X-ray Computed Tomography Research Articles

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic and ex-vivo phantoms

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

A multi-technique approach to unveil the composition and fabrication of a pre-Roman glass masterpiece: a double-faced human-head shape polychrome glass pendant (2nd -1st c. BC)

Pre-Roman glass craftsmanship reached its summit with the development of complex polychrome glass beads, being the Phoenician glass pendants the most exquisite and elaborate example. The uniqueness and complexity of such findings could reveal key information for the understanding of the production and trade of glass pieces at that age. However, these findings have practically never been studied from a physic-chemical perspective. In this work, a remarkable polychrome glass pendant (2nd -1st c. BC) found at the archaeological site of Pintia (Padilla de Duero, Valladolid, Spain) is studied by a multi-analytical non-destructive approach, employing X-ray tomography to understand its fabrication procedure, as well as X-ray fluorescence (XRF) and Raman spectroscopy, both employed in microscopic mode, to determine the composition of each glass employed in its fabrication. The outstanding preservation state and well-defined archaeological context of this glass pendant offered a unique opportunity to expand the understanding of pre-Roman glass pieces, while the combination of the experimental techniques employed provided the first complete and detailed study of a Phoenician glass pendant. The fabrication procedure of the pendant has been identified step-by-step, showing evidence of the use of pre-made pieces for the eyes, as well as hints of its fabrication in a secondary workshop. Moreover, the microchemical analysis of the vividly colored glasses by XRF and Raman spectroscopy revealed a composition compatible with the use of natron as fluxing agent, typical of Phoenician glass, the presence of surface alterations corresponding to carbonatation processes, as well as the nature of the employed chromophores or pigments: Mn, Cu, and Co for the blue, Fe-S for the black, CaSb2O7 and CaSb2O7 + TiO2 for two diverse white glasses, and a pyrochloric triple oxide (Pb2Sb2 − xSnxO7−x/2) and lead oxides for the yellow. Remarkably, the use of pyrochloric triple oxides as yellow pigments has scarcely been previously reported at that age. Finally, the identification by Raman spectroscopy of CaSb2O7 and the β-phase of CaSiO3, as well as the Raman spectra features of the glass matrix corresponding to the blue glass, indicated maximum firing temperatures below 1100 °C.

Effects of T-2 and deoxynivalenol mycotoxins on mouse spinal bone growth and integrity

Kashin-Beck Disease (KBD), an osteoarticular disorder, is influenced by various factors, including exposure to Deoxynivalenol (DON) and T-2 mycotoxins. This study systematically explored the impact of these mycotoxins on the development and structural resilience of spinal structures in mice, examining both isolated and combined effects. The experiment involved 72 male mice divided into nine groups, each subjected to varying concentrations of T-2, DON, or their combinations over four weeks. Rigorous monitoring included body weight, key indicators of bone metabolism, and cellular activities essential to bone health. Comprehensive evaluations using biomechanical analysis, x-ray, and micro-computed tomography (micro-CT) were conducted to assess alterations in spinal structure.The findings revealed a pivotal aspect: mice exhibited a dose-dependent decline in body weight when exposed to individual mycotoxins, while simultaneous exposure produced an unanticipated antagonistic effect. Moreover, decreases were noted in levels of calcium, phosphorus, and vitamin D, coupled with changes in the activities of osteoblasts (increased) and osteoclasts (decreased), all intricately tied to the toxins' dosages and combinations. Notably, variations in the biomechanical properties corresponded with the mycotoxin dosage and blend, showing a decline in biomechanical strength. Micro-CT analyses further substantiated the profound toxic impact of the toxin dosage and mixtures on both the cortical and trabecular components of the spinal structures.In summary, this investigation unequivocally illuminates the dose- and ratio-dependent deleterious impacts of DON and T-2 mycotoxins on the growth and structural soundness of spinal structures in mice. These findings highlight the urgent need for a comprehensive understanding of the potential hazards these toxins pose to bone health, providing invaluable guidance for future toxicological research and public health strategies.

Toward the Clinical Translation of Safe Intravenous Long Circulating Iodinated Lipid Nanoemulsion Contrast Agents for CT Imaging.

Current clinical small molecule x-ray CT agents are effective but pose risks such as nephrotoxicity, short blood circulation time, limiting scan durations, potential thyroid impact, and immune responses. These challenges drive the development of kidney-safe x-ray nanoparticle (NP)-based contrast agents (CAs), though translation to clinical practice is hindered by chemical complexities and potential toxicity. We have engineered an intravenous, injectable, and safe blood pool NP-based CT CAs at a clinical-equivalent dose of ∼300 mgI/kg (∼2 mL/kg), ideal for vascular and hepatic imaging which are limited by clinical agents. Our iodinated lipid nanodroplet emulsions (ILNEs) contrast agent offers high x-ray attenuation thus improved contrast enhancement, extended stability, and exceptional batch-to-batch consistency. It also boasts a straightforward and scalable manufacturing process with minimal protein interaction, prolonged blood residency (∼4h), and hepatic clearance within 3 days, avoiding nephrotoxicity. Studies in vitro, in mice, and 16.6kg porcine animal model studies confirm its safety, cytocompatibility, and absence of tissue damage. Blood, and thyroid-stimulating hormone (TSH) analyses, and kidney and liver function tests, also support further toxicity evaluations for clinical translation.

Functional performance of composite nonwoven fabrics having varying packing density of constituent fabrics under different operating conditions

In this study, two types of composite nonwoven fabrics having gradient and inverse gradient of packing density of constituent fabrics were developed and tested for their filtration performance at different operating conditions. These composite nonwoven fabrics were produced by using sequential punching technique by varying the punch densities of the constituent fabrics. Box–Behnken three factors three level design was used, and air velocity, weight of dust fed, and operating time were considered as variables. An instrument for gauging the filtration performance of the composite nonwoven filters was designed and fabricated. The packing density of nonwoven fabrics was measured by using the X-ray computed tomography (XCT) technique. The results depicted those composite nonwoven fabrics having inverse gradient of packing density of constituent fabrics outperformed the composite nonwoven fabrics having gradient of packing density in terms of filtration performance with variable operating conditions.

Deep learning-based microstructure analysis of multi-component heterogeneous composites during preparation

Monitoring microstructure evolution during the preparation has always been a difficult problem in the modification studies of SiC composite matrix. Here, we used X-ray tomography microscopy to observe the microstructure of SiCf/SiC-W-ZrB2 composites at different fabrication stage. Based on deep learning, the tracking of the densification process of matrix-modified SiCf/SiC composites was achieved and its suitability for microstructure reconstruction was also verified. The results showed that the average errors of reconstructed SiCf/SiC, pore and Metal (W/ZrB2) are respectively 7.53%, 8.31% and 0.96% by comparison with the segmentation results. Compared with the experimental results, the average error and the average relative error of reconstructed SiCf/SiC is less than 3% and 3.74%.

TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging

BackgroundThe use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739–57. https://doi.org/10.1093/plphys/kiab311.). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties.ResultsWe developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z.) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:40{0}^{3}$$\\end{document} on a desktop workstation. Our software and test dataset are freely distributed on Github.ConclusionsTopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.

3D in situ observation of the capillary infiltration of molten silicon in a SiC/SiC composite by X-ray tomography

The Liquid Silicon Infiltration (LSI) process is used to decrease residual porosity of SiC/SiC composite materials. However, it is not fully mastered since the mechanisms involved at 1500 °C under high vacuum are complex to analyze, especially without direct observation. Previous work had demonstrated the feasibility of using X-ray radiography to observe the front rise of silicon in a SiC/SiC composite material during the LSI process. 2D observations, as a first approach, could give a basic understanding of the mechanisms but raised several interrogations due to the superposition of these phenomena along the thickness preventing any quantification. The setup has been improved in order to make X-ray tomography using a fully integrated DC motor in place of the rotation stage commonly used. The sets of X-ray tomographs confirm two successive fillings. First, the molten silicon rapidly and non uniformly invades the accessible intergranular micro porosity of the powder. Then, the liquid slowly fills the remaining isolated powder areas. Once the SiC matrix is fully saturated, the liquid fills the bigger porosities such as cracks and intra yarn macro porosities. In addition, this 3D analysis enabled to give a better comprehension of the non uniform wetting front in the SiC matrix powder. During the 1st step, the accessibility of the powder was known to have a major effect on the speed of progression. Also, the cracks network plays a key role in the filling of the isolated areas in the powder matrix.

Characterization of Pore Structure and Fluid Mobility of Shale Reservoirs.

Accompanying the commercial exploitation of shale oil and gas in North America, shale oil has gradually become an important resource, sparking great interest among countries around the world in recent years. In this study, focusing on the Paleogene Shahejie Formation in Bohai Bay (Eastern China), techniques such as CT, nitrogen adsorption, mercury injection capillary pressure (MICP), and nuclear magnetic resonance (NMR) were used to characterize the pore structure and mobility of the shale reservoir. Based on the X-ray CT data, the pore radius of the shale reservoir is in the range 0.5-65 μm, and the pore coordination number is concentrated in the range of 1-4. The shale reservoir is poorly connected. The minimum size of the unit body for establishing the digital core model is 380 μm. Based on the experimental data of nitrogen adsorption and MICP, the pores of shale in the study area are mainly classified as ink-bottle-shaped pores, transition-shaped pores, and flat plate slit-shaped pores. The specific surface area and volume of pores are mainly attributed to meso- and macropores. The movable fluid saturation of shale is distributed from 23.59 to 44.42%, the pore throat radius is distributed from 0.001 to 6 μm, and the lower limit of the movable pore throat radius of shale is distributed between 9.0 and 20.1 nm. The movable fluid porosity is mainly distributed between 0.84 and 4.08%, with an average movable fluid porosity of 2.37%. The findings provide a theoretical basis for the efficient development of shale oil resources.

High-performance deep learning segmentation for non-destructive testing of X-ray tomography

For non-destructive testing (NDT) with X-ray computed tomography (XCT), the complex morphological segmentation of homogeneous defects is impossible for traditional threshold-based segmentation algorithm, due to their same absorbances. A variety of defect segmentation using convolutional neural networks (CNNs) has been suitable for such problems, and its accuracy is determined by image quality and comprehensiveness of training set. To achieve a high-performance measurement for quantitative defect evaluation, a modified U-Net-based segmentation model of XCT was proposed suitable for defects with various image quality resulting from different experimental efficiency. The dataset consisting of 24 subsets was acquired from the condition-varying measurements of aluminum alloy samples produced by laser 3D metal printing additive manufacturing. The structure and distribution of segmented pore and crack defects were accurately visualized by our model. Compared with traditional and main CNNs-based segmentation methods, our model exhibited a better recognition accuracy for pores and cracks, the Mean Intersection over Union and Pixel accuracy metrics reaching to 84.83 % and 98.87 % respectively. Practically, the option of efficiency or accuracy priority was quantitatively determined to carry out such a high-performance defect-related NDT. The proposed XCT-based segmentation mode has a great potential in fields of engineering, material science, biomedicine.

Task-Adaptive Angle Selection for Computed Tomography-Based Defect Detection.

Sparse-angle X-ray Computed Tomography (CT) plays a vital role in industrial quality control but leads to an inherent trade-off between scan time and reconstruction quality. Adaptive angle selection strategies try to improve upon this based on the idea that the geometry of the object under investigation leads to an uneven distribution of the information content over the projection angles. Deep Reinforcement Learning (DRL) has emerged as an effective approach for adaptive angle selection in X-ray CT. While previous studies focused on optimizing generic image quality measures using a fixed number of angles, our work extends them by considering a specific downstream task, namely image-based defect detection, and introducing flexibility in the number of angles used. By leveraging prior knowledge about typical defect characteristics, our task-adaptive angle selection method, adaptable in terms of angle count, enables easy detection of defects in the reconstructed images.

Effects of coarse aggregates on 3D printability and mechanical properties of ultra high performance fiber reinforced concrete

3D printing of ultra high performance fiber reinforced concrete (UHPFRC) suffers from high shrinkage and poor interlayer properties. To mitigate these problems, this study develops new mixtures of UHPFRC materials containing coarse aggregates (CA), and critically examines their suitability for 3D printing (3DP). Totally 90 mould-cast and 3DP cylinder and beam specimens with different CA sizes (5–15 mm) and CA-binder ratios (0.3–0.5) were tested under compression and bending in three directions. The internal distribution and volume fractions of steel fibers and pores and the crack trajectories were characterized and analysed by micro X-ray CT scanned 3D images with 37 μm voxel resolution. The results show that adding more and bigger CAs into UHPFRC reduced the flowability but enhanced the buildability with desired extrudability achieved by adjusting 3D printing velocity. Although the compressive strength of the 3DP cylinders was 15–42 % lower than that of the mould-cast ones due to higher porosities, over 100 MPa strength was still achieved for all the 3DP cylinders with less than 10 % anisotropy. The CT images did not show evident interlayers and interlayer delamination under compression, indicating the new 3DP mixtures and printing parameters may have highly promoted cement hydration. The flexural strength of 3DP beams was 3–34 % higher than that of the cast ones, because most fibres were oriented in the printing or beam axis direction as represented by overall orientation indices calculated from CT images, thereby providing significant crack-bridging effects. The developed new mixtures are therefore well suited for 3D printing, particularly for fabrication of structural members with preferred fibre orientation, such as beams, slabs and shells.

Power of photo-stress analysis in unravelling the mechanics of granular materials and its applications in interdisciplinary research

Granular materials, such as various grains and granulated powders, are essential in numerous industrial applications and natural processes, including the chemical, pharmaceutical, food, and materials processing industries. They also play a significant role in geotechnical phenomena like the shear-driven collapses of embankments and landslides, which can lead to extensive damage and disruptions. Despite a significant surge in research activities and advancements in discrete element modelling (DEM) tools since the late 1970s, understanding the mechanical characteristics of granular materials remains complex. Experimental techniques such as photo stress analysis (PSA), Nuclear Magnetic Resonance (NMR), and X-ray tomography (XRT) offer valuable insights, yet there is still a lack of detailed information on how granular materials respond mechanically at the microscale under various loading conditions. This knowledge gap affects industrial product quality, equipment safety, production efficiency, operational costs, environmental compliance, and overall safety. In this paper, we initially focus on our scientific contributions to the development of photo stress analysis (PSA) in unravelling the load-transmission characteristics of granular materials of varying sizes and structures (two-dimensional and quasi-three-dimensional) under different mechanical loading environments and applications. Where relevant, discrete element modelling (DEM) complements PSA to enhance our understanding of granular mechanics. We then present the application of PSA to selected key interdisciplinary areas from our recent work, demonstrating the power of PSA. Despite current challenges in applying PSA to three-dimensional granular systems, the results reported here significantly advance our fundamental understanding of the particle-scale and micromechanical characteristics in granular mechanics and particulate engineering. This work highlights the potential of PSA in addressing various inter- and multidisciplinary research problems in the future.

Quantitative Determination of Partial Voxel Compositions with X-ray CT Image-Based Data-Constrained Modelling

X-ray CT imaging is an important three-dimensional non-destructive testing technique, which has been widely applied in various fields. However, segmenting image voxels as discrete material compositions may lose information below the voxel size. In this study, six samples with known volume fractions of compositions were imaged using laboratory micro-CT. Optical microscopic images of the samples reveal numerous small particles of compositions smaller than the CT voxel size within the samples. By employing the equivalent energy method to determine the X-ray beam energy for sample imaging experiments, data-constrained modelling (DCM) was used to obtain the volume fractions of different compositions in the samples for each voxel. The results demonstrated that DCM effectively captured information about compositions occupying CT voxels partially. The computed volume fractions of compositions using DCM closely matched the known values. The results of DCM and four automatic threshold segmentation algorithms were compared and analyzed. The results showed that DCM has obvious advantages in processing those samples containing a large number of particles smaller than the CT voxel size. This work is the first quantitative evaluation of DCM for laboratory CT image processing, which provides a new idea for multi-scale structure characterization of materials based on laboratory CT.

Balancing current density and electrolyte flow for improved zinc-air battery cyclability

Zinc-air batteries (ZABs), known for their high energy density and environmental friendliness, are emerging as promising solutions for sustainable energy storage. However, the irregular deposition of zinc on electrodes hinders the widespread utilization of rechargeable ZABs due to limited durability and stability. This study investigates the role of electrolyte flow in enhancing zinc electrodeposition and overall performance in zinc-air flow batteries (ZAFBs) at high current densities. We explore the interplay between current density, flow rate, and their influence on electrode surface morphology and the removal of the passivating zinc oxide layer to improve battery efficiency and lifespan. Using advanced in-situ synchrotron radiation x-ray tomography, we found that incorporating flowing electrolyte at specific current densities results in rounded dendrite tips, thinner and more uniform deposition layers, and improved zinc adhesion. Imaging and electrochemical analyses further reveal that flowing electrolyte enhances zinc morphology, reduces charge transfer resistance, diminishes passivation, and lowers galvanostatic charge/discharge polarization across various current densities, thereby improving battery cycling performance. Notably, the impact of flowing electrolyte is more pronounced at a moderate current density of 50 mA/cm2 compared to lower or higher currents. Our findings underscore the importance of electrolyte flow and current density management in developing high-performance ZAFBs, providing valuable insights for their future commercialization.

Utilizing nano-computed tomography to characterize the structural nature of industrial minerals

Abstract X-ray computed tomography (XRT) is a nondestructive and thus reproducible examination method allowing the three-dimensional (3D) investigation of the internal and external structures of objects regardless of their material and geometry. In the present study, XRT was used to investigate the influence of hydrochloric acid leaching on the composition and constitution of iron-rich bauxite grains. Reducing the iron content in raw bauxites by acid leaching is a promising method for the beneficiation of iron-rich bauxites for subsequent use in the refractory industry. Not only the effect of the leaching process on the chemical composition of the bauxites, but also aspects such as the acid’s influence on the mineralogical composition and the resulting porosity of the individual grains have hardly been taken into account so far. To address these issues, various bauxites were examined before and after acid leaching using XRT analysis and specifically characterized with regard to their constitution.

Nondestructive and three-dimensional visualization by identifying elements using synchrotron radiation microscale X-ray CT reveals microbial and cavity distributions in anaerobic granular sludge.

Microorganisms inhabit diverse environments and often form biofilms. One factor that affects their community structure is the surrounding physical environment. The arrangement of residential space within the formed biofilm plays a crucial role in the supply and transportation of substances, as well as the discharge of metabolites. Conventional approaches, such as scanning electron microscopy and confocal laser scanning microscopy combined with fluorescence in situ hybridization, have limitations as they provide information primarily from the biofilm surface and cross-sections. In this study, we developed a method for detecting microorganisms in biofilms using synchrotron radiation X-ray microscale computer tomography. The developed method allows nondestructive three-dimensional observation of biofilms at a single-cell resolution (voxel size of approximately 200 nm), facilitating an understanding of the relationship between microorganisms and their physical habitats.

PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection

Additive Manufacturing (AM) has revolutionised the production of custom-shaped samples through direct manufacturing from digital design models. As the internal structural integrity of these printed samples is of critical importance in diverse applications, X-ray radiography and X-ray Computer Tomography (X-CT) have emerged as widely used non-destructive imaging methods for quality control of AM samples. Unfortunately, beam hardening and X-ray scatter often degrade the quality of X-CT images, posing a significant challenge for X-ray based inspection. In addressing X-ray scatter, most of the methodologies assume fixed scanning geometries or stationary/known object characteristics, limiting their practicality in dynamic industrial scenarios where these may change over time. Simulation-based methods have been proposed that estimate and suppress scatter by accurately simulating the forward projection process. Yet, these methods assume the availability of X-CT reconstruction for simulation, thereby requiring a large number of projections (and hence scan time) for faithful X-CT reconstruction. In this work, we propose a simulation-based scatter compensation method (PACS) that eliminates the need for a prior X-CT scan. By employing few projections and nominal surface meshes of the scanned objects, the actual pose of the objects and their superficial deviation (e.g., due to printing) are estimated and used during X-ray simulations. Furthermore, as the pipeline is inherently coupled with a mesh projector, analysis of projective residuals facilitates the inspection for deformities or defects within the scanned object. To demonstrate the versatility of PACS for mitigating scatter, experiments across various inspection scenarios are conducted, and the outcomes are compared with those of a well-established scatter compensation technique. The results consistently show a higher Signal-to-Noise Ratio and Contrast-to-Noise Ratio of pore-defects, as well as lower residual errors in all examined cases.

Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

Effects of Curing Conditions on Pore Structure of Ultra-High-Strength Shotcrete (UHSSC) Based on X-ray Computed Tomography.

Shotcrete is widely used in mine and civil engineering as supporting structure. A new type of ultra-high-strength shotcrete (UHSSC) with viscosity-enhancing agent was taken as the research object in this paper. A microstructure model of UHSSC under different curing conditions (standard curing, natural curing and film curing) was reconstructed using X-ray computed tomography (X-CT). The grey theory was used to analyze the correlation between pore characteristics and strength of UHSSC. The results showed that the porosity and the pore size of UHSSC were significantly reduced, the compressive strength was obviously improved by the new spraying process. The effects of curing conditions on the pore characteristics and compressive strength of UHSSC were obvious. Under natural curing, the hydration degree was the highest, the maximum pore size was the smallest, and the compressive strength was the highest, reaching 95.8 MPa, but the porosity was the highest. The curing condition had a certain influence on the sphericity distribution of UHSSC pores. Under film curing, the proportion of special-shaped pores (S < 0.4) was the largest and compressive strength was the smallest. There was a good correlation between pore characteristic parameters and the compressive strength of UHSSC under different curing conditions. In particular, the large pore size (D ≥ 5000 µm) and special-shaped pores (S < 0.4) had obvious effects on the strength of UHSSC, and the grey correlation coefficients were 0.8539 and 0.8080, respectively. Additionally, the pore direction of UHSSC had obvious directionality, and the anisotropy of UHSSC may be more prominent than poured specimen. The results will lay a foundation for the study of its mechanical properties and durability.