X-ray dark-field imaging of the human lung-A feasibility study on a deceased body

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.

In-vivo X-ray Dark-Field Chest Radiography of a Pig

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.

Characterization of Cystic Lesions by Spectral Mammography: Results of a Clinical Pilot Study

OBJECTIVES

Round lesions are a common mammographic finding, which can contribute more than 20% of overall recalls at screening. Discrimination of cystic fluid from solid tissue by spectral x-ray imaging has been demonstrated in specimen experiments. This work translates these results into a clinical pilot study to investigate the feasibility of discriminating cystic from solid lesions using spectral mammography.

MATERIALS AND METHODS

Women undergoing mammography as part of their routine diagnostic workup were consented for analysis of spectral information obtained from a photon-counting mammography system. Images were analyzed retrospectively after diagnosis was confirmed with ultrasound and pathology. Well-defined solitary lesions were delineated independently by 3 expert radiologists. A breast lesion model is generated from the spectral mammography data using the energy-dependent x-ray attenuation of cyst fluid, carcinoma, and adipose and glandular tissue. From the breast lesion model, 2 spectral features are computed and combined in a 2-feature discrimination algorithm, which is evaluated in an analysis of the receiver operating characteristic curve for the task of identifying solid lesions ("positive result"). Expected outcomes on a screening population are extrapolated from this pilot study by cross-validation with bootstrapping using a 95% confidence interval (CI).

RESULTS

The 2-feature discrimination algorithm was evaluated on the set of 119 eligible lesions (62 solids, 57 cysts) of diameter greater than 10 mm. The area under the receiver operating characteristic curve (AUC) was 0.88 with a specificity of 61% at the 99% sensitivity level on average over all expert radiologists. Cross-validation with bootstrapping of the clinical data revealed an AUC of 0.89 (95% CI, 0.79-0.96) and a specificity of 56% (95% CI, 33%-78%) when operating the algorithm at the 99% sensitivity level.

CONCLUSIONS

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.