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Dark-field radiography has been proven to be a promising tool for the assessment of various lung diseases. To evaluate the potential of dose reduction in dark-field chest radiography for the detection of the Coronavirus SARS-CoV-2 (COVID-19) pneumonia. Patients aged at least 18 years with a medically indicated chest computed tomography scan (CT scan) were screened for participation in a prospective study between October 2018 and December 2020. Patients were included if they had a CO-RADS (COVID-19 Reporting and Data System) score ≥ 4 (COVID-19 group) or if they had no pathologic lung changes (controls). A total of 89 participants with a median age of 60 years (interquartile range 48 to 68 yrs.) were included in this study. Dark-field and attenuation-based radiographs were simultaneously obtained by using a prototype system for dark-field radiography. By modifying the image reconstruction algorithm, low-dose radiographs were simulated based on real participant images. The simulated radiographs corresponded to 50%, 25%, and 13% of the full dose (41.9 μSv, median value). Four experienced radiologists served as blinded readers assessing both image modalities, displayed side by side in random order. The presence of COVID-19-associated lung changes was rated on a scale from 1 to 6. The readers' diagnostic performance was evaluated by analyzing the area under the receiver operating characteristic curves (AUC) using Obuchowski's method. Also, the dark-field images were analyzed quantitatively by comparing the dark-field coefficients within and between the COVID-19 and the control group. The readers' diagnostic performance in the image evaluation, as described by the AUC value (where a value of 1 corresponds to perfect diagnostic accuracy), did not differ significantly between the full dose images (AUC = 0.86) and the simulated images at 50% (AUC = 0.86) and 25% of the full dose(AUC = 0.84) (p>0.050), but was slightly lower at 13% dose (AUC = 0.82) (p = 0.038). For all four radiation dose levels, the median dark-field coefficients within groups were identical but different significantly by 15% between the controls and the COVID-19 pneumonia group (p<0.001). Dark-field imaging can be used to diagnose the Coronavirus SARS-CoV-2 (COVID-19) pneumonia with a median dose of 10.5 μSv, which corresponds to 25% of the original dose used for dark-field chest imaging.

Grating-based X-ray dark-field imaging is a novel imaging modality which has been refined during the last decade. It exploits the wave-like behaviour of X-radiation and can nowadays be implemented with existing X-ray tubes used in clinical applications. The method is based on the detection of small-angle X-ray scattering, which occurs e.g. at air-tissue-interfaces in the lung or bone-fat interfaces in spongy bone. In contrast to attenuation-based chest X-ray imaging, the optimal tube voltage for dark-field imaging of the thorax has not yet been examined. In this work, dark-field scans with tube voltages ranging from 60 to 120 kVp were performed on a deceased human body. We analyzed the resulting images with respect to subjective and objective image quality, and found that the optimum tube voltage for dark-field thorax imaging at the used setup is at rather low energies of around 60 to 70 kVp. Furthermore, we found that at these tube voltages, the transmission radiographs still exhibit sufficient image quality to correlate dark-field information. Therefore, this study may serve as an important guideline for the development of clinical dark-field chest X-ray imaging devices for future routine use.

Grating-based X-ray dark-field imaging is a novel imaging modality with enormous technical progress during the last years. It enables the detection of microstructure impairment as in the healthy lung a strong dark-field signal is present due to the high number of air-tissue interfaces. Using the experience from setups for animal imaging, first studies with a human cadaver could be performed recently. Subsequently, the first dark-field scanner for in-vivo chest imaging of humans was developed. In the current study, the optimal tube voltage for dark-field radiography of the thorax in this setup was examined using an anthropomorphic chest phantom. Tube voltages of 50–125 kVp were used while maintaining a constant dose-area-product. The resulting dark-field and attenuation radiographs were evaluated in a reader study as well as objectively in terms of contrast-to-noise ratio and signal strength. We found that the optimum tube voltage for dark-field imaging is 70 kVp as here the most favorable combination of image quality, signal strength, and sharpness is present. At this voltage, a high image quality was perceived in the reader study also for attenuation radiographs, which should be sufficient for routine imaging. The results of this study are fundamental for upcoming patient studies with living humans.

Background Although x-ray dark-field imaging has been intensively investigated for lung imaging in different animal models, there is very limited data about imaging features in the human lungs. Therefore, in this work, a reader study on nine post-mortem human chest x-ray dark-field radiographs was performed to evaluate dark-field signal strength in the lungs, intraobserver and interobserver agreement, and image quality and to correlate with findings of conventional x-ray and CT. Methods In this prospective work, chest x-ray dark-field radiography with a tube voltage of 70 kVp was performed post-mortem on nine humans (3 females, 6 males, age range 52–88 years). Visual quantification of dark-field and transmission signals in the lungs was performed by three radiologists. Results were compared to findings on conventional x-rays and 256-slice computed tomography. Image quality was evaluated. For ordinal data, median, range, and dot plots with medians and 95% confidence intervals are presented; intraobserver and interobserver agreement were determined using weighted Cohen κ . Results Dark-field signal grading showed significant differences between upper and middle ( p  = 0.004–0.016, readers 1–3) as well as upper and lower zones ( p  = 0.004–0.016, readers 1–2). Median transmission grading was indifferent between all lung regions. Intraobserver and interobserver agreements were substantial to almost perfect for grading of both dark-field ( κ  = 0.793–0.971 and κ  = 0.828–0.893) and transmission images ( κ  = 0.790–0.918 and κ  = 0.700–0.772). Pulmonary infiltrates correlated with areas of reduced dark-field signal. Image quality was rated good for dark-field images. Conclusions Chest x-ray dark-field images provide information of the lungs complementary to conventional x-ray and allow reliable visual quantification of dark-field signal strength.

The purpose of this study was to investigate a preclinical spectral photon-counting CT (SPCCT) prototype compared to conventional CT for pulmonary imaging. A custom-made lung phantom, including nodules of different sizes and shapes, was scanned with a preclinical SPCCT and a conventional CT in standard and high-resolution (HR-CT) mode. Volume estimation was evaluated by linear regression. Shape similarity was evaluated with the Dice similarity coefficient. Spatial resolution was investigated via MTF for each imaging system. In-vivo rabbit lung images from the SPCCT system were subjectively reviewed. Evaluating the volume estimation, linear regression showed best results for the SPCCT compared to CT and HR-CT with a root mean squared error of 21.3 mm 3 , 28.5 mm 3 and 26.4 mm 3 for SPCCT, CT and HR-CT, respectively. The Dice similarity coefficient was superior for SPCCT throughout nodule shapes and all nodule sizes (mean, SPCCT: 0.90; CT: 0.85; HR-CT: 0.85). 10% MTF improved from 10.1 LP/cm for HR-CT to 21.7 LP/cm for SPCCT. Visual investigation of small pulmonary structures was superior for SPCCT in the animal study. In conclusion, the SPCCT prototype has the potential to improve the assessment of lung structures due to higher resolution compared to conventional CT.

Background: To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa). Methods: Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles). Results: All C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ. Conclusion: In this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality.

Background: Skeletal muscle mass depletion adversely affects critically ill patient outcomes. Standardized methods for assessing muscle mass in this population are limited, particularly regarding changes during ICU stays and their implications for risk stratification. Methods: In this secondary analysis of our prospective data registry of surgical ICU patients, we used a single slice extracted from a computed tomography scan to determine the patient’s direction of absolute change in skeletal muscle mass between two different time points (−14 d to +0 d and +5 d to +21 d) during his or her critical illness. Results: In total, 98 surgical patients were included in the final analysis. A decrease in a patient’s skeletal muscle mass is associated with prolonged mechanical ventilation compared to patients whose skeletal muscle mass remained the same or increased (415 vs. 42 h, p = 0.003). Patients losing skeletal muscle mass also needed to be ventilated more frequently (88.3% vs. 60.5%, p = 0.002), had a higher rate of tracheotomy (50.0% vs. 23.7%, p = 0.011), and had an increased ICU length of stay (22 vs. 13 days, p = 0.045). Conclusions: A decreased skeletal muscle index in early critical illness negatively impacts ventilation parameters, highlighting the importance of monitoring and managing muscle mass changes to optimize outcomes in ICU patients.

PurposeEvaluation of dual-layer spectral computed tomography (CT) for contrast enhancement during image-guided biopsy of liver lesions using virtual monoenergetic images (VMI) and virtual non-contrast (VNC) images.MethodsSpectral CT data of 20 patients receiving CT-guided needle biopsy of focal liver lesions were used to generate VMI at energy levels from 40 to 200 keV and VNC images. Images were analyzed objectively regarding contrast-to-noise ratio between lesion center (CNRcent) or periphery (CNRperi) and normal liver parenchyma. Lesion visibility and image quality were evaluated on a 4-point Likert scale by two radiologists.ResultsUsing VMI/VNC images, readers reported an increased visibility of the lesion compared to the conventional CT images in 18/20 cases. In 75% of cases, the highest visibility was derived by VMI-40. Showing all reconstructions simultaneously, VMI-40 offered the highest visibility in 75% of cases, followed by VNC in 12.5% of cases. Either CNRcent (17/20) or/and CNRperi (17/20) was higher (CNR increase > 50%) in 19/20 cases for VMI-40 or VNC images compared to conventional CT images. VMI-40 showed the highest CNRcent in 14 cases and the highest CNRperi in 12 cases. High image quality was present for all reconstructions with a minimum median of 3.5 for VMI-40 and VMI-50.ConclusionsWhen implemented in the CT scanner software, automated contrast enhancement of liver lesions during image-guided biopsy may facilitate the procedure.

Objective Assessing the advantage of x-ray dark-field contrast over x-ray transmission contrast in radiography for the detection of developing radiation-induced lung damage in mice. Methods Two groups of female C57BL/6 mice (irradiated and control) were imaged obtaining both contrasts monthly for 28 weeks post irradiation. Six mice received 20 Gy of irradiation to the entire right lung sparing the left lung. The control group of six mice was not irradiated. A total of 88 radiographs of both contrasts were evaluated for both groups based on average values for two regions of interest, covering (irradiated) right lung and healthy left lung. The ratio of these average values, R, was distinguished between healthy and damaged lungs for both contrasts. The time-point when deviations of R from healthy lung exceeded 3σ was determined and compared among contrasts. The Wilcoxon-Mann-Whitney test was used to test against the null hypothesis that there is no difference between both groups. A selection of 32 radiographs was assessed by radiologists. Sensitivity and specificity were determined in order to compare the diagnostic potential of both contrasts. Inter-reader and intra-reader accuracy were rated with Cohen’s kappa. Results Radiation-induced morphological changes of lung tissue caused deviations from the control group that were measured on average 10 weeks earlier with x-ray dark-field contrast than with x-ray transmission contrast. Sensitivity, specificity, and accuracy doubled using dark-field radiography. Conclusion X-ray dark-field radiography detects morphological changes of lung tissue associated with radiation-induced damage earlier than transmission radiography in a pre-clinical mouse model. Key Points • Significant deviations from healthy lung due to irradiation were measured after 16 weeks with x-ray dark-field radiography (p = 0.004). • Significant deviations occur on average 10 weeks earlier for x-ray dark-field radiography in comparison to x-ray transmission radiography. • Sensitivity and specificity doubled when using x-ray dark-field radiography instead of x-ray transmission radiography.

ObjectivesEvaluation of sparse sampling computed tomography (SpSCT) regarding subjective and objective image criteria for the detection of pulmonary embolism (PE) at different simulated dose levels.MethodsComputed tomography pulmonary angiography (CTPA) scans of 20 clinical patients were used to obtain simulated low-dose scans with 100%–50%–25%–12.5%–6.3%–3.1% of the clinical dose, resulting in a total of six dose levels (DL). From these full sampling (FS) data, every second (2-SpSCT) or fourth (4-SpSCT) projection was used to obtain simulated sparse sampling scans. Each image set was evaluated by four blinded radiologists regarding subjective image criteria (artifacts, image quality) and diagnostic performance (confidence, sensitivity, specificity, accuracy, and area under the curve). Additionally, the contrast-to-noise ratio (CNR) was evaluated for objective image quality.ResultsSensitivity was 100% with 2-SpSCT and 4-SpSCT at the 25% DL and the 12.5% DL for all localizations of PE (one subgroup 98.5%). With FS, the sensitivity decreased to 90% at the 12.5% DL. 2-SpSCT and 4-SpSCT showed higher values for sensitivity, specificity, accuracy, and the area under the curve at all DL compared with FS. Subjective image quality was significantly higher for 4-SpSCT compared with FS at each dose level (p < 0.01, paired t test). Only with 4-SpSCT, all examinations were rated as showing diagnostic image quality at the 12.5% DL.ConclusionsVia SpSCT, a dose reduction down to a 12.5% dose level (corresponding to a mean effective dose of 0.38 mSv in the current study) for CTPA is possible while maintaining high image quality and full diagnostic confidence.Key Points• With sparse sampling CT, radiation dose could be significantly reduced in clinical routine.• Sparse sampling CT is a novel hardware solution with which less projection images are acquired.• In the current study, a dose reduction of 87.5% (corresponding to a mean effective dose of 0.38 mSv) for CTPA could be achieved while maintaining excellent diagnostic performance.