X-ray chest radiography is an inexpensive and broadly available tool for initial assessment of the lung in clinical routine, but typically lacks diagnostic sensitivity for detection of pulmonary diseases in their early stages. Recent X-ray dark-field (XDF) imaging studies on mice have shown significant improvements in imaging-based lung diagnostics. Especially in the case of early diagnosis of chronic obstructive pulmonary disease (COPD), XDF imaging clearly outperforms conventional radiography. However, a translation of this technique towards the investigation of larger mammals and finally humans has not yet been achieved. In this letter, we present the first in-vivo XDF full-field chest radiographs (32 × 35 cm2) of a living pig, acquired with clinically compatible parameters (40 s scan time, approx. 80 µSv dose). For imaging, we developed a novel high-energy XDF system that overcomes the limitations of currently established setups. Our XDF radiographs yield sufficiently high image quality to enable radiographic evaluation of the lungs. We consider this a milestone in the bench-to-bedside translation of XDF imaging and expect XDF imaging to become an invaluable tool in clinical practice, both as a general chest X-ray modality and as a dedicated tool for high-risk patients affected by smoking, industrial work and indoor cooking.

Disorders of the lungs such as chronic obstructive pulmonary disease (COPD) are a major cause of chronic morbidity and mortality and the third leading cause of death in the world. The absence of sensitive diagnostic tests for early disease stages of COPD results in under-diagnosis of this treatable disease in an estimated 60–85% of the patients. In recent years a grating-based approach to X-ray dark-field contrast imaging has shown to be very sensitive for the detection and quantification of pulmonary emphysema in small animal models. However, translation of this technique to imaging systems suitable for humans remains challenging and has not yet been reported. In this manuscript, we present the first X-ray dark-field images of in-situ human lungs in a deceased body, demonstrating the feasibility of X-ray dark-field chest radiography on a human scale. Results were correlated with findings of computed tomography imaging and autopsy. The performance of the experimental radiography setup allows acquisition of multi-contrast chest X-ray images within clinical boundary conditions, including radiation dose. Upcoming clinical studies will have to demonstrate that this technology has the potential to improve early diagnosis of COPD and pulmonary diseases in general.

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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.

Discriminating cystic from solid lesions with spectral mammography demonstrates promising results with the potential to reduce mammographic recalls. It is estimated that for each missed cancer at least 625 cystic lesions would have been correctly identified and hence would not have been needed to be recalled. Our results justify undertaking a larger reader study to refine the algorithm and determine clinically relevant thresholds to allow safe classification of cystic lesions by spectral mammography.