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.

X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle scattering. However, the limited availability of such data poses challenges for developing robust deep learning models. In this work, we present the first framework for generating dark-field images directly from standard attenuation chest X-rays using an Uncertainty-Guided Progressive Generative Adversarial Network. The model incorporates both aleatoric and epistemic uncertainty to improve interpretability and reliability. Experiments demonstrate high structural fidelity of the generated images, with consistent improvement of quantitative metrics across stages. Furthermore, out-of-distribution evaluation confirms that the proposed model generalizes well. Our results indicate that uncertainty-guided generative modeling enables realistic dark-field image synthesis and provides a reliable foundation for future clinical applications.

We present a structured-illumination technique for full-field super-resolution transmission X-ray microscopy, which employs Fourier spectral decomposition inspired by established methods in visible-light microscopy. A 2D grating creating this illumination is stepped across one period to acquire a set of images at unique illumination positions. The Fourier domain of each image is described as a linear combination of replicated sample information at each frequency harmonic. As this superposition is created independently of detection, it contains spatial information exceeding native detector resolution. Recovering the encoded high-frequency components enables the population of an expanded frequency space. We demonstrate the presence of additional sample information in the Fourier spectrum and introduce a method to recover it. We achieve a resolution improvement by a factor of 2.1 for the projection image of a resolution test pattern. We further demonstrate seamless integration into standard X-ray tomography acquisition schemes. The acquisition is inherently multimodal, as phase-contrast and dark-field images can be computed from the same data using methods such as unified modulated pattern analysis, while providing an additional super-resolved transmission channel. These results indicate broad potential for non-destructive testing and biomedical imaging, as they alleviate pixel-size limitations in photon-counting detectors and sample-size restrictions imposed by optical magnification.

Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However, safe translation to the clinic will require a substantial radiation dose reduction, which inevitably increases image noise. Supervised convolutional-neural-network-based denoising can restore image quality but depends on paired low- and high-dose datasets, which are rarely available in practice. Self-supervised methods avoid this limitation, yet most are not well adapted to the inverse problem of PBI computed tomography (CT). We introduce Neighbor2Inverse, a self-supervised denoising framework designed for low-dose PBI-CT that generalizes to clinical CT. Building on the Neighbor2Neighbor principle, each noisy projection is subsampled into two variants that preserve structural information but contain independent noise realizations. These are reconstructed separately, and the resulting pairs are used to train a denoising network directly in the image domain. We benchmark the proposed method against established analytical and self-supervised denoising approaches. In region-of-interest PBI CT experiments, Neighbor2Inverse achieves superior noise suppression while preserving fine structural details, as demonstrated by improved contrast-to-noise ratio, spatial resolution, and composite image quality metrics. Competitive performance is also observed on clinical CT data under simulated low-dose conditions. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Code, data, and interactive figures are available at https://github.com/J-3TO/Neighbor2Inverse.

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.

Inverse Compton X-ray sources are laboratory-scale devices providing quasi-monochromatic synchrotron radiation which is generated by laser photons Compton-scattering off highly relativistic electrons. Since the shape and width of the X-ray spectrum are determined by the properties of the colliding beams, these must be carefully optimised. However, device compactness limits the space for diagnostics, rendering a complete characterisation challenging, especially if an electron storage ring is combined with a laser enhancement cavity. Here, a framework for laser, electron and X-ray beam parameter determination is proposed to address this issue. First, methods for determining the laser- and X-ray parameters are presented. Knowing these, electron beam parameters are retrieved from the shape of the X-ray spectrum. To this end, an analytical physical model enabling a rapid calculation of inverse Compton scattering spectra is developed and combined with a genetic algorithm. This strategy's effectiveness is demonstrated by applying the concept at the Munich Compact Light Source, a storage ring-based inverse Compton X-ray source facility. Since the analytical model is computationally very inexpensive, the proposed framework could enable real-time monitoring of inverse Compton X-ray sources or be used as a non-invasive diagnostic based on a single spectrum for the electron beam emittance of storage rings or accelerators.

Background This study aims to construct and implement an experimental radioluminescence microscopy (RLM) setup using a standard light microscope to visualize tumor organoids with high resolution. This approach seeks to address the challenges of imaging organoids, facilitating advancements in precision medicine for cancer research. Tumor organoids grown in collagen were incubated with 100 MBq to 120 MBq of 18F-fluorodeoxyglucose to visualize metabolic activity within their structures. Imaging was performed using a sensitive electron-multiplying charge-coupled device in conjunction with a scintillator crystal and an optical system with a high light collection efficiency and adapted magnification.Results The constructed RLM setup demonstrated high-resolution imaging of organoid samples, significantly exceeding the resolution achievable with positron-emission tomography. The combination of RLM with brightfield and fluorescence microscopy provided comprehensive morphologic and metabolic characterization of the organoids at sub-organoid spatial resolution.Conclusion The study successfully illustrates the feasibility of using a modified light microscope for RLM to achieve imaging of tumor cell metabolism in organoids with a spatial resolution on the order of tens of micrometers. This approach opens new avenues for studies of tumor cell metabolism during tumor progression and during therapeutic interventions.

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.

Grating interferometry is a promising diagnostic technique that enables simultaneous acquisition of three complementary, synergistic X-ray images: transmission, differential phase, and dark-field. Its key advantage over other setups is its ability to use large pixels and, hence, large-area detectors, as well as its compatibility with low-coherence, compact X-ray sources, both of which are key factors for human-scale imaging. It has already demonstrated strong potential for chest imaging applications, including the diagnosis of pulmonary emphysema, fibrosis, and cancer. To retrieve transmission, differential phase, and dark-field images from data, an algorithm is required to separate the distinct mechanisms contributing to measured contrast. Since its realization, this image-retrieval step has remained fundamentally unchanged. In this work, we develop a novel transmission- and dark-field retrieval algorithm for grating-interferometry derived from the X-ray Fokker-Planck equation. To demonstrate and validate our Fokker-Planck algorithm, we apply it to experimental measurements of a test sample and to data from a mouse chest acquired with varying exposure times and added Poisson noise. The retrieved images were qualitatively and quantitatively compared with those retrieved using a conventional sinusoidal-fitting approach. Across both samples, the Fokker–Planck method produced images consistent with conventional retrieval, with a comparable signal-to-noise ratio. Notably, our Fokker-Planck method suppresses artefacts arising in the conventional approach under grating perturbations (e.g., structural defects like scratches) and reduced flux or visibility, yielding smoother and more reproducible images. Additionally, we demonstrate that our Fokker-Planck method has an advantage over the conventional dark-field retrieval method for fast sample imaging with short exposure times and high noise.

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 retrospective study evaluates U-Net-based artifact reduction for dose-reduced sparse-sampling CT (SpSCT) in terms of image quality and diagnostic performance using a reader study and automated detection. CT pulmonary angiograms from 89 patients were used to generate SpSCT data with 16 to 512 views. Twenty patients were reserved for a reader study and test set, the remaining 69 were used to train (53) and validate (16) a dual-frame U-Net for artifact reduction. U-Net post-processed images were assessed for image quality, diagnostic performance, and automated pulmonary embolism (PE) detection using the top-performing network from the 2020 RSNA PE detection challenge. Statistical comparisons were made using two-sided Wilcoxon signed-rank and DeLong two-sided tests. Post-processing with the dual-frame U-Net significantly improved image quality in the internal test set, with a structural similarity index of 0.634/0.378/0.234/0.152 for FBP and 0.894/0.892/0.866/0.778 for U-Net at 128/64/32/16 views, respectively. The reader study showed significantly enhanced image quality (3.15 vs. 3.53 for 256 views, 0.00 vs. 2.52 for 32 views), increased diagnostic confidence (0.00 vs. 2.38 for 32 views), and fewer artifacts across all subsets (P < 0.05). Diagnostic performance, measured by the Sørensen–Dice coefficient, was significantly better for 64- and 32-view images (0.23 vs. 0.44 and 0.00 vs. 0.09, P < 0.05). Automated PE detection was better at fewer views (64 views: 0.77 vs. 0.80, 16 views: 0.59 vs. 0.80), although the differences were not statistically significant. U-Net-based post-processing of SpSCT data significantly enhances image quality and diagnostic performance, supporting substantial dose reduction in CT pulmonary angiography.

This study aims to investigate the effect of various beam geometries and dimensions of input data on the sparse-sampling streak artifact correction task with U-Nets for clinical CT scans as a means of incorporating the volumetric context into artifact reduction tasks to improve model performance. A total of 22 subjects were retrospectively selected (01.2016-12.2018) from the Technical University of Munich's research hospital, TUM Klinikum rechts der Isar. Sparsely-sampled CT volumes were simulated with the Astra toolbox for parallel, fan, and cone beam geometries. 2048 views were taken as full-view scans. 2D and 3D U-Nets were trained and validated on 14, and tested on 8 subjects, respectively. For the dimensionality study, in addition to the 512x512 2D CT images, the CT scans were further pre-processed to generate a so-called '2.5D', and 3D data: Each CT volume was divided into 64x64x64 voxel blocks. The 3D data refers to individual 64-voxel blocks. An axial, coronal, and sagittal cut through the center of each block resulted in three 64x64 2D patches that were rearranged as a single 64x64x3 image, proposed as 2.5D data. Model performance was assessed with the mean squared error (MSE) and structural similarity index measure (SSIM). For all geometries, the 2D U-Net trained on axial 2D slices results in the best MSE and SSIM values, outperforming the 2.5D and 3D input data dimensions.

Knowledge of a detection system's point-spread function (PSF) allows improving image resolution by deconvolving this PSF. The slanted-edge or Siemens-star approaches are commonly used to retrieve the PSF. The latter retrieves the PSF with a poor angular resolution and requires an intricate, sometimes expensive test pattern. The former provides the line-spread function only. Rotating this edge, the PSF could be retrieved in a tedious and time-consuming procedure. Other alternatives are line-pair resolution test charts or point-like light sources, e.g., tiny pinholes or fluorescent beads, which suffer from long acquisition times either due to the need for pattern rotation or very low flux. Here, a single-shot method is presented to retrieve the complete two-dimensional PSF by employing a circular aperture and a back-projection approach similar to computed tomography, which overcomes the issues mentioned above. Additionally, the accuracy of the PSF determination is improved by integrating a sub-pixel-resolution approach. Furthermore, simulations are employed to analyze the method's susceptibility to noise and to assess its intrinsic accuracy. Lastly, an X-ray detector assembly is characterized with this method to showcase the detailed insights into the system's aberrations that can be obtained. Since this technique is not restricted to the X-ray regime, it can be applied to characterize detector systems in other regions of the electromagnetic spectrum. This enables the method's widespread use in the imaging community.

K-edge subtraction (KES) imaging provides material-specific information exploiting the steep increase in attenuation at an absorption edge. Classically, two monochromatic images acquired with energies right above and below the K-edge are subtracted weighting one image with the approximate energy scaling of the photoelectric effect. This technique was transferred to quasi-monochromatic inverse Compton X-ray sources by implementing a filter made of the investigated material itself, yet at the cost of a reduced contrast-to-noise ratio. While switching between two spectra with a larger energy separation overcomes this issue, the X-ray energy must be changed on the timescale of seconds for this to be used in practical applications. To this end, a framework for rapid X-ray energy switching of inverse Compton X-ray sources is developed and experimentally realised. In case of a larger energy separation, the Compton contribution to the attenuation leads to artifacts using classical KES as it decreases the effective energy scaling. A theoretical model for calculating the reduced scaling factor is derived to overcome this issue. Image improvement by this optimised KES approach is experimentally demonstrated.

Purpose To estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs on a pixel level using lung thickness maps generated by a U-Net deep learning model. Materials and Methods This retrospective study included 5959 chest CT scans from two public datasets, the Lung Nodule Analysis 2016 (Luna16) (n = 656) and the Radiological Society of North America Pulmonary Embolism Detection Challenge 2020 (n = 5303). Additionally, 72 participants were selected from the Klinikum Rechts der Isar dataset (October 2018 through December 2019), each with a corresponding chest radiograph obtained within 7 days. Synthetic radiographs and lung thickness maps were generated using forward projection of CT scans and their lung segmentations. A U-Net model was trained on synthetic radiographs to predict lung thickness maps and estimate TLV. Model performance was assessed using mean squared error (MSE), Pearson correlation coefficient, and two-sided Student t distribution. Results The study included 72 participants (45 male and 27 female participants; 33 healthy participants: mean age, 62 years [range, 34-80 years]; 39 with chronic obstructive pulmonary disease: mean age, 69 years [range, 47-91 years]). TLV predictions showed low error rates (MSEPublic-Synthetic, 0.16 L2; MSEKRI-Synthetic, 0.20 L2; MSEKRI-Real, 0.35 L2) and strong correlations with CT-derived reference standard TLV (nPublic-Synthetic, 1191; r = 0.99; P < .001) (nKRI-Synthetic, 72; r = 0.97; P < .001) (nKRI-Real, 72; r = 0.91; P < .001). When evaluated on different datasets, the U-Net model achieved the highest performance for TLV estimation on the Luna16 test dataset, with the lowest MSE (0.09 L2) and strongest correlation (r = 0.99; P < .001) compared with CT-derived TLV. Conclusion The U-Net-generated pixel-level lung thickness maps successfully estimated TLV for both synthetic and real radiographs. Keywords: Frontal Chest Radiographs, Lung Thickness Map, Pixel-Level, Total Lung Volume, U-Net Supplemental material is available for this article. © RSNA, 2025.

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.