OBJECTIVE: To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention.
METHODS: The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source\'s production over time, corresponding author\'s countries, and a treemap of authors\' keywords.
RESULTS: A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the \"European Radiology\" (181 articles, h-index = 46), followed by the \"European Journal of Radiology\" (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest.
CONCLUSIONS: Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future.
UNASSIGNED: This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice.
CONCLUSIONS: Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications.

This paper investigates the impact of element size on the microstructure of the steel-concrete interface in self-compacting concrete (SCC). Experiments were conducted on two types of test elements: a deep beam measuring 1440 × 640 × 160 mm and a wall element measuring 2240 × 1600 × 160 mm. The SCC mix was consistently pumped from the top, using a single casting point located near the formwork\'s edge. Horizontal steel ribbed rebars with a diameter of 16 mm were embedded in these elements. X-ray computed tomography (CT) was employed to provide three-dimensional insights into the microstructure of the rebar-to-concrete interface. An analysis of X-ray CT images from core samples revealed that the microstructure of this interface is influenced by the distance of the specimen from the mix casting point and its vertical position within the element. The combined effects of bleeding, air-pore entrapment, and plastic settlement within the SCI were observed under the top rebars. Their extent was independent of the type of element analyzed, suggesting that the deterioration of the SCI is related to the distance from the top surface of the element. These results elucidate phenomena occurring during the fresh state of concrete near reinforcing bars and their implications for bond properties. To date, some of the standards differentiate between bond conditions according to the depth of concrete beneath the rebar. In the view of the studies, this approach may be unduly rigorous. The findings offer valuable guidance for reinforced concrete execution and design.

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The two-phase seepage fluid (i.e., air and water) behaviors in undisturbed granite residual soil (U-GRS) have not been comprehensively studied due to a lack of accurate and representative models of its internal pore structure. By leveraging X-ray computed tomography (CT) along with the lattice Boltzmann method (LBM) enhanced by the Shan-Chen model, this study simulates the impact of internal pore characteristics of U-GRS on the water-gas two-phase seepage flow behaviors. Our findings reveal that the fluid demonstrates a preference for larger and straighter channels for seepage, and as seepage progresses, the volume fraction of the water/gas phases exhibits an initial increase/decrease trend, eventually stabilizing. The results show the dependence of two-phase seepage velocity on porosity, while the local seepage velocity is influenced by the distribution and complexity of the pore structure. This emphasizes the need to consider pore distribution and connectivity when studying two-phase flow in undisturbed soil. It is observed that the residual gas phase persists within the pore space, primarily localized at the pore margins and dead spaces. Furthermore, the study identifies that hydrophobic walls repel adjacent fluids, thereby accelerating fluid movement, whereas hydrophilic walls attract fluids, inducing a viscous effect that decelerates fluid flow. Consequently, the two-phase flow rate is found to increase with then-enhanced hydrophobicity. The apex of the water-phase volume fraction is observed under hydrophobic wall conditions, reaching up to 96.40%, with the residual gas-phase constituting 3.60%. The hydrophilic wall retains more residual gas-phase volume fraction than the neutral wall, followed by the hydrophobic wall. Conclusively, the investigations using X-ray CT and LBM demonstrate that the pore structure characteristics and the wettability of the pore walls significantly influence the two-phase seepage process.

OBJECTIVE: This study developed and validated a nomogram utilizing clinical and multi-slice spiral computed tomography (MSCT) features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma. Additionally, we assessed the predictive accuracy of Ki-67 expression levels, as determined by our model, in estimating the prognosis of stage IA lung adenocarcinoma.
METHODS: We retrospectively analyzed data from 395 patients with pathologically confirmed stage IA lung adenocarcinoma. A total of 322 patients were divided into training and internal validation groups at a 6:4 ratio, whereas the remaining 73 patients composed the external validation group. According to the pathological results, the patients were classified into high and low Ki-67 labeling index (LI) groups. Clinical and CT features were subjected to statistical analysis. The training group was used to construct a predictive model through logistic regression and to formulate a nomogram. The nomogram\'s predictive ability and goodness-of-fit were assessed. Internal and external validations were performed, and clinical utility was evaluated. Finally, the recurrence-free survival (RFS) rates were compared.
RESULTS: In the training group, sex, age, tumor density type, tumor-lung interface, lobulation, spiculation, pleural indentation, and maximum nodule diameter differed significantly between patients with high and low Ki-67 LI. Multivariate logistic regression analysis revealed that sex, tumor density, and maximum nodule diameter were significantly associated with high Ki-67 expression in stage IA lung adenocarcinoma. The calibration curves closely resembled the standard curves, indicating the excellent discrimination and accuracy of the model. Decision curve analysis revealed favorable clinical utility. Patients with a nomogram-predicted high Ki-67 LI exhibited worse RFS.
CONCLUSIONS: The nomogram utilizing clinical and CT features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma demonstrated excellent performance, clinical utility, and prognostic significance, suggesting that this nomogram is a noninvasive personalized approach for the preoperative prediction of Ki-67 expression.

Water imbibition is an important process in reservoir rocks during hydraulic fracturing and water-based enhanced oil recovery operations. However, the water imbibition behavior in tight sandstones has not been fully understood due to their complex pore structure, including the presence of nano and micron-sized pores and throats, surface properties, and wide variation in mineralogy. The present study focuses on the effect of spontaneous water imbibition on the porosity evolution of a tight sandstone. Within this context, a core of Torrey Buff sandstone was characterized by using a combination of multiscale imaging methods (X-ray Computed Tomography, Scanning Electron Microscopy), laboratory experiments (porosity-permeability measurements), and analytical techniques (X-ray Diffraction, Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy-Energy Dispersive Spectroscopy, and Thermogravimetry). The studied tight sandstone core has a porosity of 12.3 % and permeability of 2.05mD with minerals of quartz (58 %), clays (kaolinite and illite, 23 %), K-feldspar (7 %), dolomite (7 %) and calcite (5 %). Primary and secondary pores, ranging in size from 60 to 140 μm and 30-50 μm, respectively, are mostly filled with highly-soluble carbonate minerals and hydrophilic illite, which influence the spontaneous water imbibition capacity of the tight sandstone. The multiscale imaging technique indicates that after a 10-h long water imbibition experiment, the average pore size of the tight sandstone increased by 1.28 %, reaching 2.35 % at the rock-water contact and 0.13 % at the top of the core. In other words, throughout the core, the porosity changes upon water imbibition are not uniform but show an almost linear trend. This observation could be explained by the significant contribution of highly-soluble carbonates and hydrophilic illite on the microstructure of the tight sandstone. This study implies that multiscale imaging techniques, crucial in examining spontaneous water imbibition, hold promise for further research in enhanced oil recovery or hydraulic fracking in tight sandstones.

Lattice structures have become an innovative solution for the improvement of part design, as they are able to substitute solid regions, maintain mechanical capabilities, and reduce material usage; however, dimensional quality control of these geometries is challenging. X-ray computed tomography (XCT) is the most suitable non-destructive metrological technique as it is capable of characterizing internal features and hidden elements. Uncertainty estimation of XCT is still in development, and studies typically use high-resolution calibrated devices such as focal variation microscopes (FVMs) as a reference, focusing on certain parts of the lattice but not the whole structure. In this paper, an estimation of the accuracy of XCT evaluation of a complete lattice structure in comparison to a higher-resolution reference device (FVM) is presented. Experimental measurements are taken on ad hoc designed test objects manufactured in polyamide 12 (PA12) using selective laser sintering (SLS), optimized for the evaluation on both instruments using different cubic-based lattice typologies. The results confirm higher precision on XCT evaluation in both qualitative and quantitative analysis. Even with a lower resolution, XCT is able to characterize details of the surface such as re-entrant features; as well, standard deviations and uncertainties in strut diameter evaluation remain more stable in all cells in XCT, identifying on the other hand reconstruction problems on FVM measurements. Moreover, it is shown that, using XCT, no additional evaluation errors were found in inner cells, suggesting that the measurement of external elements could be representative of the whole structure for metrological purposes.

BACKGROUND: To determine the predictive value of interstitial lung abnormalities (ILA) for epidermal growth factor receptor (EGFR) mutation status and assess the prognostic significance of EGFR and ILA in patients with non-small cell lung cancer (NSCLC).
METHODS: We reviewed 797 consecutive patients with a histologically proven diagnosis of primary NSCLC from January 2013 to October 2018. Of these, 109 patients with NSCLC were found to have concomitant ILA. Multivariate logistic regression analysis was used to identify the significant clinical and computed tomography (CT) findings in predicting EGFR mutations. Cox proportional hazard models were used to identify significant prognostic factors.
RESULTS: EGFR mutations were identified in 22 of 109 tumors (20.2%). Multivariate analysis showed that the models incorporating clinical, tumor CT and ILA CT features yielded areas under the receiver operating characteristic curve (AUC) values of 0.749, 0.838, and 0.849, respectively. When combining the three models, the independent predictive factors for EGFR mutations were non-fibrotic ILA, female sex, and small tumor size, with an AUC value of 0.920 (95% confidence interval[CI]: 0.861-0.978, p < 0.001). In the multivariate Cox model, EGFR mutations (hazard ratio = 0.169, 95% CI = 0.042-0.675, p = 0.012; 692 days vs. 301 days) were independently associated with extended overall survival compared to the wild-type.
CONCLUSIONS: Non-fibrotic ILA independently predicts the presence of EGFR mutations, and the presence of EGFR mutations rather than non-fibrotic ILA serves as an independent good prognostic factor for patients with NSCLC.

In X-ray computed tomography (CT) reconstructions of gas diffusion layers (GDLs), grayscale threshold selection is a critical issue. Although various selection methods exist, they all have their own drawbacks. This study investigates the influence of grayscale threshold on GDL properties and compares Otsu and porosity-adaptive thresholds. We utilized X-ray CT to reconstruct a Toray carbon paper sample (TGP-H-060) at a resolution of 2 μm. Using reconstructed 3D models generated under different grayscale thresholds, we performed structural analysis, computational fluid dynamics simulation, and compression simulation. We subsequently calculated porosity, tortuosity, permeability, and macroscopic stress-strain relationships, quantitatively analyzing the sensitivity of these parameters to the change of grayscale threshold. The results indicated that small change in the grayscale threshold can significantly impact the transport and mechanical properties of reconstructed GDLs. The difference between Otsu and porosity-adaptive thresholds is notable, and the porosity-adaptive threshold appears to be less accurate than the Otsu threshold.

UNASSIGNED: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).
UNASSIGNED: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the \"One-vs-Rest\" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.
UNASSIGNED: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary \"One-vs-Rest\" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively.
UNASSIGNED: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.