X-ray Computed Tomography Research Articles

Structural analysis of vacuum microwave dried shiitake mushrooms based on X-ray CT images and physical properties

Structural analysis of vacuum microwave dried shiitake mushrooms based on X-ray CT images and physical properties

Analytical electron tomography, fine structure EELS and nanoscale X-ray CT of nanoporous Copper materials

Analytical electron tomography, fine structure EELS and nanoscale X-ray CT of nanoporous Copper materials

An image quality assessment index based on image features and keypoints for X-ray CT images.

Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain.

Wood cauliflower mushroom (Sparassis crispa) suppresses the body weight and visceral fat increased by ovariectomy in mice

Sparassis crispa, an edible mushroom, has been reported to show many kinds of physiological functions. The present paper focused on reducing body weight, subcutaneous fat, and visceral fat gain in ovariectomized (OVX) mice. Using the fruiting body powder of the indoor cultivation S. crispa (IT S. crispa: ITSc), one week after the OVX, ITSc was administered to two OVX groups by per os (p.o). In the sham group, 10 mL/kg water and 10 mL/kg saline were administered by p.o. and subcutaneous adm, respectively. OVX groups were divided into four groups. These treatments were performed on animals 6 days a week for 8 weeks. Subcutaneous and visceral fat measurements were performed under inhalation anesthesia with isoflurane using a Latheta LCT-200 X-ray CT system. The biochemical markers and the mRNA expression levels of the PPARγ, adiponectin, TNF-α, PPARα, and leptin were measured. Significant increases in body weight, fat ratio, and glucose levels were detected in OVX mice compared to sham mice. These increases were significantly blocked by ITSc, but not estradiol. Furthermore, ITSc treatment significantly increased adiponectin and leptin levels in adipose tissue. These results suggest that ITSc improves lipid abnormalities due to the less activity of women's ovary function, excluding estrogen functions.

A comparative study on the scintillation characteristics of Gd2SiO5:Ce and Lu1.2Gd0.8SiO5:Ce scintillators

In the present day, inorganic scintillators have been developed to detect high-energy photons such as x-rays and γ-rays. They have been applied in many works, such as astronomy, materials inspection, high energy physics, industrial application, and natural resources exploration. Especially in the medical field, they are used as radiation detectors in diagnostic equipment such as PET scanner, x-ray CT scanner, SPECT scanner, etc. This research studied the scintillation characteristics of Ce:GSO and Ce:LGSO single crystals. We found that at 662 keV gamma-ray energy, Ce:LGSO crystal has about 2.3 times more light yield than Ce:GSO crystal, but the energy resolution of both crystals is not different. For the degree of non-proportionality of both crystals, Ce:LGSO scintillator shows 0.08, which is better than the 0.19 obtained from Ce:GSO scintillator. This work also discusses the two scintillators’ peak-to-total ratio, loss parameter, and intrinsic light yield.

X-ray CT visualization of particle array structure in sandy soil using ring shear experiment

X-ray CT visualization of particle array structure in sandy soil using ring shear experiment

Orthogonal Multi-Material Decomposition for X-Ray CT and Application in Metal Artifact Correction

Orthogonal Multi-Material Decomposition for X-Ray CT and Application in Metal Artifact Correction

Detection of Sternum Fracture with POCUS Despite Normal Computed Tomography Findings: A Case Report

Sternal fractures are extremely rare in children. It often develops after high-energy chest trauma. Many methods other than ultrasonography are used in the diagnosis of sternal fractures. However, Point-of-care ultrasound (POCUS) can outperform other methods due to its ease of use, less radiation and fast results. A thirteen-year-old male patient was brought due to chest pain that started after blunt chest trauma after falling from a height. In her initial evaluation in the trauma room, the patient's respiratory and cardiovascular examinations were found to be normal. On palpation, there was local tenderness in the middle 1/3 of the sternum. No pathology was detected in E-FAST. The portable chest radiograph was normal. POCUS was performed after the patient did not respond to analgesic treatment and had local sensitivity on the sternum. A sternal fracture was detected. Suspicious cortical irregularity was detected in repeated chest X-ray and thorax CT was reported as normal. When CT was re-evaluated, a wet tree fracture was observed in the sternum body. The patient was discharged without any complications. In this article, the importance of diagnosing a sternal fracture case whose computed tomography was reported as normal with POCUS and integrating rapid, noninvasive and radiation-free ultrasonography into the physical examination will be discussed in the management of these patients.

Novel Comparative Study of Covid-19 Detection from X-ray and CT Scan Images Using CNN and MLP Neural Networks

The coronavirus has caused the deaths of millions of people and has endangered the entire healthcare system. In order to count positive cases and stop the disease from spreading, Rapid clinical results may prevent the COVID-19 from spreading and help medical professionals treat patients while working under challenging circumstances.. Normal disease diagnosis using a laboratory test requires equipment and takes some time with the use of X-ray and chest CT Scan images, artificial intelligence techniques are extensively used to categorize the COVID-19. In this study we present an automatic detection approach for COVID-19 infection based on Chest CT and X-ray images using a Multilayer Perceptron (MLP) Neurons Network and a Convolutional Neural Network (CNN). The two models are evaluated in two classes, COVID-19 and normal images, for detection by Chest X-ray images we obtained 95,7% accuracy using MLP model and 90% accuracy using CNN model. For detection by Chest CT image we obtained, 80,60 % accuracy using the MLP model and 88,49 % accuracy using the CNN. The experimental results indicate that the proposed approach can achieve high accuracy in detecting COVID-19 from X-ray images, demonstrating the potential of using machine learning techniques in medical diagnosis.

Crack propagation and non-uniform deformation analysis in hydraulic asphalt concrete: Insights from X-ray CT scanning and digital volume correlation

This study investigates the crack propagation and internal non-uniform deformation field of hydraulic asphalt concrete (HAC) under triaxial compression. Utilizing in situ triaxial compression tests at temperatures of 5 °C and 20 °C, the investigation incorporated X-ray computed tomography (CT) scanning to observe internal changes. Through digital volume correlation (DVC), the deformation field within HAC was analyzed in detail. Results indicated that the crack fraction of longitudinal sections at 20 °C was smaller across various deformation stages than that at 5 °C. The displacement and strain fields within the material displayed increased heterogeneity with increasing deformation. It was observed that the probability density distributions of both equivalent and volumetric strains conformed to a normal distribution at different deformation stages. By analyzing the crack propagation and the internal non-uniform deformation field, the progressive failure process of HAC with sub-voxel accuracy at 5 °C and 20 °C was clarified. This research confirmed the presence of both shear deformation failure and volumetric deformation characteristics, which undergo a transition from shear shrinkage to shear expansion at the meso scale.

Metal Artifact Reduction in CT Images through Sinogram Data Inpainting

When metallic implants are present within the human body, they frequently introduce metallic artifacts into X-ray CT images. These artifacts can lead to significant distortions, obscuring critical information and potentially degrading the quality of the CT images, thereby impacting diagnostic accuracy for clinicians. In recent years, there has been extensive research aimed at mitigating the challenges posed by metallic artifacts, resulting in the development of multiple solutions to address this issue. In this study, we present an efficient approach for artifact removal. Our method involves utilizing the image reconstructed from a sinogram affected by artifacts to generate a synthesized sinogram, deviating from the conventional acquisition of sinogram data. The key stages of our approach encompass segmentation, sinogram gap-filling, and subsequent image enhancement. To achieve rapid segmentation, we employed a K-means classification method. For the retrieval of missing data, we utilized an interpolation algorithm based on a penalized least squares method. In the final phase of image reconstruction enhancement, we implemented an advanced contrast equalization technique to restore image intensities to their inherent dynamic range. Through rigorous verification using both simulated and clinical data, our method consistently demonstrates a remarkable improvement in image quality.

Multi-Scale Characterization of Porosity and Cracks in Silicon Carbide Cladding after Transient Reactor Test Facility Irradiation

Silicon carbide (SiC) ceramic matrix composite (CMC) cladding is currently being pursued as one of the leading candidates for accident-tolerant fuel (ATF) cladding for light water reactor applications. The morphology of fabrication defects, including the size and shape of voids, is one of the key challenges that impacts cladding performance and guarantees reactor safety. Therefore, quantification of defects’ size, location, distribution, and leak paths is critical to determining SiC CMC in-core performance. This research aims to provide quantitative insight into the defect’s distribution under multi-scale characterization at different length scales before and after different Transient Reactor Test Facility (TREAT) irradiation tests. A non-destructive multi-scale evaluation of irradiated SiC will help to assess critical microstructural defects from production and/or experimental testing to better understand and predict overall cladding performance. X-ray computed tomography (XCT), a non-destructive, data-rich characterization technique, is combined with lower length scale electronic microscopic characterization, which provides microscale morphology and structural characterization. This paper discusses a fully automatic workflow to detect and analyze SiC-SiC defects using image processing techniques on 3D X-ray images. Following the XCT data analysis, advanced characterizations from focused ion beam (FIB) and transmission electron microscopy (TEM) were conducted to verify the findings from the XCT data, especially quantitative results from local nano-scale TEM 3D tomography data, which were utilized to complement the 3D XCT results. In this work, three SiC samples (two irradiated and one unirradiated) provided by General Atomics are investigated. The irradiated samples were irradiated in a way that was expected to induce cracking, and indeed, the automated workflow developed in this work was able to successfully identify and characterize the defects formation in the irradiated samples while detecting no observed cracking in the unirradiated sample. These results demonstrate the value of automated XCT tools to better understand the damage and damage propagation in SiC-SiC structures for nuclear applications.

Recent progress of in-situ/operando characterization approaches of zinc-air batteries

Zinc-air batteries (ZABs) belong to the category of metal-air batteries, with high theoretical energy density, safety, and low cost. Nevertheless, there are still many challenges that need to be solved for the practical application of ZABs, including high overpotential, poor cycle life, and so on. This article first briefly introduced the principle of ZABs, covering the key components, functions of each element, and challenges faced by the system. Subsequently, seven methods for studying ZABs in-situ or operando were introduced, including X-ray computed tomography (XCT), optical microscopy imaging (OMI), transmission electron microscopy, nuclear magnetic resonance imaging (MRI), X-ray diffraction (XRD), Raman spectroscopy, and X-ray absorption spectroscopy, accompanied by specific research examples. The future perspectives of ZAB characterization have also been discussed.

Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data.

This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.

Implication of Filters on the Accuracy of Radiotherapy Planning and the Need for Recalibration of the CT-to-ED Curve

This article examines the effect of the presence or absence of an ImAR filter on the calibration curve for converting X-ray tomography (CT) data to relative electron density (CT-to-ED) values for the abdominal region and head. On CT images this results in distortion of Hounsfield Units (HU) values. The latter, in turn, can lead to inaccuracies in the CT-to-ED calibration curve.
 Conclusions: If the filters are set up correctly and applied according to the planning system manufacturer's recommendations, changes in the CT-to-Ed curve should be negligible. CT-to-ED curves are an important tool for improving the accuracy and quality of radiation therapy for cancer.

Advanced Imaging Techniques for the Characterization of Subcellular Organelle Structure in Pancreatic Islet β Cells.

Type 2 diabetes (T2D) affects more than 32.3 million individuals in the United States, creating an economic burden of nearly $966 billion in 2021. T2D results from a combination of insulin resistance and inadequate insulin secretion from the pancreatic β cell. However, genetic and physiologic data indicate that defects in β cell function are the chief determinant of whether an individual with insulin resistance will progress to a diagnosis of T2D. The subcellular organelles of the insulin secretory pathway, including the endoplasmic reticulum, Golgi apparatus, and secretory granules, play a critical role in maintaining the heavy biosynthetic burden of insulin production, processing, and secretion. In addition, the mitochondria enable the process of insulin release by integrating the metabolism of nutrients into energy output. Advanced imaging techniques are needed to determine how changes in the structure and composition of these organelles contribute to the loss of insulin secretory capacity in the β cell during T2D. Several microscopy techniques, including electron microscopy, fluorescence microscopy, and soft X-ray tomography, have been utilized to investigate the structure-function relationship within the β cell. In this overview article, we will detail the methodology, strengths, and weaknesses of each approach. © 2024 American Physiological Society. Compr Physiol 14:5243-5267, 2024.

Mesostructural evolution of fine-aggregate bitumen emulsion–cement composites by X-ray tomography

ABSTRACT This research quantified the temporal mesostructural evolution of bitumen emulsion–cement composites using the time-lapse high-energy X-ray tomography of a fine-aggregate matrix. The image post-processing and analysis showed that the mastic’s significant temporal decrease in volume complemented the expansion of the pore space. Nevertheless, the volume fractions determined by the image analysis essentially differed from the physical composition of specimens and were several times less sensitive to curing. Because of the extremely heterogenous microstructure and the abundance of calcium, the mastic phase had the highest attenuation of X-rays, but the attenuations of bitumen- and cement-dominated systems experienced contrary temporal trends. The sand and pore space had typically smooth and oppositely evolving axial distributions with the highest-density plateau in the middle, while the mastic was distributed uniformly. The radial distributions evolved less irregularly and notably interfered with the post-processing of beam hardening. The temporal increase in the pore space’s local thicknesses was extremely unevenly distributed across diameters. Except the sand particle size distributions, all results were almost excellently repeatable. Finally, the interaction with X-rays was identified as crucial for the interpretation and validity of the results. Moreover, although water could not be segmented using the conventional X-ray tomography, its discrete signature was present throughout the behaviour of other phases.

Cattle manure application for 12 and 17 years enhanced depth distribution of soil organic carbon and X-ray computed tomography-derived pore characteristics

Long-term fertilizer application in row crops may influence soil pore characteristics, thereby impacting soil aggregation and structure. Therefore, understanding the influences on soil pore characteristics is useful for adopting suitable conservation practices. However, the impact of cattle manure and inorganic fertilizer application at varied rates on soil pore characteristics in the soil profile at a microscale level remains limited. This study quantifies the impacts of manure and inorganic fertilizer amendments under a corn (Zea mays L.)-soybean (Glycine max L.)-spring wheat (Triticum aestivum) rotation system on soil pore characteristics using the X-ray computed tomography (XCT). Treatments included: low manure (LM; 4.4 and 3.3 Mg ha−1), medium manure (MM; 27.4 and 18.7 Mg ha−1), high manure (HM; 54.8 and 37.4 Mg ha−1), medium fertilizer (MF; 136 kg N ha−1, 49 kg P2O5 ha−1, and 91.5 kg K2O ha−1), high fertilizer (HF; 204 kg N ha−1, 73.5 kg P2O5 ha−1, and 137.3 kg K2O ha−1), and control (CK), respectively, at Brookings (initiated in 2008) and Beresford (2003) in South Dakota. Four intact soil cores were collected from each treatment at 0–10, 10–20, 20–30, and 30–40 cm depths. Results showed that the HM treatment increased the SOC by 8–68% compared to the CK and MF at 0–20 cm at the study sites. Both HM and MM treatments increased the macroporosity and mesoporosity in 0–20 cm soil depths at both study sites. Treatment did not always improve soil pore characteristics below 20 cm soil depth. Additionally, a positive correlation was observed between the XCT-derived macroporosity, total number of macropores, and SOC for all the treatments. Therefore, this study encourages the adoption of the XCT technique in quantifying soil pore characteristics and suggests that long-term medium manure application enhances soil structure as compared to an equivalent inorganic fertilizer application.

Imaging the 3D microstructural changes of ice cream during meltdown.

Ice cream is a multiphase frozen dessert that often melts during distribution and upon consumption. The meltdown phenomenon is one of the concerns in the quality preservation of ice cream for consumer convenience in the frozen food industry. In this context, X-ray tomography was used to visualise and quantify 3D ice crystal and air bubble evolution during the meltdown of ice cream. Two ice cream products, namely I and II, with varying air volume fractions, were evaluated for this study. The results indicated a small mean diameter of 66.43 ± 2.07µm at 0min and decreased to 45.74 ± 3.92µm during 10min of the meltdown of ice cream II. A large mean diameter of ice crystals of 75.02 ± 3.14µm was found in ice cream I, at 0min that decreased significantly (p < 0.05) to 54.30 ± 2.63µm during 10min of the meltdown. The air bubbles were also observed to decrease in mean diameter. The 3D datasets on the ice crystals and air bubbles described in this work provide more insight into the 3D microstructural evolution during the meltdown and are useful in controlling the sensory quality attributes of ice cream desserts.

Nondestructive Inspection of Hot Corrosion Damage on Internal Surfaces of Turbine Blades

Abstract Two nondestructive techniques for the identification of hot corrosion damage on internal surfaces were investigated: magnetic permeability measurements and X-ray computed tomography (CT). A group of sixty-one Solar Titan 130 stage 1 blades, which were known to have hot corrosion damage on the internal surfaces at a specific location, were used for the investigation. X-ray CT was able to accurately identify the presence of hot corrosion as well as the extent to which it had progressed through the wall at the location of interest. The magnetic permeability technique was found to accurately identify whether hot corrosion damage had occurred at the location of interest, but could not as accurately determine the extent to which the damage had progressed through the wall. The results of the nondestructive testing were validated by destructive examination of some blades. The nondestructive testing methods evaluated through the study were able to determine the presence and extent of localized hot corrosion damage on internal surfaces, allowing for higher repair yields.