X-ray Data Research Articles

Small topographical variations controlling trace maker community: Combining palaeo- and neoichnological data at the Porcupine Abyssal Plain

Ichnological research has generally assumed that abyssal plains are dominated by quiescent, homogenous environmental conditions. Thus, deep-sea trace fossil assemblage changes have been usually linked to significant spatial and temporal environmental variations. Here, we conducted a comparative ichnological analysis between a small abyssal hill (50 m elevation) and the surrounding abyssal plain; this modest bathymetric variation is known to generate substantial environmental heterogeneity for the benthic fauna community of the Porcupine Abyssal Plain (c. 4850 m depth), Northeast Atlantic. Based on X-ray data from a 5 × 5 core grid emplaced in two box cores, we compared hill and plain bioturbational sedimentary structures, including trace fossil assemblages (e.g., ichnotaxonomy) and biodeformational structures (e.g., mixed-layer depth). We observed that topographically-enhanced near-bottom currents over the hill likely produce significant changes in depositional dynamics and sediment properties (e.g., grain size, organic matter content and degradation), and control specificities of bioturbational sedimentary structures (e.g., trace fossils, mixed layer attributes such as thickness, mottled background, discrete traces). Palaeoichnological data suggested that the abyssal plain had experienced consistent conditions during the last thousands of years while the abyssal hill recorded improving environmental conditions for the trace maker community. Our results highlight the complexity of the deep-sea environment, demonstrating that small changes in bioturbated sedimentary assemblages appear even within the same box core (m-scale), and that substantial changes can occur due to environmental heterogeneity (e.g., subtle topographic variations) at the local scale (km-scale). Considering the vast global extent of abyssal hill terrain, we suggest that their influence on the bioturbational sedimentary record may be significantly under-appreciated and require more attention in palaeoenvironmental reconstructions.

Encapsulating Azolates Within Cationic Metal-Organic Frameworks for High-Energy-Density Materials.

Despite the synthesis of numerous cationic metal-organic frameworks (CMOFs), their counter anions have been primarily limited to inorganic Cl-, NO3 -, ClO4 -, BF4 -, and Cr2O7 2-, which have weak coordination abilities. In this study, a series of new CMOFs is synthesized using azolates with strong coordination abilities as counter anions, which are exclusively employed as ligands for coordinating with metals. Owing to the unique nitrogen-rich composition of azolates, the CMOFs demonstrate significant potential as high-energy-density materials. Notably, CMOF(CuTNPO) has an exceptionally high heat of detonation of 7375kJ kg-1, surpassing even that of the state-of-art CL-20 (6536kJ kg-1). To further validate the advantages of employing azolates as counter anions, analogues with azolates serving as ligands are also synthesized. The comparison study indicates that encapsulating azolates within the cationic frameworks confers both high energy and safety properties. X-ray data and quantum calculations indicate that their enhanced performance stems from stronger H─bonds and π-π interactions. This study introduces new roles for azolates in MOFs and expands possibilities for structural diversity and potential applications of framework materials.

High-resolution X-ray spectra of the compact binary supersoft X-ray source CAL 87

In this study we present an analysis of the archival X-ray data of the eclipsing supersoft X-ray binary CAL 87 observed with the Chandra Advanced CCD Imaging Spectrometer (ACIS) camera and Low Energy Transmission Grating (LETG) in 2001 August and with XMM-Newton in 2003 April. The high-resolution X-ray spectra are almost unchanged on the two different dates. The average unabsorbed X-ray luminosity during the exposure was 4.64 − 5.46 × 1036 ergs s−1 in 2001 and 4.54 − 4.82 × 1036 ergs s−1 in 2003, with prominent and redshifted emission lines, mostly of nitrogen, oxygen, iron, and argon, contributing to at least 30% of the X-ray flux. The continuum X-ray flux is at least an order of magnitude too low for a hot hydrogen-burning white dwarf (WD). However, the continuum flux is consistent with Thomson-scattering reflecting about 5% of the light of a hydrogen-burning WD with effective temperature of 800 000 K and a mass of ∼1.2 M⊙. It has been noted that a large Thomson-scattering corona explains the X-ray eclipse of CAL 87, in which the size of the eclipsed region is found to be on the order of a solar radius. The emission lines originate in an even more extended region beyond the eclipsed central X-ray source; the emission spectrum is very complex, with unusual line ratios.

Theoretical Modeling of the Exceptional GRB 221009A Afterglow

The extraordinary gamma-ray burst GRB 221009A provides a great opportunity to investigate the enigmatic origin and evolution of gamma-ray bursts (GRBs). However, the complexity of the observations associated with this GRB provides significant challenges to developing a theoretical modeling in a coherent framework. In this paper, we present a theoretical interpretation of the GRB 221009A afterglow within the relativistic fireball scenario, aiming to describe the broadband data set with a consistent model evolution. We find that the adiabatic fireball evolution in the slow-cooling regime provides a viable scenario in good agreement with observations. Crucial to our analysis is the set of simultaneous GeV and TeV gamma-ray data obtained by AGILE and LHAASO during the early afterglow phases. Having successfully modeled as inverse Compton emission the high-energy spectral and lightcurve properties of the afterglow up to 104 s, we extend our model to later times when also optical and X-ray data are available. This approach results in a coherent physical framework that successfully describes all observed properties of the afterglow up to very late times, approximately 106 s. Our model requires time-variable microphysical parameters, with a moderately increasing efficiency ε e of a few percent for transferring the shock energy to radiating particles and a decreasing efficiency for magnetic field generation ε B in the range 10−5–10−7. Fitting the detailed multifrequency spectral data across the afterglow provides a unique test of our model.

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. Multi-view lesion correspondence is an important task that clinicians perform for characterizing lesions during routine mammographic exams. Automated registration tools can aid in this task, yet if the tools also provide confidence estimates, they can be of greater value to clinicians, especially in cases involving dense tissue where lesions may be difficult to see. A set of deep ensemble-based techniques, which leverage a negative log-likelihood (NLL)-based cost function, are implemented for estimating uncertainties. The ensemble architectures involve significant modifications to an existing CNN dual-view lesion registration algorithm. Three architectural designs are evaluated, and different ensemble sizes are compared using various performance metrics. The techniques are tested on synthetic X-ray data, real 2D X-ray data, and slices from real 3D X-ray data. The ensembles generate covariance-based uncertainty ellipses that are correlated with registration accuracy, such that the ellipse sizes can give a clinician an indication of confidence in the mapping between the CC and MLO views. The results also show that the ellipse sizes can aid in improving computer-aided detection (CAD) results by matching CC/MLO lesion detects and reducing false alarms from both views, adding to clinical utility. The uncertainty estimation techniques show promise as a means for aiding clinicians in confidently establishing multi-view lesion correspondence, thereby improving diagnostic capability.

Jaxspec : A fast and robust Python library for X-ray spectral fitting

Inferring spectral parameters from X-ray data is one of the cornerstones of high-energy astrophysics, and is achieved using software stacks that have been developed over the last 20 years and more. However, as models get more complex and spectra are obtained with higher resolutions, these established software solutions become more feature-heavy, difficult to maintain and less efficient. We present jaxspec a Python package for performing this task quickly and robustly in a fully Bayesian framework. Based on the JAX ecosystem jaxspec allows the generation of differentiable likelihood functions compilable on core or graphical process units (GPUs), enabling the use of robust algorithms for Bayesian inference. We demonstrate the effectiveness of jaxspec samplers, in particular the no U-turn sampler, using a composite model and comparing what we obtain with the existing frameworks. We also demonstrate its ability to process high-resolution spectroscopy data using original methods by reproducing the results of the Hitomi collaboration on the Perseus cluster, while solving the inference problem using variational inference on a GPU. We obtain identical results when compared to other software and approaches, meaning that jaxspec provides reliable results while being $ 10$ times faster than existing alternatives. In addition, we show that variational inference can produce convincing results even on high-resolution data in less than 10 minutes on a GPU. With this package, we aim to pursue the goal of opening up X-ray spectroscopy to the existing ecosystem of machine learning and Bayesian inference, enabling researchers to apply new methods to solve increasingly complex problems in the best possible way. Our long-term ambition is the scientific exploitation of the data from the newAthena X-ray Integral Field Unit (X-IFU).

The Chandra Source Catalog Release 2 Series

The Chandra Source Catalog (CSC) is a virtual X-ray astrophysics facility that enables both detailed individual source studies and statistical studies of large samples of X-ray sources detected in Advanced CCD Imaging Spectrometer and High Resolution Camera-I imaging observations obtained by the Chandra X-ray Observatory. The catalog provides carefully curated, high-quality, and uniformly calibrated and analyzed tabulated positional, spatial, photometric, spectral, and temporal source properties, as well as science-ready X-ray data products. The latter includes multiple types of source- and field-based FITS format products that can be used as a basis for further research, significantly simplifying follow-up analysis of scientifically meaningful source samples. We discuss in detail the algorithms used for the CSC Release 2 Series, including CSC 2.0, which includes 317,167 unique X-ray sources on the sky identified in observations released publicly through the end of 2014, and CSC 2.1, which adds Chandra data released through the end of 2021 and expands the catalog to 407,806 sources. Besides adding more recent observations, the CSC Release 2 Series includes multiple algorithmic enhancements that provide significant improvements over earlier releases. The compact source sensitivity limit for most observations is ∼5 photons over most of the field of view, which is ∼2× fainter than Release 1, achieved by coadding observations and using an optimized source detection approach. A Bayesian X-ray aperture photometry code produces robust fluxes even in crowded fields and for low-count sources. The current release, CSC 2.1, is tied to the Gaia-CRF3 astrometric reference frame for the best sky positions for catalog sources.

Separating states in astronomical sources using hidden Markov models: with a case study of flaring and quiescence on EV Lac

ABSTRACT We present a new method to distinguish between different states (e.g. high and low, quiescent and flaring) in astronomical sources with count data. The method models the underlying physical process as latent variables following a continuous-space Markov chain that determines the expected Poisson counts in observed light curves in multiple passbands. For the underlying state process, we consider several autoregressive processes, yielding continuous-space hidden Markov models of varying complexity. Under these models, we can infer the state that the object is in at any given time. The continuous state predictions from these models are then dichotomized with the help of a finite mixture model to produce state classifications. We apply these techniques to X-ray data from the active dMe flare star EV Lac, splitting the data into quiescent and flaring states. We find that a first-order vector autoregressive process efficiently separates flaring from quiescence: flaring occurs over 30 per cent–40 per cent of the observation durations, a well-defined persistent quiescent state can be identified, and the flaring state is characterized by higher plasma temperatures and emission measures.

Multi-wavelength spectroscopic analysis of the ULX Holmberg II X-1 and its nebula suggests the presence of a heavy black hole accreting from a B-type donor

Ultra-luminous X-ray sources (ULXs) are high-mass X-ray binaries with an X-ray luminosity above $10^ $. These ULXs can be powered by black holes that are more massive than $20M_ accreting in a standard regime, or lighter compact objects accreting supercritically. There are only a few ULXs with known optical or ultraviolet (UV) counterparts, and their nature is debated. Determining whether optical/UV radiation is produced by the donor star or by the accretion disc is crucial for understanding ULX physics and testing massive binary evolution. We conduct, for the first time, a fully consistent multi-wavelength spectral analysis of a ULX and its circumstellar nebula. We aim to establish the donor star type and test the presence of strong disc winds in the prototypical ULX Holmberg\,II X-1 ( Furthermore, we aim to obtain a realistic spectral energy distribution of the ionising source, which is needed for robust nebula analysis. We acquired new UV spectra of with the Hubble Space Telescope ( ) and complemented them with archival optical and X-ray data. We explored the spectral energy distribution of the source and analysed the spectra using the stellar atmosphere code and the photoionisation code Our analysis of the X-ray, UV, and optical spectra of and its nebula consistently explains the observations. We do not find traces of disc wind signatures in the UV and the optical, rejecting previous claims of the ULX being a supercritical accretor. The optical/UV counterpart of is explained by a B-type supergiant donor star. Thus, the observations are fully compatible with being a close binary consisting of an $ 66\,M_ black hole accreting matter from an $ 22\,M_ B-supergiant companion. Furthermore, we propose a possible evolution scenario for the system, suggesting that is a potential gravitational wave source progenitor.

The symbiotic recurrent nova V745 Sco at radio wavelengths

Abstract V745 Sco is a Galactic symbiotic recurrent nova with nova eruptions in 1937, 1989 and 2014. We study the behaviour of V745 Sco at radio wavelengths (0.6–37 GHz), covering both its 1989 and 2014 eruptions and informed by optical, X-ray, and γ-ray data. The radio light curves are synchrotron-dominated. Surprisingly, compared to expectations for synchrotron emission from explosive transients such as radio supernovae, the light curves spanning 0.6–37 GHz all peak around the same time (∼18–26 days after eruption) and with similar flux densities (5–9 mJy). We model the synchrotron light curves as interaction of the nova ejecta with the red giant wind, but find that simple spherically symmetric models with wind-like circumstellar material (CSM) cannot explain the radio light curve. Instead, we conclude that the shock suddenly breaks out of a dense CSM absorbing screen around 20 days after eruption, and then expands into a relatively low density wind ($\dot{M}_{out} \approx 10^{-9}-10^{-8}$ M⊙ yr−1 for vw = 10 km s−1) out to ∼1 year post-eruption. The dense, close-in CSM may be an equatorial density enhancement or a more spherical red giant wind with $\dot{M}_{in} \approx [5-10] \times 10^{-7}$ M⊙ yr−1, truncated beyond several × 1014 cm. The outer lower-density CSM would not be visible in typical radio observations of Type Ia supernovae: V745 Sco cannot be ruled out as a Type Ia progenitor based on CSM constraints alone. Complementary constraints from the free–free radio optical depth and the synchrotron luminosity imply the shock is efficient at accelerating relativistic electrons and amplifying magnetic fields, with εe and εB ≈ 0.01 − 0.1.

Substitutional/positional disorder of biguanide and guanylurea in the structure of a decavanadate complex [(Bg)(HV10O285-)]0.4[(HGU+)(V10O286-)]0.6(H2Met2+)2(H3O+)·8H2O.

A hydrated salt of decavanadate containing diprotonated metforminium(2+) (H2Met2+), hydronium (H3O+) and either neutral biguanide (Bg) or monoprotonated guanylurea (HGU+) exhibits a previously seen complex charge-stabilized hydrogen-bonded network [Chatkon et al. (2022). Acta Cryst. B78, 798-808]. Charge balance is achieved in two ways through substitutional disorder: a 0.6 occupied HGU+ cation is paired with a V10O286- anion, and a 0.4 occupied neutral Bg molecule is paired with a HV10O285- anion, with the remaining charge in both cases balanced by two H2Met2+ dications and one H3O+ monocation. Bg/HGU+ moieties exhibit bifurcated N-H...O hydrogen bonding to the H3O+ cation and are substitutionally/positionally disordered along with the H3O+ cation about an inversion center. The HGU+ V10O286- synthon seen in the previous study occurs again. Bg exhibits bifurcated hydrogen bonding from two amino groups to two rows of cluster O atoms running diagonally across the equatorial plane of the HV10O285- anion with a return hydrogen bond from the cluster H atom to the imino N atom of the Bg. Thus, a Bg...cluster synthon similar to the HGU+...cluster synthon previously reported is found. The disordered moieties occupy spaces with excess volume in the 3-D network structure. Interestingly, when the crystallographic unit cell of the current compound, whose X-ray data was collected at 100 K, is compared with that of a previous compound exhibiting the same supramolecular framework, unit-cell parameter c does not shorten as a and b expectantly do because of the lower data collection temperature. The lack of contraction on unit-cell parameter c is possibly due to the supramolecular structure.

The high-energy environment of the heavy sub-Earth GJ 367 b indicates likely complete evaporation of its atmosphere

The planet GJ 367 b is a recently discovered high-density sub-Earth orbiting an M dwarf star. Its composition was modelled to be predominantly iron with a potential remainder of a hydrogen-helium envelope. Here we report an X-ray detection of this planet’s host star for the first time, using data from the spectro-imaging X-ray telescope eROSITA onboard the Spectrum-Roentgen-Gamma mission. We characterise the magnetic activity of the host star from the X-ray data and estimate its effects on a potential atmosphere of the planet. We find that despite the very low activity level of the host star the expected mass loss rates, both under core-powered and photoevaporative mass loss regimes, are so high that a potential primordial or outgassed atmosphere would evaporate very quickly. Since the activity level of the host star indicates that the system is several Gigayears old, it is very unlikely that the planet currently still hosts any atmosphere.

Exploring the synthesis, characterization, crystal structures, DFT calculation, hirshfeld surface analysis, and anticancer activity of Ni(II) complex of 1-(4-nitrobenzoyl)-4-phenyl-3-thiosemicarbazide

The chemistry of biologically active thiosemicarbazide has been of interest to researchers owing to its diverse application and bonding features. In the present study, a new thiosemicarbazide derivative namely 1-(4-nitrobenzoyl)-4-phenyl-3-thiosemicarbazide (HnbphTSC) has been prepared and along with its [Ni(nbphTSC)2(en)]·CHCl3 (1) complex. The prepared ligand (HnbphTSC) and complex 1 have been characterized through spectroscopic and single-crystal X-ray data. The HnbphTSC and complex 1 crystallize in triclinic and monoclinic crystal systems with space groups P1¯ and P21/C, respectively. The weak interactions present in HnbphTSC and complex 1 have been quantitively studied through hirshfeld surface analysis. The physiochemical characteristics of HnbphTSC and complex 1 are also verified using DFT calculations, and the resultant results are in strong agreement with the experimental findings. The HOMO and LUMO energy gap have been calculated as 3.160 eV and 2.212 eV for HnbphTSC and complex 1, respectively. MTT assay was used to evaluate the cytotoxicity of HnbphTSC and complex 1 against Dalton's lymphoma (DL) cells. We further assessed the mode of tumor cell death and the drug's effect on nuclear condensation and plasma membrane integrity by employing DAPI/PI dual staining technique.

X-Ray Variability in the Symbiotic Binary RT Cru: Principal Component Analysis

Hard X-ray-emitting (δ-type) symbiotic binaries, which exhibit a strong hard X-ray excess, have posed a challenge to our understanding of accretion physics in degenerate dwarfs. RT Cru, which is a member of the δ-type symbiotics, shows stochastic X-ray variability. Timing analyses of X-ray observations from XMM-Newton and NuSTAR, which we consider here, indicate hourly fluctuations, in addition to a spectral transition from 2007 to a harder state in 2012 seen with Suzaku observations. To trace the nature of X-ray variability, we analyze the multimission X-ray data using principal component analysis (PCA), which determines the spectral components that contribute most to the flickering behavior and the hardness transition. The Chandra HRC-S/LETG and XMM-Newton EPIC-pn data provide the primary PCA components, which may contain some variable emission features, especially in the soft excess. Additionally, the absorbing column (first order with 50%), along with the source continuum (20%), and a third component (9%)—which likely accounts for thermal emission in the soft band—are the three principal components found in the Suzaku XIS1 observations. The PCA components of the NuSTAR data also correspond to the continuum and possibly emission features. Our findings suggest that the spectral hardness transition between the two Suzaku observations is mainly due to changes in the absorbing material and X-ray continuum, while some changes in the thermal plasma emission may result in flickering-type variations.

Importance of powder diffraction raw data archival in a curated database for materials science applications.

In recent years, there is a significant interest from the crystallographic and materials science communities to have access to raw diffraction data. The effort in archiving raw data for access by the user community is spearheaded by the International Union of Crystallography (IUCr) Committee on Data. In materials science, where powder diffraction is extensively used, the challenge in archiving raw data is different to that from single crystal data, owing to the very nature of the contributions involved. Powder diffraction (X-ray or neutron) data consist of contributions from the material under study as well as instrument specific parameters. Having raw powder diffraction data can be essential in cases of analysing materials with poor crystallinity, disorder, micro structure (size/strain) etc. Here, the initiative and progress made by the International Centre for Diffraction Data (ICDDR) in archiving powder X-ray diffraction raw data in the Powder Diffraction FileTM (PDFR) database is outlined. The upcoming 2025 release of the PDF-5+ database will have more than 20 800 raw powder diffraction patterns that are available for reference.

Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data

In this study, we address critical barriers hindering the widespread adoption of surgical navigation in orthopedic surgeries due to limitations such as time constraints, cost implications, radiation concerns, and integration within the surgical workflow. Recently, our work X23D showed an approach for generating 3D anatomical models of the spine from only a few intraoperative fluoroscopic images. This approach negates the need for conventional registration-based surgical navigation by creating a direct intraoperative 3D reconstruction of the anatomy. Despite these strides, the practical application of X23D has been limited by a significant domain gap between synthetic training data and real intraoperative images.In response, we devised a novel data collection protocol to assemble a paired dataset consisting of synthetic and real fluoroscopic images captured from identical perspectives. Leveraging this unique dataset, we refined our deep learning model through transfer learning, effectively bridging the domain gap between synthetic and real X-ray data. We introduce an innovative approach combining style transfer with the curated paired dataset. This method transforms real X-ray images into the synthetic domain, enabling the in-silico-trained X23D model to achieve high accuracy in real-world settings.Our results demonstrated that the refined model can rapidly generate accurate 3D reconstructions of the entire lumbar spine from as few as three intraoperative fluoroscopic shots. The enhanced model reached a sufficient accuracy, achieving an 84% F1 score, equating to the benchmark set solely by synthetic data in previous research. Moreover, with an impressive computational time of just 81.1 ms, our approach offers real-time capabilities, vital for successful integration into active surgical procedures.By investigating optimal imaging setups and view angle dependencies, we have further validated the practicality and reliability of our system in a clinical environment. Our research represents a promising advancement in intraoperative 3D reconstruction. This innovation has the potential to enhance intraoperative surgical planning, navigation, and surgical robotics.

Rietveld analysis and cation distribution of Co substituted Ni–Zn ferrite nanoparticles

This study investigates the cation distribution in Co-substituted Ni–Zn ferrite nanoparticles synthesized via the sol-gel auto-combustion method. The research aims to understand the modifications in cationic distributions at the nanoscale compared to bulk counterparts and their impact on electromagnetic properties. Rietveld structural analysis, lattice constant determination, and magnetic moment measurements were employed to analyze the nanoparticles. The results reveal a single-phase spinel structure with cubic symmetry, crystallite sizes between 26.2 and 36.4 nm, and saturation magnetizations ranging from 50 to 68 emu/g. Results and calculated cation distributions of these ferrite nanoparticles were evaluated in terms of compositional modifications, particle sizes, and related inversion degree parameters. The study confirms the cation distributions based on X-ray and magnetic data, highlighting the importance of cationic inversions in designing nanoscale ferrites with improved electromagnetic characteristics for electronic applications.

Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning

Obstructive sleep apnea (OSA) is closely associated with the development and chronicity of temporomandibular disorder (TMD). Given the intricate pathophysiology of both OSA and TMD, comprehensive diagnostic approaches are crucial. This study aimed to develop an automatic prediction model utilizing multimodal data to diagnose OSA among TMD patients. We collected a range of multimodal data, including clinical characteristics, portable polysomnography, X-ray, and MRI data, from 55 TMD patients who reported sleep problems. This data was then analyzed using advanced machine learning techniques. Three-dimensional VGG16 and logistic regression models were used to identify significant predictors. Approximately 53% (29 out of 55) of TMD patients had OSA. Performance accuracy was evaluated using logistic regression, multilayer perceptron, and area under the curve (AUC) scores. OSA prediction accuracy in TMD patients was 80.00–91.43%. When MRI data were added to the algorithm, the AUC score increased to 1.00, indicating excellent capability. Only the obstructive apnea index was statistically significant in predicting OSA in TMD patients, with a threshold of 4.25 events/h. The learned features of the convolutional neural network were visualized as a heatmap using a gradient-weighted class activation mapping algorithm, revealing that it focuses on differential anatomical parameters depending on the absence or presence of OSA. In OSA-positive cases, the nasopharynx, oropharynx, uvula, larynx, epiglottis, and brain region were recognized, whereas in OSA-negative cases, the tongue, nose, nasal turbinate, and hyoid bone were recognized. Prediction accuracy and heat map analyses support the plausibility and usefulness of this artificial intelligence-based OSA diagnosis and prediction model in TMD patients, providing a deeper understanding of regions distinguishing between OSA and non-OSA.

Evaluation of Femoral Head Avascular Necrosis With Virtual Noncalcium Dual-Energy Computed Tomography.

Our aim was to investigate the effectiveness of the dual-energy computed tomography (DECT) virtual noncalcium (VNCa) technique in avascular necrosis (AVN) for detecting bone marrow edema (BME) and staging. This prospective study included adult patients diagnosed with unilateral or bilateral femoral head AVN between January 2023 and December 2023, who had magnetic resonance imaging (MRI) and DECT. Two participants were excluded from the study due to undergoing surgical procedures during the period between the scans. Two reviewers, blinded to MRI images and clinical data, visually examined color-coded VNCa pictures to assess BME using a binary classification (0 = normal bone marrow, 1 = BME). Same 2 reviewers also used color-coded and nonmapped images to stage AVN in accordance to the "Association for Research on Osseous Circulation" (ARCO) staging system. Interobserver agreements for the visual evaluation and staging were calculated with κ coefficient. Following a visual assessment of BME and the staging of AVN, same 2 reviewers conducted CT density measurements on regions of BME regions utilizing DECT noncalcium images. An independent third investigator (reference standard) utilized MRI, x-ray, and clinical data to confirm the definitive diagnosis and staging of AVN. A P value less than 0.05 was considered statistically significant. Fifty patients (28 men, 22 women, mean age: 44.2 ± 13.1 years, range: 25-75 years) were included in the final analysis. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the VNCa technique in detecting BME were 96.0%, 94.4%, 97.9%, 89.4%, and 95.6%, respectively, for reviewer 1; and 96.0%, 88.9%, 96.0%, 88.9%, and 94.1%, respectively, for reviewer 2. Interobserver agreement was almost perfect ( κ = 0.84). Both reviewer 1 and reviewer 2 accurately classified 92.7% of the AVNs. The density measurements showed a statistically significant difference ( P = 0.001) between the edema regions and the normal marrow regions. No statistically significant difference was observed in the density measurements of edema regions at different stages ( P = 0.13). DECT VNCa technique exhibits excellent performance in detecting BME in hip AVN cases, as well as accurately determining the stage of AVN.

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.