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

Hellbach, Katharina; Bähr, Andrea; De Marco, Fabio; Willer, Konstantin; Gromann, Lukas B.; Herzen, Julia; Dmochewitz, Michaela; Auweter, Sigrid; Fingerle, Alexander A.; Noeel, Peter B.; Rummeny, Ernst J.; Yaroshenko, Andre; Maack, Hanns-Ingo; Pralow, Thomas; Heijden, Hendrik van der; Wieberneit, Nataly; Proksa, Roland; Koehler, Thomas; Rindt, Karsten; Schröter, Tobias J.; Mohr, Jürgen; Bamberg, Fabian; Ertl-Wagner, Birgit; Pfeiffer, Franz; Reiser, Maximilian F. (2018): Depiction of pneumothoraces in a large animal model using x-ray dark-field radiography. In: Scientific Reports, Vol. 8, 2602

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-tonoise 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.

• Item Type: Journal article
• Faculties: Medicine
• Subjects: 600 Technology > 610 Medicine and health
• ISSN: 2045-2322
• Language: English