Translation from murine to human lung imaging using x-ray dark field radiography: A simulation study

The unremarkable alveolar epithelial glycocalyx: a thorium dioxide-based electron microscopic comparison after heparinase or pneumolysin treatment

Recent investigations analyzed in depth the biochemical and biophysical properties of the endothelial glycocalyx. In comparison, this complex cell-covering structure is largely understudied in alveolar epithelial cells. To better characterize the alveolar glycocalyx ultrastructure, unaffected versus injured human lung tissue explants and mouse lungs were analyzed by transmission electron microscopy. Lung tissue was treated with either heparinase (HEP), known to shed glycocalyx components, or pneumolysin (PLY), the exotoxin of Streptococcus pneumoniae not investigated for structural glycocalyx effects so far. Cationic colloidal thorium dioxide (cThO₂) particles were used for glycocalyx glycosaminoglycan visualization. The level of cThO₂ particles orthogonal to apical cell membranes (≙ stained glycosaminoglycan height) of alveolar epithelial type I (AEI) and type II (AEII) cells was stereologically measured. In addition, cThO₂ particle density was studied by dual-axis electron tomography (≙ stained glycosaminoglycan density in three dimensions). For untreated samples, the average cThO₂ particle level was ≈ 18 nm for human AEI, ≈ 17 nm for mouse AEI, ≈ 44 nm for human AEII and ≈ 35 nm for mouse AEII. Both treatments, HEP and PLY, resulted in a significant reduction of cThO₂ particle levels on human and mouse AEI and AEII. Moreover, a HEP- and PLY-associated reduction in cThO₂ particle density was observed. The present study provides quantitative data on the differential glycocalyx distribution on AEI and AEII based on cThO₂ and demonstrates alveolar glycocalyx shedding in response to HEP or PLY resulting in a structural reduction in both glycosaminoglycan height and density. Future studies should elucidate the underlying alveolar epithelial cell type-specific distribution of glycocalyx subcomponents for better functional understanding.

The choice of an autocorrelation length in dark-field lung imaging

Respiratory diseases are one of the most common causes of death, and their early detection is crucial for prompt treatment. X-ray dark-field radiography (XDFR) is a promising tool to image objects with unresolved micro-structures such as lungs. Using Talbot-Lau XDFR, we imaged inflated porcine lungs together with Polymethylmethacrylat (PMMA) microspheres (in air) of diameter sizes between 20 and 500 [Formula: see text] over an autocorrelation range of 0.8-5.2 [Formula: see text]. The results indicate that the dark-field extinction coefficient of porcine lungs is similar to that of densely-packed PMMA spheres with diameter of [Formula: see text], which is approximately the mean alveolar structure size. We evaluated that, in our case, the autocorrelation length would have to be limited to [Formula: see text] in order to image [Formula: see text]-thick lung tissue without critical visibility reduction (signal saturation). We identify the autocorrelation length to be the critical parameter of an interferometer that allows to avoid signal saturation in clinical lung dark-field imaging.

Guidelines for Micro-Computed Tomography Analysis of Rodent Dentoalveolar Tissues

Micro–computed tomography (μCT) has become essential for analysis of mineralized as well as nonmineralized tissues and is therefore widely applicable in the life sciences. However, lack of standardized approaches and protocols for scanning, analyzing, and reporting data often makes it difficult to understand exactly how analyses were performed, how to interpret results, and if findings can be broadly compared with other models and studies. This problem is compounded in analysis of the dentoalveolar complex by the presence of four distinct mineralized tissues: enamel, dentin, cementum, and alveolar bone. Furthermore, these hard tissues interface with adjacent soft tissues, the dental pulp and periodontal ligament (PDL), making for a complex organ. Drawing on others' and our own experience analyzing rodent dentoalveolar tissues by μCT, we introduce techniques to successfully analyze dentoalveolar tissues with similar or disparate compositions, densities, and morphological characteristics. Our goal is to provide practical guidelines for μCT analysis of rodent dentoalveolar tissues, including approaches to optimize scan parameters (filters, voltage, voxel size, and integration time), reproducibly orient samples, define regions and volumes of interest, segment and subdivide tissues, interpret findings, and report methods and results. We include illustrative examples of analyses performed on genetically engineered mouse models with phenotypes in enamel, dentin, cementum, and alveolar bone. The recommendations are designed to increase transparency and reproducibility, promote best practices, and provide a basic framework to apply μCT analysis to the dentoalveolar complex that can also be extrapolated to a variety of other tissues of the body. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC. on behalf of American Society for Bone and Mineral Research.

Contrast-to-noise ratios and thickness-normalized, ventilation-dependent signal levels in dark-field and conventional in vivo thorax radiographs of two pigs

Lung tissue causes significant small-angle X-ray scattering, which can be visualized with grating-based X-ray dark-field imaging. Structural lung diseases alter alveolar microstructure, which often causes a dark-field signal decrease. The imaging method provides benefits for diagnosis of such diseases in small-animal models, and was successfully used on porcine and human lungs in a fringe-scanning setup. Micro- and macroscopic changes occur in the lung during breathing, but their individual effects on the dark-field signal are unknown. However, this information is important for quantitative medical evaluation of dark-field thorax radiographs. To estimate the effect of these changes on the dark-field signal during a clinical examination, we acquired in vivo dark-field chest radiographs of two pigs at three ventilation pressures. Pigs were used due to the high degree of similarity between porcine and human lungs. To analyze lung expansion separately, we acquired CT scans of both pigs at comparable posture and ventilation pressures. Segmentation, masking, and forward-projection of the CT datasets yielded maps of lung thickness and logarithmic lung attenuation signal in registration with the dark-field radiographs. Upon correlating this data, we discovered approximately linear relationships between the logarithmic dark-field signal and both projected quantities for all scans. Increasing ventilation pressure strongly decreased dark-field extinction coefficients, whereas the ratio of lung dark-field and attenuation signal changed only slightly. Furthermore, we investigated ratios of dark-field and attenuation noise levels at realistic signal levels via calculations and phantom measurements. Dark-field contrast-to-noise ratio (CNR) per lung height was 5 to 10% of the same quantity in attenuation. We conclude that better CNR performance in the dark-field modality is typically due to greater anatomical noise in the conventional radiograph. Given the high physiological similarity of human and porcine lungs, the presented thickness-normalized, ventilation-dependent values allow estimation of dark-field activity of human lungs of variable size and inspiration, which facilitates the design of suitable clinical imaging setups.