BACKGROUND:Clinical X-ray dark-field radiography has shown to be promising for visualizing different lung pathologies. To keep the radiation dose as low as reasonably achievable (ALARA principle), individualized exposure planning is necessary. However, the current scanning-based implementation of dark-field radiography complicates the use of automatic exposure control. Previously, a BMI-based linear regression model was proposed as a substitute. Here, we aim to improve this proposed model by investigating multiple linear regression for patient-individual exposure planning of dark-field chest radiography. METHODS:For this retrospective study, 273 posteroanterior thorax images acquired at a prototype system for dark-field chest radiography were analyzed retrospectively regarding the X-ray tube current needed to achieve the target radiation dose. Different multiple linear regression models were tested to find the optimal multiple regression model for predicting the necessary tube current based on a person's weight, height, age, and sex. R2 score, the root mean square error (RMSE), and the mean absolute percentage error (MAPE) were used to evaluate the goodness-of-fit of different regression models. Each model was also compared to a BMI-based model. RESULTS:To predict the target tube current for dark-field chest radiography, multiple linear regression using ordinary least squares performed best (R2 = 0.712, RMSE = 0.234, and MAPE = 0.033). In comparison, simple linear regression using only the body mass index achieved only R2 = 0.627, RMSE = 0.266, and MAPE = 0.037. CONCLUSION:Multiple linear regression allows better exposure planning in X-ray dark-field chest radiography than simple linear regression.

BACKGROUND:The microstructure of material at a μ m $\,\umu\mathrm{m}$ length scale leads to ultra-small-angle scattering of X-rays, which typically occurs, e.g., for lung tissue or some plastic foams. When using an interferometer, this effect alters the visibility of the fringe pattern, which can be detected and resolved by the detector. Thus, the ultra-small-angle scattering can be represented as a dark-field image. For a polychromatic source, the hardening of the source spectrum changes visibility as well, generating an additional fake dark-field signal by the attenuation of the material on top of the real ultra-small-angle scatter-related dark-field signal. Consequently, even homogeneous materials without microstructure typically exhibit a change in visibility. PURPOSE:The objective of this study is to develop a fast, simple, and robust method to correct dark-field signals and bony structures present due to beam hardening on dark-field chest radiographs of study participants. METHODS:The method is based on calibration measurements and image processing. BH by bones and soft tissue is modeled by aluminum and water, respectively, which have no microstructure and thus only generate an artificial dark-field signal. Look-up tables were then created for both. By using a weighted mean of these, forming a single LUT, and using the attenuation images, the artificial dark-field signal and thus the bone structures present are reduced for study participants. RESULTS:It was found that applying a correction using a weighted LUT leads to a significant reduction of bone structures in the dark-field image. The weighting of the aluminum component has a substantial impact on the degree to which bone structures remain visible in the dark-field image. Furthermore, a large negative bias in the dark-field image-dependent on the aluminum weighting-was successfully corrected. CONCLUSIONS:BH-induced signal in the dark-field images was successfully reduced using the method described. The choice of aluminum weighting to suppress rib structures, as well as the selection of bias correction, should be evaluated based on the specific clinical question.

BACKGROUND:Dark-field radiography is a novel x-ray imaging modality that provides complementary diagnostic information by visualising microstructural properties of lung tissue. Implemented via a Talbot-Lau interferometer integrated into a conventional x-ray system, it permits simultaneous acquisition of perfectly registered attenuation and dark-field radiographs. Clinical studies have shown that dark-field radiography outperforms conventional radiography in diagnosing and staging pulmonary diseases, yet the polychromatic nature of medical x-ray sources causes beam hardening and introduces structured artifacts, especially from ribs and clavicles. PURPOSE:To address the artificial dark-field signal arising from beam-hardening and thereby improve the reliability of clinical dark-field chest radiography by suppressing bone-induced artifacts. METHODS:A segmentation-based beam-hardening correction (BHC) was developed that employs deep learning to segment ribs and clavicles and uses attenuation-contribution masks derived from dual-layer detector computed-tomography data to refine the material distribution and estimate beam-hardening effects. The rib segmentation network was trained on 196 chest radiographs with 49 validation images (VinDr-RibCXR), and a clavicle network was trained on 56 images with 12 validation and 12 test cases. The trained models were applied to 174 dark-field chest radiographs (51 chronic obstructive pulmonary disease, 86 COVID-19, 37 healthy) and spectral CT scans from two patients; input data consisted of attenuation and dark-field images and outputs were corrected dark-field images and derived lung-signal metrics. RESULTS:The proposed method markedly reduced bone-induced artifacts and improved the homogeneity of the lung dark-field signal. In comparative analyses, the corrected images exhibited diminished structured cross-talk between attenuation and dark-field channels, enhancing both visual interpretation and quantitative consistency across cohorts. CONCLUSIONS:By combining deep-learning-based anatomical segmentation with material-specific attenuation weighting, the proposed BHC suppresses the artificial dark-field signal caused by polychromatic x-ray spectra, leading to more reliable assessment of pulmonary microstructure in clinical dark-field chest radiography.

Background: X-ray dark-field radiography uses small-angle scattering to visualize the structural integrity of lung alveoli. To study the influence of dose reduction on clinical dark-field radiographs, one can simulate low-dose images by virtually reducing the irradiated area. However, these simulations can exhibit stripe artifacts. Purpose: Validation of the low-dose simulation algorithm reported in Schick et al. (2024). Furthermore, we want to demonstrate that stripe artifacts observed in simulated images at very low-dose levels are introduced by limitations of the algorithm and would not appear in actual low-dose dark-field images. Methods: Dark-field radiographs of an anthropomorphic chest phantom were acquired at different tube currents equaling different radiation doses. Based on the measurement with a high radiation dose, dark-field radiographs corresponding to lower radiation doses were simulated by virtually reducing the irradiated area. The simulated low-dose radiographs were evaluated by a quantitative comparison of the dark-field signal using different regions of interest. Results: Dark-field radiographs acquired at one quarter of the standard dose were artifact-free. The dark-field signal differed from the simulated radiographs by up to 10%. Algorithm-induced stripe artifacts decrease the image quality of the simulated radiographs. Conclusions: Virtually reducing the irradiation area is a simple approach to generate low-dose radiographs based on images acquired with scanning-based dark-field radiography. However, as the algorithm creates stripe artifacts in the dark-field images, particularly at very low dose levels, that are not present in measured low-dose images, simulated images have reduced image quality compared to their measured counterparts.

Abstract Spectral computed tomography (CT) is a burgeoning quantitative imaging technique with applications in oncologic diagnostics, prognostic prediction, tissue perfusion studies, and treatment follow-up. While normalized iodine concentration values have been correlated with microenvironmental biophysical changes, obtaining accurate iodine concentrations, particularly at low concentrations remains difficult due to varying spectral CT instrumentation performance. Hybrid spectral CT systems, combining multiple spectral CT instrumentation techniques, address these quantitation insufficiencies by increasing spectral separation but have not been evaluated on a clinically analogous platform. We validate a hybrid spectral CT system, comprised of clinical-grade components, acquiring four distinct effective spectra and applying efficient noise-reducing weighting schemes to compare iodine noise and bias against conventional kVp-Switching (kVp-S). Two tube current levels (50, 350 mA) and three duty cycle ratios (33/67, 50/50, 75/25) were implemented to elucidate radiation dose exposure and kVp-S parameterization impact. A standard quality assurance (QA) and patient-derived, abdominal IodinePrint phantom were scanned on the system. The average absolute bias in iodine density images of the QA phantom was comparable across acquisition techniques, below 0.5 mg/mL, while quantitative noise improved by 22% using noise-optimized weighting schemes. In the IodinePrint phantom aorta and pancreas structures, the noise-optimized weighting scheme increased signal-to-noise ratio (SNR) by 1.3x compared to kVp-S alone. These results highlight the increased precision of hybrid, multi-channel spectral CT systems and motivate CT designs that enable robust CT biomarker development.

Purpose:To develop and evaluate a novel double bowtie filter integrating a K-edge material layer with a conventional Teflon filter for pediatric spectral computed tomography (CT). The proposed design aims to enhance spectral signal-to-noise ratio (SNR) and spectral separation while maintaining radiation dose levels suitable for pediatric imaging. Methods:A simulation framework was set up and used to model a rapid kVp-switching CT system operating at 70/110 kVp with realistic tube power and geometry constraints. Pediatric phantoms of three sizes (100- 200 mm anterior-posterior width) were used to evaluate performance. Five accessible and safe filter materials-gadolinium (Gd), holmium (Ho), erbium (Er), silver (Ag), and tin (Sn)-were tested in combination with a Teflon bowtie. System performance was quantified using virtual monoenergetic image (VMI) SNR at 40 keV and 70 keV, and the area under the monoenergetic SNR curve (AUMC) as a comprehensive spectral image quality metric. Dose consistency with a traditional Teflon bowtie reference was enforced. Results:The Teflon + Gd configuration achieved the highest performance, improving AUMC by 47.5 % on average and up to 56 % for the largest phantom. VMI SNR increased by approximately 49 % at 40 keV and 42 % at 70 keV. Conclusions:The double-bowtie concept substantially enhances spectral performance. The Teflon + Gd design provides a manufacturable, pediatric-optimized solution adaptable to kVp-switching and other spectral CT architectures, offering improved diagnostic quality at low dose levels.

BackgroundDark-field chest radiography allows the assessment of the structural integrity of the alveoli by exploiting the wave properties of x-rays.PurposeTo compare the qualitative and quantitative features of dark-field chest radiography in patients with COVID-19 pneumonia with conventional CT imaging.Materials and methodsIn this prospective study conducted from May 2020 to December 2020, patients aged at least 18 years who underwent chest CT for clinically suspected COVID-19 infection were screened for participation. Inclusion criteria were a CO-RADS score ≥4, the ability to consent to the procedure and to stand upright without help. Participants were examined with a clinical dark-field chest radiography prototype. For comparison, a healthy control cohort of 40 subjects was evaluated. Using Spearman's correlation coefficient, correlation was tested between dark-field coefficient and CT-based COVID-19 index and visual total CT score as well as between the visual total dark-field score and the visual total CT score.ResultsA total of 98 participants [mean age 58 ± 14 (standard deviation) years; 59 men] were studied. The areas of signal intensity reduction observed in dark-field images showed a strong correlation with infiltrates identified on CT scans. The dark-field coefficient had a negative correlation with both the quantitative CT-based COVID-19 index (r = −.34, p = .001) and the overall CT score used for visual grading of COVID-19 severity (r = −.44, p < .001). The total visual dark-field score for the presence of COVID-19 was positively correlated to the total CT score for visual COVID-19 severity grading (r = .85, p < .001).ConclusionCOVID-19 pneumonia-induced signal intensity losses in dark-field chest radiographs are consistent with CT-based findings, showing the technique's potential for COVID-19 assessment.

Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. However, previous studies of dark-field chest radiographs evaluated the lung signal only in the inspiratory breathing state. Our work aims to add a new perspective to these previous assessments by locally comparing dark-field lung information between different respiratory states. To this end, we discuss suitable image registration methods for dark-field chest radiographs to enable consistent spatial alignment of the lung in distinct breathing states. Utilizing full inspiration and expiration scans from a clinical chronic obstructive pulmonary disease study, we assess the performance of the proposed registration framework and outline applicable evaluation approaches. Our regional characterization of lung dark-field signal changes between the breathing states provides a proof-of-principle that dynamic radiography-based lung function assessment approaches may benefit from considering registered dark-field images in addition to standard plain chest radiographs.

This study proposes a spectral data reduction method for multi-channel computed tomography (CT) that optimizes material decomposition accuracy while minimizing data complexity. Spectral CT enables quantitative assessments by utilizing multiple spectral channels, yet the associated noise and computational demands can limit its clinical application. We introduce a weighting scheme that reduces acquired four spectral channels-derived from a dual-layer, rapid kVp-switching (kVp-S) CT setup-into two optimized input channels for material decomposition. This scheme minimizes noise in iodine and water decomposition tasks by optimizing weights based on the Cramer-Rao lower bound. We modeled various duty cycles and patient sizes and compared results to full four-channel and traditional kVp-S configurations. The two-input weighting schemes showed consistently low estimated noise performance within 0.27% difference to the ideal, four-input material decomposition results for all tested duty cycles in a standard adult-sized 300 mm water phantom. In the pediatric (150 mm) and large adult (400 mm) phantom cases, the two-input weighted schemes were within 1% difference of the ideal four-input noise estimator results on average across all tested duty cycles. This study shows that optimized two-channel weighting in spectral CT matches the accuracy of four-channel setups for material decomposition, reducing noise and computational demands.

BACKGROUND:Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. However, most previous studies of dark-field chest radiographs evaluated the lung signal only in the inspiratory breathing state. PURPOSE:Our work aims to add a new perspective to these previous assessments by locally comparing dark-field lung information between different respiratory states to explore new ways of functional lung imaging based on dark-field chest radiography. METHODS:We use suitable deformable image registration methods for dark-field chest radiographs to establish a mapping of lung areas in distinct breathing states. After registration, we utilize an inter-frame ratio approach to examine the local dark-field signal changes and evaluate the gradient of the craniocaudal axis projections and mean lung field values to draw a quantitative comparison to standard chest radiographs and assess the relationship with the respiratory capacity. RESULTS:Considering full inspiration and expiration scans from a clinical chronic obstructive pulmonary disease study, the registration framework allows to establish an accurate spatial correspondence (Median Dice score 0.95/0.94, mean surface distance 3.71/3.52 mm, and target registration error 6.10 mm) between dark-field chest radiographs in different respiratory states and thus to perform a local signal change analysis. Compared to the utilization of standard chest radiographs, the presented approach benefits from the absence of bone and soft-tissue structures in the dark-field images, which move differently during respiration than the lung tissue. Our quantitative evaluation of the inter-frame ratios demonstrates evidence of craniocaudal gradient-sensitivity advantages concerning the relative vital lung capacity of the study participants in the dark-field images (Spearman correlation coefficients: r s , r i g h t = 0.55 $r_{s,right}=0.55$ , p < 0.01 $p<0.01$ and r s , l e f t = 0.48 $r_{s,left}=0.48$ , p < 0.01 $p<0.01$ compared to the attenuation image-based gradient correlations r s , r i g h t = 0.20 $r_{s,right}=0.20$ , p = 0.16 $p=0.16$ and r s , l e f t = 0.40 $r_{s,left}=0.40$ , p < 0.01 $p<0.01$ ). Moreover, our alternative lung field analysis approach provides insights into the distinct behavior of the dark-field signal changes with the breathing capacity, which are in good agreement with the expected lung volume changes in the respective lung regions. In quantitative terms, this is reflected in a weak Spearman correlation ( r s , upper = 0.30 $r_{s,\mathrm{upper}}=0.30$ , p = 0.01 $p=0.01$ ) of the mean dark-field signal ratio within the upper lung region, but strong correlations within the middle ( r s , middle = 0.71 $r_{s,\mathrm{middle}}=0.71$ , p < 0.01 $p<0.01$ ) and lower ( r s , lower = 0.67 $r_{s,\mathrm{lower}}=0.67$ , p < 0.01 $p<0.01$ ) lung region. CONCLUSIONS:Our regional characterization of lung dark-field signal changes between the breathing states via deformable image registration provides a proof-of-principle that dynamic radiography-based lung function assessment approaches may benefit from considering registered dark-field images in addition to standard plain chest radiographs. This opens up new options for low-dose and rapid lung ventilation assessment via dark-field chest radiography that has the potential to improve lung diagnostics considerably.

Background: Material structures at the micrometer scale cause ultra-small-angle X-ray scattering, e.g., seen in lung tissue or plastic foams. In grating-based X-ray imaging, this causes a reduction of the fringe visibility, forming a dark-field signal. Polychromatic beam hardening also changes visibility, adding a false dark-field signal due to attenuation, even in homogeneous, non-scattering materials. Purpose: The objective of this study is to develop a fast, simple, and robust method to correct dark-field signals and bony structures present due to beam hardening on dark-field chest radiographs of study participants. Methods: The method is based on calibration measurements and image processing. Beam hardening by bones and soft tissue is modeled by aluminum and water, respectively, which have no microstructure and thus only generate an artificial dark-field signal. Look-up tables were then created for both. By using a weighted mean of these, forming a single look-up table, and using the attenuation images, the artificial dark-field signal and thus the bone structures present are reduced for study participants. Results: It was found that applying a correction using a weighted look-up table leads to a significant reduction of bone structures in the dark-field image. The weighting of the aluminum component has a substantial impact on the degree to which bone structures remain visible in the dark-field image. Furthermore, a large negative bias in the dark-field image, dependent on the aluminum weighting, was successfully corrected. Conclusions: The beam-hardening-induced signal in the dark-field images was successfully reduced using the method described. The choice of aluminum weighting to suppress rib structures, as well as the selection of bias correction, should be evaluated based on the specific clinical question.

Dark-field chest radiography substantially reduced reading time without compromising sensitivity for pneumothorax detection compared with conventional radiography in clinical practice.

Dark-field chest radiography is sensitive to the lung alveolar structure. We evaluated the change of dark-field signal between inspiration and expiration. From 2018 to 2020, patients who underwent chest computed tomography (CT) were prospectively enrolled, excluding those with any lung condition besides emphysema visible on CT. Participants were imaged in both inspiration and expiration with a prototype dark-field chest radiography system. We calculated the total dark-field signal ∑DF and the dark-field coefficient ϵ, assumed to be proportional to the total number of alveoli and the alveolar density, respectively. Eighty-eight subjects, aged 64 years ± 11 (mean ± standard deviation), 55 males, were enrolled. Dark-field signal in the lung projection appeared higher in expiration compared to inspiration. Over all participants, ∑DF was higher in inspiration (1.6 × 10-2 ± 0.4 × 10-2 m2) compared to expiration (1.5 × 10-2 ± 0.4 m2) (p < 0.001), with its expiration-to-inspiration not ratio being different for any emphysema subgroup. The dark-field coefficient ϵ was lower in inspiration (2.3 ± 0.6 m-1) compared to expiration (3.1 ± 1.1 m-1) (p < 0.001) over all participants. The dark-field coefficient in inspiration and expiration, as well as their ratio, was lower for at least moderate emphysema when compared to the control group (e.g., ϵ = 2.5 ± 1.0 m-1 for moderate emphysema in expiration versus ϵ = 3.6 ± 0.7 m-1 for participants without emphysema (p = 0.003). The dark-field signal depends on the breathing state. Differences between breathing states are influenced by emphysema severity. The patient’s breathing state influences the dark-field chest radiograph, potentially impacting its diagnostic value.

X-ray computed tomography (CT) is a well-established and frequently used technique in clinical diagnostics because of its non-destructively generated high-resolution images. There have been several refinements and promising new technologies such as dual-energy CT or photon counting detectors which gain even more detailed insights. All these techniques are restricted to the X-rays' attenuation contrast only. Dark-field CT, on the contrary, amplifies the radiological information by accessing indirect information on the tissues microstructure, including e.g., porosity, by utilizing a grating interferometer. In this work, we use ventilated ex-vivo porcine lungs to mimic the clinical use case of dark-field CT realistically. We demonstrate that the system has a sufficient sensitivity for lung imaging and obtain a first benchmark for HUd values of lung tissue.

X-ray computed tomography (CT) is a crucial tool for non-invasive medical diagnosis that uses differences in materials' attenuation coefficients to generate contrast and provide 3D information. Grating-based dark-field-contrast X-ray imaging is an innovative technique that utilizes small-angle scattering to generate additional co-registered images with additional microstructural information. While it is already possible to perform human chest dark-field radiography, it is assumed that its diagnostic value increases when performed in a tomographic setup. However, the susceptibility of Talbot-Lau interferometers to mechanical vibrations coupled with a need to minimize data acquisition times has hindered its application in clinical routines and the combination of X-ray dark-field imaging and large field-of-view (FOV) tomography in the past. In this work, we propose a processing pipeline to address this issue in a human-sized clinical dark-field CT prototype. We present the corrective measures that are applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. This is achieved by identifying spatially and temporally variable vibrations in air reference scans. By translating the found correlations to the sample scan, we can identify and mitigate relevant fluctuation modes for scans with arbitrary sample sizes. This approach effectively eliminates the requirement for sample-free detector area, while still distinctly separating fluctuation and sample information. As a result, samples of arbitrary dimensions can be reconstructed without being affected by vibration artifacts. To demonstrate the viability of the technique for human-scale objects, we present reconstructions of an anthropomorphic thorax phantom.

X-ray dark-field imaging enables a spatially-resolved visualization of ultra-small-angle X-ray scattering. Using phantom measurements, we demonstrate that a material's effective dark-field signal may be reduced by modification of the visibility spectrum by other dark-field-active objects in the beam. This is the dark-field equivalent of conventional beam-hardening, and is distinct from related, known effects, where the dark-field signal is modified by attenuation or phase shifts. We present a theoretical model for this group of effects and verify it by comparison to the measurements. These findings have significant implications for the interpretation of dark-field signal strength in polychromatic measurements.

BACKGROUND:Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray optical gratings to enable small-angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human-scale x-ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefact removal. PURPOSE:Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for training data. METHODS:We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging of patients. RESULTS:The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truth images. CONCLUSIONS:Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinical radiology.