The functional roles of zebrafish HoxA- and HoxD-related clusters in the pectoral fin development.

The paralogs 9-13 Hox genes in mouse HoxA and HoxD clusters are critical for limb development. When both HoxA and HoxD clusters are deleted in mice, significant limb truncation is observed compared to the phenotypes of single and compound mutants of Hox9-13 genes in these clusters. In zebrafish, mutations in hox13 genes in HoxA- and HoxD-related clusters result in abnormal morphology of pectoral fins, homologous to forelimbs. However, the effect of the simultaneous deletions of entire HoxA- and HoxD-related clusters on pectoral fin development remains unknown. Here, we generated mutants with several combinations of hoxaa, hoxab, and hoxda cluster deletions and analyzed the pectoral fin development. In hoxaa-/-;hoxab-/-;hoxda-/- larvae, the endoskeletal disc and the fin-fold are significantly shortened in developing pectoral fins. In addition, we show that this anomaly is due to defects in the pectoral fin growth after the fin bud formation. Furthermore, in the surviving adult mutants, micro-CT scanning reveals defects in the posterior portion of the pectoral fin which is thought to represent latent regions of the limb. Our results further support that the functional role of HoxA and HoxD clusters is conserved in the paired appendage formation in bony fishes.

The distribution of particulate organic matter in the heterogeneous soil matrix - Balancing between aerobic respiration and denitrification.

Denitrification, a key process in soil nitrogen cycling, occurs predominantly within microbial hotspots, such as those around particulate organic matter (POM), where denitrifiers use nitrate as an alternative electron acceptor. For accurate prediction of dinitrogen (N2) and nitrous oxide (N2O) emissions from denitrification, a precise quantification of these microscale hotspots is required. The distribution of POM is of crucial importance in this context, as the local oxygen (O2) balance is governed not only by its high O2 demand but also by the local O2 availability. Employing a unique combination of X-ray CT imaging, microscale O2 measurements, and 15N labeling, we were able to quantify hotspots of aerobic respiration and denitrification. We analyzed greenhouse gas (GHG) fluxes, soil oxygen supply, and the distribution of POM in intact soil samples from grassland and cropland under different moisture conditions. Our findings reveal that both proximal and distal POM, identified through X-ray CT imaging, contribute to GHG emissions. The distal POM, i.e. POM at distant locations to air-filled pores, emerged as a primary driver of denitrification within structured soils of both land uses. Thus, the higher denitrification rates in the grassland could be attributed to the higher content of distal POM. Conversely, despite possessing compacted areas that could favor denitrification, the cropland had only small amounts of distal POM to stimulate denitrification in it. This underlines the complex interaction between soil structural heterogeneity, organic carbon supply, and microbial hotspot formation and thus contributes to a better understanding of soil-related GHG emissions. In summary, our study provides a holistic understanding of soil-borne greenhouse gas emissions and emphasizes the need to refine predictive models for soil denitrification and N2O emissions by incorporating the microscale distribution of POM.

Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions.

Background and Objective: The incidence rate of lung cancer, which also has the highest mortality rates for both men and women worldwide, is increasing globally. Due to advancements in imaging technology and the growing inclination of individuals to undergo screening, the detection rate of ground-glass nodules (GGNs) has surged rapidly. Currently, artificial intelligence (AI) methods for data analysis and interpretation, image processing, illness diagnosis, and lesion prediction offer a novel perspective on the diagnosis of GGNs. This article aimed to examine how to detect malignant lesions as early as possible and improve clinical diagnostic and treatment decisions by identifying benign and malignant lesions using imaging data. It also aimed to describe the use of computed tomography (CT)-guided biopsies and highlight developments in AI techniques in this area. Methods: We used PubMed, Elsevier ScienceDirect, Springer Database, and Google Scholar to search for information relevant to the article's topic. We gathered, examined, and interpreted relevant imaging resources from the Second Affiliated Hospital of Nanchang University's Imaging Center. Additionally, we used Adobe Illustrator 2020 to process all the figures. Key Content and Findings: We examined the common signs of GGNs, elucidated the relationship between these signs and the identification of benign and malignant lesions, and then described the application of AI in image segmentation, automatic classification, and the invasiveness prediction of GGNs over the last three years, including its limitations and outlook. We also discussed the necessity of conducting biopsies of persistent pure GGNs. Conclusions: A variety of imaging features can be combined to improve the diagnosis of benign and malignant GGNs. The use of CT-guided puncture biopsy to clarify the nature of lesions should be considered with caution. The development of new AI tools brings new possibilities and hope to improving the ability of imaging physicians to analyze GGN images and achieving accurate diagnosis.

Technical Notes: Robustness of three-dimensional treatment and imaging isocenter testing using a new gel dosimeter and kilovoltage CBCT.

BACKGROUND: Coincidence of the treatment and imaging isocenter coordinates is required to safely perform small-margin treatments, such as stereotactic radiosurgery of multiple brain metastases. A comprehensive and direct methodology for verifying concordance of kilovoltage cone-beam computed tomography (kV-CBCT) and treatment coordinates using an x-ray CT-based polymer gel dosimeter (dGEL) and onboard kV-CBCT was previously reported. Using this methodology, we tested the ability of a new commercially available x-ray CT-based polymer dGEL with a rapid response to provide efficient quality assurance (QA). PURPOSE: The aim of this study was to evaluate the robustness of the three-dimensional geometric QA methodology using dGEL. METHODS: The dGEL were commercially manufactured. The prescribed dose for each field was determined by visually identifying the 5, 10, and 20 Gy isodose lines. A linear accelerator was used to irradiate the gels with seven non-coplanar beams. An in-house analysis program was used to identify the beam axes and treatment isocenter in kV-CBCT coordinates by processing the pre- and post-irradiation CBCT images. The impact of the radiation dose on the test reproducibility was examined, and the detectability of an intentional geometric error was assessed. RESULTS: The treatment isocenter was within 0.4 mm of the imaging isocenter for all radiation doses. The residual error of the test with the intentional error was within 0.2 mm. The analysis and image quality variations for a single dGEL introduced displacement errors less than 0.3 mm. CONCLUSIONS: The test assessed the coincidence of treatment and kV-CBCT isocenter coordinates and detected errors with high robustness. Even for a 10 Gy dose, the test yielded results comparable with those obtained using higher radiation doses owing to the rapid response of the dGEL dosimeter.

Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice.

BACKGROUND AND AIMS: Root system architecture (RSA) plays a key role in plant adaptation to drought because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, as early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional (3D) image analysis. METHODS: We used 109 F12 recombinant inbred lines (RILs) obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25 cm height) by stopping irrigation 14 days after sowing (DAS). Time-series RSA at 14, 21, and 28 DAS was visualized by X-ray computed tomography, and subsequently compared between drought and well-watered conditions. Following this analysis, we further investigated drought-avoidant RSA by testing 20 randomly selected RILs under drought conditions. KEY RESULTS: We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth, which positively impacted shoot growth, ranged between 1.7-3.4 cm under drought conditions and between 0.0-1.7 cm under well-watered conditions. Drought-avoidant RILs had a higher root density in the lower layers of the topsoil compared to the others. CONCLUSIONS: Fine classification of soil layers using 3D image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice.

Experimental observation of cavity-free ice-free isochoric vitrification via combined pressure measurements and photon counting x-ray computed tomography.

Isochoric (constant-volume or volumetrically confined) vitrification has shown potential as an alternative cryopreservation-by-vitrification technique, but the complex processes at play within the chamber are yet poorly characterized, and recent investigations have prompted significant debate around whether a truly isochoric vitrification process (in which the liquid remains completely confined by solid boundaries) is indeed feasible. Based on a recent thermomechanical simulation of a high-concentration Me2SO solution, Solanki and Rabin (Cryobiology, 2023, 111, 9-15.) argue that isochoric vitrification is not feasible, because differential thermal contraction of the solution and container will necessarily drive generation of a cavity, corrupting the rigid confinement of the liquid. Here, we provide direct experimental evidence to the contrary, demonstrating cavity-free isochoric vitrification of a ∼3.5 M vitrification solution by combined isochoric pressure measurement (IPM) and photon-counting x-ray computed tomography (PC-CT). We hypothesize that the absence of a cavity is due to the minimal thermal contraction of the solution, which we support with additional volumetric analysis of the PC-CT reconstructions. In total, this study provides experimental evidence both demonstrating the feasibility of isochoric vitrification and highlighting the potential of designing vitrification solutions that exhibit minimal thermal contraction.

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 ± 2.07 µm at 0 min and decreased to 45.74 ± 3.92 µm during 10 min of the meltdown of ice cream II. A large mean diameter of ice crystals of 75.02 ± 3.14 µm was found in ice cream I, at 0 min that decreased significantly (p < 0.05) to 54.30 ± 2.63 µm during 10 min 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.

Enhancing UHPC Tensile Performance Using Polystyrene Beads: Significant Improvements and Mechanisms.

This study investigates utilizing spherical polystyrene (PS) beads as artificial flaws to improve ultrahigh-performance concrete (UHPC) tensile performance using a uniaxial tensile test and explains the corresponding mechanisms by analyzing the internal material structure of UHPC specimens with X-ray CT scanning. With a hooked steel fiber volume fraction of 2%, three PS bead dosages were employed to study tensile behavior changes in dog-bone UHPC specimens. A 33.4% increase in ultimate tensile strength and 174.8% increase in ultimate tensile strain were recorded after adding PS beads with a volume fraction of 2%. To explain this improvement, X-ray CT scanning was utilized to investigate the post-test internal material structures of the dog-bone specimens. AVIZO software was used to analyze the CT information. The CT results revealed that PS beads could not only serve as the artificial flaws to increase the cracking behavior of the matrix of UHPC but also significantly optimize the fiber orientation. The PS beads could serve as stirrers during the mixing process to distribute fiber more uniformly. The test results indicate a relationship between fiber orientation and UHPC tensile strength.

Distinct biophysical and chemical mechanisms governing sucrose mineralization and soil organic carbon priming in biochar amended soils: evidence from 10 years of field studies.

While many studies have examined the role of biochar in carbon (C) accrual in short-term scale, few have explored the decadal scale influences of biochar on non-biochar C, e.g., native soil organic C (SOC) and added substrate. To address this knowledge gap, soils were collected from decade-old biochar field trials located in the United Kingdom (Cambisol) and China (Fluvisol), with each site having had three application rates (25-30, 50-60 and 75-100 Mg ha-1) of biochar plus an unamended Control, applied once in 2009. We assessed physicochemical and microbial properties associated with sucrose (representing the rhizodeposits) mineralization and the priming effect (PE) on native SOC. Here, we showed both soils amended with biochar at the middle application rate (50 Mg ha-1 biochar in Cambisol and 60 Mg ha-1 biochar in Fluvisol) resulted in greater substrate mineralization. The enhanced accessibility and availability of sucrose to microorganisms, particularly fast-growing bacterial genera like Arenimonas, Spingomonas, and Paenibacillus (r-strategists belonging to the Proteobacteria and Firmicutes phyla, respectively), can be attributed to the improved physicochemical properties of the soil, including pH, porosity, and pore connectivity, as revealed by synchrotron-based micro-CT. Random forest analysis also confirmed the contribution of the microbial diversity and physical properties such as porosity on sucrose mineralization. Biochar at the middle application rate, however, resulted in the lowest PE (0.3 and 0.4 mg of CO2-C g soil-1 in Cambisol and Fluvisol, respectively) after 53 days of incubation. This result might be associated with the fact that the biochar promoted large aggregates formation, which enclosed native SOC in soil macro-aggregates (2-0.25 mm). Our study revealed a diverging pattern between substrate mineralization and SOC priming linked to the biochar application rate. This suggests distinct mechanisms, biophysical and physicochemical, driving the mineralization of non-biochar carbon in a field where biochar was applied a decade before. Supplementary Information: The online version contains supplementary material available at 10.1007/s42773-024-00327-0.

Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels.

BACKGROUND: X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes. RESULTS: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data. CONCLUSIONS: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.

Wire-tracking of bent electric cable using X-ray CT and deep active learning.

We have demonstrated a quantification of all component wires in a bent electric cable, which is necessary for discussion of cable products in actual use cases. Quantification became possible for the first time because of our new technologies for image analysis of bent cables. In this paper, various image analysis techniques to detect all wire tracks in a bent cable are demonstrated. Unique cross-sectional image construction and deep active learning schemes are the most important items in this study. These methods allow us to know the actual state of cables under external loads, which makes it possible to elucidate the mechanisms of various phenomena related to cables in the field and further improve the quality of cable products.

Spatial and temporal motion characterization for x-ray CT.

BACKGROUND: Motion induced image artifacts have been the focus of many investigations for x-ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts. PURPOSE: Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image-space spatial- and temporal-consistency metric (CM) to detect and characterize the local motion. METHOD: In a non-rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region-of-interests for consistency-metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. RESULTS: Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in-plane motion, cross-plane motion, cone-beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. CONCLUSIONS: In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view-by-view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact.

A radiomics model of contrast-enhanced computed tomography for predicting post-acute pancreatitis diabetes mellitus.

Background: Diabetes mellitus can occur after acute pancreatitis (AP), but the accurate quantitative methods to predict post-acute pancreatitis diabetes mellitus (PPDM-A) are lacking. This retrospective study aimed to establish a radiomics model based on contrast-enhanced computed tomography (CECT) for predicting PPDM-A. Methods: A total of 374 patients with first-episode AP were retrospectively enrolled from two tertiary referral centers. There were 224 patients in the training cohort, 56 in the internal validation cohort, and 94 in the external validation cohort, and there were 86, 22, and 27 patients with PPDM-A in these cohorts, respectively. The clinical characteristics were collected from the hospital information system. A total of 2,398 radiomics features, including shape-based features, first-order histogram features, high order textural features, and transformed features, were extracted from the arterial- and venous-phase CECT images. Intraclass correlation coefficients were used to assess the intraobserver reliability and interobserver agreement. Random forest-based recursive feature elimination, collinearity analysis, and least absolute shrinkage and selection operator (LASSO) were used for selecting the final features. Three classification methods [eXtreme Gradient Boosting (XGBoost), Adaptive Boosting, and Decision Tree] were used to build three models and performances of the three models were compared. Each of the three classification methods were used to establish the clinical model, radiomics model, and combined model for predicting PPDM-A, resulting in a total of nine classifiers. The predictive performances of the models were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results: Eleven radiomics features were selected after a reproducibility test and dimensionality reduction. Among the three classification methods, the XGBoost classifier showed better and more consistent performances. The AUC of the XGBoost's radiomics model to predict PPDM-A in the training, internal, and external cohorts was good (0.964, 0.901, and 0.857, respectively). The AUC of the XGBoost's combined model to predict PPDM-A in the training, internal, and external cohorts was good (0.980, 0.901, and 0.882, respectively). The AUC of the XGBoost's clinical model to predict PPDM-A in the training, internal, and external cohorts did not perform well (0.685, 0.733, and 0.619, respectively). In the external validation cohort, the AUC of the XGBoost's radiomics model was significantly higher than that of the clinical model (0.857 vs. 0.619, P<0.001), but there was no significant difference between the combined and radiomics models (0.882 vs. 0.857, P=0.317). Conclusions: The radiomics model based on CECT performs well and can be used as an early quantitative method to predict the occurrence of PPDM-A.

Multi-Scale Analysis of the Damage Evolution of Coal Gangue Coarse Aggregate Concrete after Freeze-Thaw Cycle Based on CT Technology.

This study utilized X-ray computed tomography (CT) technology to analyze the meso-structure of concrete at different replacement rates, using a coal gangue coarse aggregate, after experiencing various freeze-thaw cycles (F-Ts). A predictive model for the degradation of the elastic modulus of Coal Gangue coarse aggregate Concrete (CGC), based on mesoscopic damage, was established to provide an interpretation of the macroscopic mechanical behavior of CGC after F-Ts damage at a mesoscopic scale. It was found that after F-Ts, the compressive strength of concrete, with coal gangue replacement rates of 30%, 60%, and 100%, respectively, decreased by 33.76%, 34.89%, and 42.05% compared with unfrozen specimens. The results indicate that an increase in the coal gangue replacement rate exacerbates the degradation of concrete performance during the F-Ts process. Furthermore, the established predictive formula for elastic modulus degradation closely matches the experimental data, offering a reliable theoretical basis for the durability design of CGC in F-Ts environments.

Fabrication of Composite Material by Directly Printing Resin on Aluminum Foam by 3D Printer.

Aluminum foam has relatively low tensile and flexural strengths because it is composed of many pores with thin cell walls. One method of strengthening aluminum foam is to fabricate a composite material with a dense lightweight resin. In this study, the fabrication of composite materials by directly printing resin on an aluminum foam surface using a 3D printer was attempted. The resin was directly printed on both heated and unheated aluminum foam. It was shown that composite materials consisting of aluminum foam and resin can be fabricated by directly printing resin with a 3D printer on both heated and unheated aluminum foam. The resin was softened during the printing process in the case of directly printed resin on heated aluminum foam, allowing more resin to penetrate into the pores than in the case of directly printed resin on unheated aluminum foam. In addition, it was shown that resin can be directly printed on the aluminum foam with a high bonding strength, as a large amount of resin penetrated into the pores, resulting in an anchor effect. That is, composite materials consisting of aluminum foam and arbitrary-shaped resin with relatively high bonding strength can be fabricated when a large amount of resin is allowed to penetrate into the pore.

End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotyping of grapevine trunk internal structure.

Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine (Vitis vinifera L.). Vineyard's sustainability is threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key-measurements in evaluating vines' sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of tissue quality and plant health across plant species.

Persimmon preservation using edible coating of chitosan enriched with ginger oil and visualization of internal structure changes using X-ray computed tomography.

The incorporation of ginger oil (GO) influenced the physical, optical, and structural properties of the chitosan (CH) film including the decreases of moisture content (60.15 %), water solubility (35.37 %) and water vapor permeability (WVP) (32.79 %) and the increases of tensile strength (TS) (125 %), elongation at break (EAB) (2.74 %) and opacity (131.08 %). Antifungal capacity of the CH film was enhanced when GO was added to the film. The CH + GO film showed a less homogeneous surface that the presence of the oil droplets on the film surface. Moreover, the CH and CH + GO coatings reduced weight loss of persimmon by 14.87 %, and 21.13 %, respectively, compared to the control. Moisture content loss of the coated CH- and the coated CH + GO- persimmons was decreased by 1.94 % and 4.92 %, respectively, compared to that of the control persimmon. Furthermore, the CH and CH + GO coatings decreased in color changes, respiration rate, ethylene production, changes in pH and TSS, and remained firmness of persimmon during storage at 25 °C. In addition, X-ray CT images can be used to monitor internal changes and observe the tissue breakdown during storage period. The ΔGS value can be used as a predictor of persimmon internal qualities. Thus, the CH film containing GO can be applied as an active packaging material.

Consistency of Monoenergetic Attenuation Measurements for a Clinical Dual-Source Photon-Counting Detector CT System Across Scanning Paradigms: A Phantom Study.

BACKGROUND. Use of virtual monoenergetic images (VMIs) from multienergy CT scans can mitigate inconsistencies in traditional attenuation measurements that result from variation in scan-related factors. Photon-counting detector (PCD) CT systems produce VMIs as standard image output under flexible scanning conditions. OBJECTIVE. The purpose of this article was to evaluate the consistency of monoenergetic attenuation measurements obtained from a clinical PCD CT scanner across a spectrum of scanning paradigms. METHODS. A phantom with 10 tissue-simulating inserts was imaged using a clinical dual-source PCD CT scanner. Nine scanning paradigms were obtained across combinations of tube voltages (90, 120, and 140 kVp) and image quality (IQ) levels (80, 145, and 180). Images were reconstructed at VMI levels of 50, 60, 70, and 80 keV. Consistency of attenuation measurements was assessed, using the 120 kVp with IQ level of 145 scanning paradigm as the reference scan. RESULTS. For all scanning paradigms, attenuation measurements showed intra-class correlation of 0.999 and higher with respect to the reference scan. Across inserts, mean bias relative to the reference scan ranged from -14.9 to 13.6 HU, -2.7 to 1.7 HU, and -3.9 to 3.8 HU at tube voltages of 90, 120, and 140 kVp, respectively; and from -14.9 to 13.6 HU, -6.4 to 3.8 HU, -3.7 to 1.4 HU, and -7.2 to 4.3 HU at VMI levels of 50, 60, 70, and 80 keV, respectively. Thus, mean bias did not exceed 5 HU for any insert at tube potentials of 120 kVp and 140 kVp, nor for any insert at a VMI level of 70 keV. At a VMI level of 50 keV and tube potential of 90 kVp, mean bias exceeded 5 HU for 14 of 30 possible combinations of inserts and scanning paradigms and exceeded 10 HU for four of 30 such combinations. At VMI levels of both 60 and 80 keV, mean bias exceeded 5 HU for only two combinations of inserts and scanning paradigms, all at a tube potential of 90 kVp. CONCLUSION. PCD CT generally provided consistent attenuation measurements across combinations of scanning paradigms and VMI levels. CLINICAL IMPACT. PCD CT may facilitate quantitative applications of CT data in clinical practice.

CT-guided core needle biopsies of head and neck tumors: a comprehensive monocenter analysis of safety and outcomes.

BACKGROUND: Although core needle biopsy is an important tool in minimally invasive tissue sampling and diagnostics for head and neck masses, comprehensive data about safety and outcomes is lacking. PURPOSE: To retrospectively evaluate the diagnostic performance and safety of computed tomography (CT)-guided percutaneous core needle biopsy of head and neck masses. MATERIAL AND METHODS: This retrospective single-center study included patients from 04/2007 to 12/2021, and a total of 156 core needle biopsies were evaluated. The initial histopathological results were compared with the long-term final diagnosis to evaluate the diagnostic yield of CT-guided core needle biopsies. The patients' age, sex, and history of malignancy, as well as procedural complications and radiation exposure were collected. RESULTS: A total of 156 biopsies of 150 patients (mean age 56 years ± 17; 89 men) were evaluated. 57.3% (86/150) of patients had a history of malignancy. 55.1% (86/156) of the lesions were accessed by an infrahyoid needle approach. 92.9% (145/156) of biopsies yielded conclusive results. There were no false positives and 4 false negatives, resulting in a total false negative rate of 2.7% (4/145) and a total diagnostic yield of 90.4% (141/156). There were nine puncture-related complications (9/156-5.7%). None of the complications required further reintervention. The average dose length product was 311.3 mGy × cm. CONCLUSION: CT-guided core needle biopsies of head and neck masses showed excellent results with high diagnostic yield and clinical safety. CLINICAL RELEVANCE STATEMENT: General anesthesia for open biopsy carries a higher risk for elderly patients, and fine needle aspiration has a poor reputation in terms of its diagnostic yield. This study focuses on safety and diagnostic yield of CT-guided core needle biopsies. KEY POINTS: • CT-guided core needle biopsy in head and neck tumors was a reliable and safe procedure. • The most common cause for an inconclusive biopsy result was a shortage of tissue collected during the biopsy. • During our study period of nearly 15 years, the radiation exposure of head and neck biopsies decreased.

Comparison of large-volume 3D reconstruction using plasma FIB-SEM and X-ray CT.

We demonstrated large-volume three-dimensional (3D) reconstruction using plasma-focused ion beam-scanning electron microscopy (PFIB-SEM). We successfully reconstructed a 750 µm (W) × 143 µm (H) × 310 µm (D) volume at a resolution of 200 nm/pix from 1550 SEM backscattered electron images of a Li-ion battery cathode sheet. The PFIB-SEM system was found to be capable of acquiring and reconstructing larger volume 3D datasets than X-ray computed tomography, and with higher resolution and contrast.