Ray Imaging Research Articles

The MeerKAT view on Galactic supernova remnants

The integrated radio spectrum of supernova remnants (SNRs) and the spatial variation of the spectral indices across these extended sources are powerful tools for studying the shocks and particle acceleration processes occurring in different SNR regions. Characterization of these processes requires sensitive flux density measurements and high-resolution images, which are not always available due to observing difficulties. We want to show the potentiality of the high-resolution SARAO MeerKAT legacy Galactic Plane Survey (SMGPS) images regarding the morphological and spectral characterization of 29 known galactic SNRs. We used the SMGPS data at $1.284$ GHz coupled with data from the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) survey ($0.072-0.231$ GHz) to characterize the integrated spectrum of each source and search for spatial spectral variation through analysis of sensitive spectral index maps. We were able to redefine the exact morphology of four SNRs (G024.7-00.6, G051.4+00.7, G348.7+0.3, and G351.9+00.1), distinguishing them from unrelated sources or identifying new emission regions associated with them and never observed before. In many other cases, we identified in the SMGPS images several overlaid with the remnants, and we were able to estimate their spectral contribution through inspection of the spatial variation of the spectral indices across the remnants. The integrated spectral indices show a more uniform distribution with respect to what is obtained by considering the values reported in the literature. We show that new sensitive and high-resolution data are crucial to firmly constraining both the integrated and spatially resolved spectrum of SNRs, especially for the less studied objects of the southern hemisphere. The comparison of our SMGPS-GLEAM spectral index maps with IR, molecular, and gamma -ray images allowed us to investigate the nature of the peculiar remnant regions.

Multislit gamma camera for external beam radiotherapy assistance: Experimental proof of concept

The orthogonal computed tomography (OrthoCT) concept, based on orthogonal ray imaging, is a low-dose imaging technique currently under investigation to potentially aid in external-beam radiation therapy treatments. This technique involves detecting radiation scattered within the patient and emitted at approximately 90° from the direction of the incoming beam. This scattered radiation can be collected by a 1D-detector system with a multisliced collimator positioned perpendicular to the incident beam axis. Such a system holds promise for on-board imaging with the patient positioned and prepared for treatment, as well as for real-time treatment monitoring. In this study, a multi-slice OrthoCT detector prototype was developed and tested under in-beam irradiation. The system utilizes gadolinium orthosilicate scintillator crystals coupled to photomultiplier tubes and a collimator made of lead slices. Experimental measurements were conducted using a heterogeneous phantom made of acrylic with an air cavity inside. The phantom was irradiated with a TrueBeam linac operating at 6 MV in the flattening-filter-free mode. The findings of this study indicate that this innovative imaging technique is capable of providing morphological images of the phantom. This accomplishment is achieved without the need to rotate the X-ray source around the object to be irradiated, demonstrating the feasibility of such a system.

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies

The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model’s performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.

Mask structure optimization for beyond EUV lithography.

Beyond extreme ultraviolet (BEUV) lithography with a 6 × nm wavelength is regarded as a future technique to continue the pattern shirking in integrated circuit (IC) manufacturing. This work proposes an optimization method for the mask structure to improve the imaging quality of BEUV lithography. Firstly, the structure of mask multilayers is optimized to maximize its reflection coefficient. Then, a mask diffraction near-field (DNF) model is established based on the Born series algorithm, and the aerial image of BEUV lithography system can be further calculated. Additionally, the mask absorber structure is inversely designed using the particle swarm optimization (PSO) algorithm. Simulation results show a significant improvement of the BEUV lithography imaging obtained by the proposed optimization methods. The proposed workflow can also be expanded to areas of EUV and soft x ray imaging.

Design of elemental-image-predistortion and splicing seamless integral near-eye display based on aspherical microlens arrays

The near-eye display (NED) has long been a challenging and intriguing research focus in the fields of augmented reality (AR) and virtual reality (VR). In this paper, we propose a design method of elemental-image (EI) predistortion and splicing seamless integral NED based on microlens arrays (MLAs), which combines rigorous optical design with image preprocessing. The construction of MLA is based on sub-channel optimization and splicing, enabling the realization of both VR-NED and AR-NED. The calibration of each sub-channel in NED is conducted using a perfect lens that possesses an effective focal length equivalent to that of the human eye. To achieve a high-precision seamless integral NED, the precise location and acquisition of initial EIs are accomplished through chief ray tracing and image clipping. The predistortion EI array (EIA) is further obtained by combining chief ray tracing and radial basis function (RBF) image warping techniques. A seamless MLA-based integral AR-NED with a full field of view (FOV) of 60° is realized and verified. A prototype of a seamless MLA-based integral VR-NED is further implemented by integrating multiple discrete single-focal predistortion EIAs. The effectiveness of the proposed method has been validated through illumination and luminance simulations as well as experiments.

Synthetic X-ray Image Generation for Non-Destructive Testing using Generative Adversarial Networks

Developing and optimizing a Machine Learning (ML) based Non-Destructive Testing (NDT) tool for automotive manufacturing can be challenging due to the need for a significant amount of annotated training data. Obtaining such an extent of real X-ray images of different defects can be challenging and expensive. Hence, generating synthetic images with adequate defects is a viable option to fill this gap. This contribution presents a novel approach that harnesses Generative Adversarial Networks (GANs) to simulate industrial X-ray images of specific parts of a car wheel (rim and spokes) with defects and their annotations. The presented method draws inspiration from techniques used in medical healthcare radiography as well as the NDT field for simulating synthetic X-ray images. We propose a novel deep neural network architecture for generating high-resolution X-ray images of size 1024 x 512 pixels with porous defects in specified locations. These images are automatically generated from edge masks of the desired structure. We conducted separate evaluations for both, the generated images and the included defects. To assess the generated image quality, we used image properties and structural similarity metrics. The generated images achieved an MSSIM index of over 0.90. We analyzed the local gray-scale profile of synthetic images to evaluate the quality of generated defects. A state-of-the-art defect detector (ISAR, developed by Fraunhofer EZRT) was used to estimate defect detection based on various IoU thresholds. In conclusion, the presented research demonstrates the feasibility of leveraging GANs to generate synthetic 2D X- ray image data, including defects for training ML-based NDT tools. This approach opens new possibilities for developing and optimizing robust NDT solutions by overcoming the challenges of obtaining real data with defects.

Ultrastable and flexible glass−ceramic scintillation films with reduced light scattering for efficient X−ray imaging

The thickness of the scintillation films in indirect X−ray detectors can significantly influence their luminescence intensity. However, due to the scattering and attenuation of incoherent photons, thick scintillation films tend to reduce light yield. Herein, a highly transparent perovskite glass−ceramic scintillation film, in which the CsPbBr3 nanocrystals are in-situ grown inside a transparent amorphous polymer structure, is designed to achieve ultrastable and efficient X-ray imaging. The crystal coordination−topology growth and in−situ film formation strategy is proposed to control the crystal growth and film thickness, which can prevent light scattering and non−uniform distribution of CsPbBr3 nanocrystals while providing sufficient film thickness to absorb X−ray, thus enabling a high−quality glass−ceramic scintillator without agglomeration and Ostwald ripening. This glass−ceramic scintillation film with a thickness of 250 μm achieves a low detection limit of 326 nGyair s−1 and a high spatial resolution of 13.9 lp mm−1. More importantly, it displays remarkable scintillation stability under X−ray irradiation (radiation intensity can still reach 95% at 278 μGyair s−1 for 3600 s), water soaking (150 days), and high−temperature storage (150 days at 60 °C). Hence, this work presents a approach to construct ultrastable and flexible scintillation films for X−ray imaging with reduced light scattering and improved resolution.

Tuberculosis Detection and Air Purifier Suggestion

Tuberculosis (TB) is a disease that can be fatal if not promptly treated. Ensemble deep learning methods have shown promise in aiding the early detection of TB. Previous research typically trained ensemble classifiers using images with similar features, but for optimal performance, an ensemble requires a range of errors, achievable through diverse classification techniques or feature sets. This study focuses on the latter approach, presenting TB detection using deep learning alongside contrast-enhanced canny edge detected (CEED-Canny) X-ray images. CEED-Canny was employed to generate edge-detected lung X-ray images. Two sets of features were derived: one from the enhanced X- ray images and the other from the edge-detected images. By introducing this variation in features, the diversity of errors among the base classifiers was increased, resulting in improved TB detection. The proposed ensemble method achieved a comparable accuracy of 93.59%, sensitivity of 92.31%, and specificity of 94.87% compared to prior research.

The Review of Modeling and Rendering 3D Environment: A Survey

This paper explores several topics in the field of 3D Environments, including ray tracing, rendering techniques, algorithms and optimization, data processing, real-time rendering, modeling, physical simulation, and material modeling. The paper covers a large variety of applications and techniques that are widely used in computer graphics and visualization. This paper also points out that significant progress has been made in ray tracing, texture filtering, image processing, motion synthesis, geometric modeling, and physical simulation. These advances have improved the visual quality, performance, and fidelity of computer-generated images. In addition, the article mentions the trend of integrating machine learning, deep learning, and data analysis techniques into computer graphics and visualization, especially in data processing and analysis models, to improve the results of rendering and simulation. Overall, this article explores details of the current state of the computer graphics and visualization field, highlighting the various technologies and applications which shapes the field.

X-Ray Fluorescence Analysis of Historic Art Paint Pigments

Utilizing synchrotron radiation, X-ray fluorescence (XRF) microscopy enables researchers to deduce the elemental composition of paint pigments with a higher sensitivity and resolution than that of lab-based XRF instruments. With this information, art historians can date paintings by examining the elemental makeup of paint pigments and the time periods in which they were used. One painting, Christ and the Woman Taken in Adultery, has been duplicated by Pieter Brueghel the Younger and other artists, leading to confusion over which artworks are Brueghel masterpieces or copies by other artists. Art historian Maurizio Seracini, retaining a painting that could be assigned to Brueghel, confirmed the artwork was a reproduction not created by Brueghel. With radiocarbon dating, Dr. Seracini discovered that the painting was created between 1679 and 1939, after Brueghel’s death in 1638. Dr. Seracini also employed X- ray imaging to differentiate between the painting’s visible and lower layers. This technique uncovered a seated woman within the artwork’s lower layers, indicating that the artist has painted over the original artwork. Substantiating Dr. Seracini’s findings, the sub-µm spatial resolution of Brookhaven National Laboratory’s (BNL’s) Submicron Resolution X-ray Spectroscopy (SRX) beamline was used to conduct an XRF microscopy analysis on a paint fragment from the artwork. The software PyXRF was used to identify elements based on emission spectra, contributing to the discovery of titanium within the painting. The presence of titanium, an element incorporated into paint pigments starting in 1921, indicates that the artwork may be more modern than previously thought. These results illustrate the applicability of synchrotron radiation in characterizing the physical and chemical properties of cultural heritage artifacts, supporting historians in their quests to discover the stories behind these objects.

Pneumonia Detection using CNN, Resnet and DenseNet

Abstract: Pneumonia is a prevalent respiratory infection that poses a significant threat to public health. Timely and precise diagnosis is essential for effective treatment and patient management. The proposed methodology involves training a CNN (Convolutional Neural Networks), ResNet-50 and DenseNet-121, on a large dataset of chest X ray images. CNNs play a crucial role in feature extraction from chest X-ray images for various computer vision tasks, including image recognition. The CNN model automatically learns and extracts essential visual features from the input images, capturing patterns and characteristics. On the other hand, ResNet-50 and DenseNet-121 leverage their effectiveness in handling deeper architectures and handling vanishing gradient problems. We compare our approach with existing methods to assess the quality and accuracy of the generated captions. The models undergo training and testing utilizing an extensive dataset comprising chest X-ray images, demonstrating high accuracy in detecting pneumonia, potentially offering a valuable tool for early diagnosis and treatment. The proposed pneumonia detection framework holds great promise for assisting healthcare professionals in diagnosing and treating pneumonia, thereby improving patient outcomes, and reducing healthcare costs.

A lightweight multi-feature fusion network for unmanned aerial vehicle infrared ray image object detection

UAV (Unmanned Aerial Vehicle) infrared object detection is crucial in pedestrian monitoring and traffic dispatch, which detects and locates objects in infrared images. In light of issues such as unnoticeable texture features and limited resolution of infrared image objects, a lightweight multi-scale feature fusion method for UAV infrared object detection is presented to enhance the performance of UAVs carrying intelligent devices to detect infrared objects. By changing the anchorless frame strategy of the YOLOX method, a lightweight Multi-Feature Fusion Network (MFFNet) for UAV infrared ray (IR) image object detection is proposed. First, a lightweight backbone network is built using ShuffleNetv2_block, spatial pyramid pooling, and other modules to reduce the network's number of parameters and inference time while maintaining its capacity to extract features. Second, we develop a multi-feature fusion module to improve the detection capabilities of the model for IR objects by fusing the local features and the overall characteristics of IR objects since the texture features of IR objects are challenging to employ, but the boundary information is evident. The boundary frame regression loss is then optimized using SCYLLA-IoU (SIoU) by comparing the predicted frame to the actual frame in terms of angle, distance, shape, and IoU (Intersection over Union), which forces the model to reach the optimum predicted box more quickly. The experimental results demonstrate that our method achieves an 81.5% mean average precision (mAP) with 4.21M parameters and an inference time of only 4.84ms per image, outperforming most networks in speed and accuracy.

Gamma-ray imaging of Np-237 metal using an organic glass imager

Neutron and gamma-ray imaging systems are deployed within the field of nuclear safeguards for the detection and localization of special nuclear materials and other materials of interest. 237Np is one of these materials of interest due its presence in spent nuclear fuel and potential for use in nuclear weapons when purified. Here, for the first time, a 6 kg neptunium sphere (98.8 wt% 237Np) was measured using a dual-particle imager, from the University of Michigan, consisting of organic glass and inorganic scintillators. The novel composition of organic glass scintillator was recently developed at Sandia National Labs and has been used in particle imaging systems due to its time resolution and particle discrimination capabilities. Gamma-ray energy spectra from single and coincident events were extracted and the sequencing of Compton scatter and photoelectric absorption gamma-ray events was used to generate images using simple backprojection. The emissions of interest in this work are the 312 keV and 416 keV gamma rays from 233Pa, a daughter isotope from the neptunium decay series. The results of this work show that there is close agreement between the true source location in angular space and the converged location from the gamma ray images created using the system. The gamma spectroscopy from single and coincident events also identified the characteristic emission from the daughter isotope and could be used to assist with the identification of 237Np. Furthermore, successful localization of the source with 5 s of data demonstrates the practical application of the imaging system for imaging and detection of material in weapons-useable quantities.

Covid-19 Pneumonia Level Detection Using Deep Learning Algorithm and Transfer Learning

This paper frames the improvement of a deep learning model intended for the exact discovery and order of pneumonia levels (Mild and Severe) in Coronavirus affirmed patients in light of chest X-beam pictures. The model's development includes the execution of two particular strategies. The primary strategy utilizes a convolutional neural network worked without any preparation to characterize pneumonia levels in Coronavirus affirmed patients. Conversely, the subsequent strategy uses transfer learning with pre-trainedd models for a similar order task. The essential goal of this study is to achieve raised degrees of precision, awareness, and particularity in separating pneumonia levels among Coronavirus affirmed patients by utilizing these two assorted techniques. Keywords—Chest X Ray Images, Transfer Learning, Convolutional Neural Network, Accuracy, Covid Pneumonia Levels.

Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images

Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessing for contrast enhancement and noise removal while addressing dataset imbalance through near-miss resampling. Ensemble learning techniques, including two and three-level methods, are employed to harness the strengths of individual base learners—VGG16, InceptionV3, and MobileNetV2. The model's performance is evaluated using metrics such as accuracy, recall, precision, and F1-score. For remote access, a user interface and a shared web link are developed using Python Gradio. Findings: In two-level ensembles, features from base learners are concatenated and classified using a support vector machine. Three-level ensembles use concatenated features classified by three machine learning classifiers, employing a majority voting system for the final prediction. The two-level method achieved 93% accuracy, precision, recall, and F1 score. The three-level ensemble model demonstrates superior performance, achieving 94% accuracy in detecting four lung diseases, namely COVID-19, pneumonia, lung opacity, and normal states. Novelty: This research contributes to the field by showcasing the efficacy of deep learning technology, particularly ensemble learning, in enhancing the detection of lung diseases from raw chest X-ray images. The model employs three modified and efficient pretrained networks for automatic feature extraction, eliminating the need for manual feature engineering. The developed model stands as a promising decision-support tool for healthcare professionals, particularly in low-resource environments. Keywords: Convolutional Neural Network (CNN), Deep Learning (DL), Transfer Learning (TL), Ensemble learning (EL), Fixed feature extraction, Chest X­rays (CXR), Lung diseases

Development of a Triple-GEM detector with strip readout and GEMINI chip for X rays and neutron imaging

Thermal neutron imaging can be a useful tool in the study of the internal structure of an object. The different attenuation properties of the materials with respect to X rays give rise to different interactions and the result is a complementary non-destructive analysis, which can provide important additional information. This technique has been successfully employed in different areas of work, especially in material science and cultural heritage studies. This paper describes the development of a new detection system and its characterization performed with X ray emissions. The system features the use of a gaseous detector, based on the Gas Electron Multiplier technology, and a fully digital electronic readout, with a combination of custom-made ASICs (called GEMINI) and FPGA boards, enabling fast single photon counting. The detector can be thus used directly for X ray imaging, while the addition of a suitable converter in its active volume will allow for detection of neutrons and for reconstruction of their tracks. The readout system is based on a x-y strip structure and features the reconstruction of single events through the center of mass methodology, allowing for accurate tomography, with sub-mm spatial resolution, in combination with sub-ms time resolution and high rate capabilities (up to MHz/mm2).

Characterization of organic glass scintillator bars and their potential for a hybrid neutron/gamma ray imaging system for proton radiotherapy range verification

For accurate and simultaneous imaging of fast neutrons (FNs) and prompt gamma rays (PGs) produced during proton therapy, the selection of a highly performant detector material is crucial. In this work, a promising candidate material known as organic glass scintillator (OGS) is characterized for this task. To this end, a precisely-timed source of neutrons and Bremsstrahlung radiation produced by the nELBE facility was used to study the light output and neutron/gamma ray pulse shape discrimination (PSD) properties of a 1 × 1 × 20 cm3 OGS bar with double-sided readout. Furthermore, the energy, timing, and depth-of-interaction (DOI) resolutions of 1 × 1 × 10 cm3 and 1 × 1 × 20 cm3 OGS and EJ-200 bars were characterized with radioactive sources. For electron-equivalent energies above 0.5 MeVee, OGS was found to have excellent PSD capabilities (figure-of-merit above 1.27), energy resolution (below 12%), coincident time resolution (below 500 ps), and DOI resolution (below 10 mm). This work establishes the data analysis methods required for hybrid FN/PG imaging using OGS, and demonstrates the materials' excellent performance for this application.

High-Confidentiality X-Ray Imaging Encryption Using Prolonged Imperceptible Radioluminescence Memory Scintillators.

X-ray imaging plays an increasingly crucial role in clinical radiography, industrial inspection, and military applications. However, current X-ray imaging technologies have difficulty in protecting against information leakage caused by brute force attacks via trial-and-error. Here high-confidentiality X-ray imaging encryption by fabricating ultralong radioluminescence memory films composed of lanthanide-activated nanoscintillators (NaLuF4 : Gd3+ or Ce3+ ) with imperceptible purely-ultraviolet (UV) emission is reported. Mechanistic investigations unveil that ultralong X-ray memory is attributed to the long-lived trapping of thermalized charge carriers within Frenkel defect states and subsequent slow release in the form of imperceptible radioluminescence. The encrypted X-ray imaging can be securely stored in the memory film for more than 7 days and optically decoded by perovskite nanocrystal. Importantly, this encryption strategy can protect X-ray imaging information against brute force trial-and-error attacks through the perception of lifetime change in the persistent radioluminescence. It is further demonstrated that the as-fabricated flexible memory film enables achieving of 3D X-ray imaging encryption of curved objects with a high spatial resolution of 20 lp/mm and excellent recyclability. This study provides valuable insights into the fundamental understanding of X-ray-to-UV conversion in nanocrystal lattices and opens up a new avenue toward the development of high-confidential 3D X-ray imaging encryption technologies.

X-Ray Flow Visualization: Techniques and Applications

Abstract Multiphase flows, defined as a discrete phase in a continuous fluid phase, are found in many natural, industrial, and consumer flows, from rain fall and avalanches to petroleum processing and fuel combustion to cookie dough mixing and pasta making. Many of these flows have an interior that is hidden from optical flow measurements, and intrusive probes can modify the flows of interest. Noninvasive measurement techniques, like X-ray flow visualization, provide a means to visualize and quantify the flow conditions in areas obstructed to visual access. Additionally, X-rays are unlikely to modify or alter the flow of interest. This paper reviews various X ray flow visualization techniques, including those using X-rays from tube sources, electron guns, and synchrotron sources. X-ray fundamentals are first reviewed. Then, various X ray imaging techniques are highlighted, and applications of those techniques are summarized using several multiphase flow examples. Advantages and disadvantages of each technique are provided and the unique flow features that can be captured with X-ray flow visualization are summarized. As detailed, X-ray flow visualization is a powerful tool for multiphase flow visualization and characterization, particularly when the flow of interest has limited or no optical access.

Dark-field and directional dark-field on low-coherence x ray sources with random mask modulations: validation with SAXS anisotropy measurements.

Phase-contrast imaging, dark-field, and directional dark-field imaging are recent x ray imaging modalities that have been demonstrated to reveal different information and contrast from those provided by conventional x ray imaging. Access to these new types of images is currently limited because the acquisitions require coherent sources such as synchrotron radiation or complicated optical setups. This Letter demonstrates the possibility of efficiently performing phase-contrast, dark-field, and directional dark-field imaging on a low-coherence laboratory system equipped with a conventional x ray tube, using a simple, fast, and robust single-mask technique.