This study introduces the qualitative and quantitative characteristics of x-ray dark-field chest radiographs in healthy humans. Background X-ray dark-field radiography takes advantage of the wave properties of x-rays, with a relatively high signal in the lungs due to the many air-tissue interfaces in the alveoli. Purpose To...

Dark-field chest radiography shows dark-field signal intensity losses that correspond to emphysematous areas found on chest CT images in participants with emphysema. Background Dark-field chest radiography allows for assessment of lung alveolar structure by exploiting wave optical properties of x-rays. Purpose To evaluate the qualitative and quantitative featur...

Background: Currently, alternative medical imaging methods for the assessment of pulmonary involvement in patients infected with COVID-19 are sought that combine a higher sensitivity than conventional (attenuation-based) chest radiography with a lower radiation dose than CT imaging. Methods: Sixty patients with COVID-19-associated lung changes in a CT scan and 40 subjects without pathologic lung changes visible in the CT scan were included (in total, 100, 59 male, mean age 58 ± 14 years). All patients gave written informed consent. We employed a clinical setup for grating-based dark-field chest radiography, obtaining both a dark-field and a conventional attenuation image in one image acquisition. Attenuation images alone, dark-field images alone, and both displayed simultaneously were assessed for the presence of COVID-19-associated lung changes on a scale from 1 to 6 (1 = surely not, 6 = surely) by four blinded radiologists. Statistical analysis was performed by evaluation of the area under the receiver–operator-characteristics curves (AUC) using Obuchowski’s method with a 0.05 level of significance. Results: We show that dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging and that the combination of both is superior to one imaging modality alone. Furthermore, a quantitative image analysis shows a significant reduction of dark-field signals for COVID-19-patients. Conclusions: Dark-field imaging complements and improves conventional radiography for the visualisation and detection of COVID-19-pneumonia.

Objectives: Dark-field chest radiography (dfCXR) has recently reached clinical trials. Here we compare dfCXR to conventional radiography for the detection and staging of pulmonary emphysema. Materials and Methods: Subjects were included after a medically indicated computed tomography (CT) scan, showing either no lung impairments or different stages of emphysema. To establish a ground truth, all CT scans were assessed by 3 radiologists assigning emphysema severity scores based on the Fleischner Society classification scheme. Participants were imaged at a commercial chest radiography device and at a prototype for dfCXR, yielding both attenuation-based and dark-field images. Three radiologists blinded to CT score independently assessed images from both devices for presence and severity of emphysema (no, mild, moderate, severe). Statistical analysis included evaluation of receiver operating characteristic curves and pairwise comparison of adjacent Fleischner groups using an area under the curve (AUC)–based z test with a significance level of 0.05. Results: A total of 88 participants (54 men) with a mean age of 64 ± 12 years were included. Compared with conventional images (AUC = 0.73), readers were better able to identify emphysema with images from the dark-field prototype (AUC = 0.85, P = 0.005). Although ratings of adjacent emphysema severity groups with conventional radiographs differed only for trace and mild emphysema, ratings based on images from the dark-field prototype were different for trace and mild, mild and moderate, and moderate and confluent emphysema. Conclusions: Dark-field chest radiography is superior to conventional chest radiography for emphysema diagnosis and staging, indicating the technique's potential as a low-dose diagnostic tool for emphysema assessment.

Purpose: The purpose of this study was to evaluate the dose characteristic for patient examinations at the first clinical X‐ray dark‐field chest radiography system and to determine whether the effective patient dose is within a clinically acceptable dose range.Methods: A clinical setup for grating‐based dark‐field chest radiography was constructed and commissioned, operating at a tube voltage of 70 kVp. Thermoluminescent dosimeter (TLD) measurements were conducted using an anthropomorphic phantom modeling the reference person to obtain a conversion coefficient relating dose area product (DAP) to effective patient dose at the dark‐field system. For 92 patients, the DAP values for posterior–anterior measurements were collected at the dark‐field system. Using the previously determined conversion coefficient, the effective dose was calculated.Results: A reference person, modeled by an anthropomorphic phantom, receives an effective dose of 35 µSv. For the examined patients, a mean effective dose of 39 µSv was found.Conclusions: The effective dose at the clinical dark‐field radiography system, generating both attenuation and dark‐field images, is within the range of reported standard dose values for chest radiography.

Dark-field radiography of the human chest is a promising novel imaging technique with the potential of becoming a valuable tool for the early diagnosis of chronic obstructive pulmonary disease and other diseases of the lung. The large field-of-view needed for clinical purposes could recently be achieved by a scanning system. While this approach overcomes the limited availability of large area grating structures, it also results in a prolonged image acquisition time, leading to concomitant motion artifacts caused by intrathoracic movements (e.g. the heartbeat). Here we report on a motion artifact reduction algorithm for a dark-field X-ray scanning system, and its successful evaluation in a simulated chest phantom and human in vivo chest X-ray dark-field data. By partitioning the acquired data into virtual scans with shortened acquisition time, such motion artifacts may be reduced or even fully avoided. Our results demonstrate that motion artifacts (e.g. induced by cardiac motion or diaphragmatic movements) can effectively be reduced, thus significantly improving the image quality of dark-field chest radiographs.