromann, Julia Herzen, Michaela Dmochewitz, Sigrid Auweter, Alexander A. Fingerle, Peter B. Noël, Ernst J. Rummeny, Andre Yaroshenko, Hanns Ingo Maack, Thomas Pralow, Hendrik Van Der Heijden, Nataly Wieberneit, Roland Proksa, Thomas Koehler, Karsten Rindt, Tobias J. SchroeterJuergen Mohr, Fabian Bamberg, Birgit Ertl-Wagner, Franz Pfeiffer, Maximilian F. Reiser

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

The aim of this study was to assess the diagnostic value of x-ray dark-field radiography to detect pneumothoraces in a pig model. Eight pigs were imaged with an experimental grating-based large-animal dark-field scanner before and after induction of a unilateral pneumothorax. Image contrast-to-noise ratios between lung tissue and the air-filled pleural cavity were quantified for transmission and dark-field radiograms. The projected area in the object plane of the inflated lung was measured in dark-field images to quantify the collapse of lung parenchyma due to a pneumothorax. Means and standard deviations for lung sizes and signal intensities from dark-field and transmission images were tested for statistical significance using Student's two-tailed t-test for paired samples. The contrast-to-noise ratio between the air-filled pleural space of lateral pneumothoraces and lung tissue was significantly higher in the dark-field (3.65 ± 0.9) than in the transmission images (1.13 ± 1.1; p = 0.002). In case of dorsally located pneumothoraces, a significant decrease (-20.5%; p > 0.0001) in the projected area of inflated lung parenchyma was found after a pneumothorax was induced. Therefore, the detection of pneumothoraces in x-ray dark-field radiography was facilitated compared to transmission imaging in a large animal model.

Original languageEnglish
Article number2602
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - 1 Dec 2018

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