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Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications. [ABSTRACT FROM AUTHOR]

Dynamic chest radiography (DCR) is a real-time sequential high-resolution digital X-ray imaging system of the thorax in motion over the respiratory cycle, utilising pulsed image exposure and a larger field of view than fluoroscopy coupled with a low radiation dose, where post-acquisition image processing by computer algorithm automatically characterises the motion of thoracic structures. We conducted a systematic review of the literature and found 29 relevant publications describing its use in humans including the assessment of diaphragm and chest wall motion, measurement of pulmonary ventilation and perfusion, and the assessment of airway narrowing. Work is ongoing in several other areas including assessment of diaphragmatic paralysis. We assess the findings, methodology and limitations of DCR, and we discuss the current and future roles of this promising medical imaging technology. Critical relevance statement Dynamic chest radiography provides a wealth of clinical information, but further research is required to identify its clinical niche. Key points: Dynamic chest radiography (DCR) captures high-resolution moving images of the thorax. The ionising radiation dose of DCR is low. DCR can image the diaphragm, chest wall, ventilation and perfusion. Most papers on DCR are small, with heterogeneity in study design or outcome. Large, multicentre studies with similar outcomes and healthy controls are desirable. [ABSTRACT FROM AUTHOR]

One challenge in using X-ray imaging for post-process inspection of aluminum alloys fusion welded with conventional filler wires (e.g., 4xxx, 5xxx) is the negligible contrast between the fusion zone and base metal, which hinders measurement of important weld characteristics such as weld penetration. The current study outlines the development of novel filler wires that are based on commercial 4xxx (Al-Si) wires, but include additions of Ce to create grayscale contrast in X-ray imaging. Four custom Al-Si-Ce filler wires were fabricated by casting and extrusion, and were used for fusion welding of AA6061 plates. X-ray computed tomography of the lap joints with the Al-Si-Ce filler wires showed enhanced contrast between the fusion zone and the base metal enabling superior quantitative inspection. Microstructure analysis revealed that the increased X-ray contrast was due to Ce-containing precipitate particles within the fusion zone, which were identified to be τ1-Ce(AlxSi1-x)2 and τ4-Al2CeSi2. Hardness and other mechanical tests indicate a slight increase in mechanical properties with increasing Si and Ce content in the filler wire. It is concluded that the addition of relatively small amounts of Ce to conventional Al-Si filler wires is an effective method for creating the needed fusion zone contrast for measurement of weld penetration by X-ray imaging. [ABSTRACT FROM AUTHOR]

Background: EOSedge™* (EOS Imaging, Paris, France) is an X-ray imaging system using automatic exposure control (AEC) with tube current modulation, in order to optimize dose deposition in patients. Purpose: This study aims at characterizing EOSedge organ dose deposition in comparison to a digital radiography (DR) system and the previous EOS system (EOS-1st generation), in relation to their respective image quality levels. Method: Organ doses were measured in an anthropomorphic female adult phantom and a 5-year-old pediatric phantom using optically stimulated luminescence (OSL) dosimeters, which were carefully calibrated within the studied energy range. Organ doses were recorded on the EOSedge and the Fuji Visionary DRF (Fujifilm Medical Systems U.S.A., Inc, Lexington, MA). The resulting effective doses were compared to the EOS-1st-generation values present in the literature. Image quality assessment was carried out on end-user images. Quantitative image quality metrics were computed for all tested modalities on a quality assurance phantom. Qualitative assessment of EOSedge image quality was based on anthropomorphic phantom acquisitions against the EOS-1st-generation system, and on clinical images against the tested DR system. Results: For a full-spine exam, and on the female adult phantom (respectively, the pediatric phantom), an effective dose of 92 Sv (respectively, 32 Sv) was obtained on EOSedge, and 572 Sv (respectively, 179 Sv) on the DR system; these values were compared to effective dose values of 290 Sv (respectively, 200 Sv) from the literature on EOS-1st generation, leading to an effective dose reduction factor of 6 with respect to the DR system, and of 3-6 with respect to EOS-1st generation. EOSedge provides the best compromise between contrast-to-noise ratio (CNR) and dose, with more consistent CNR values than the other tested modalities, in a range of attenuation from 10 to 40 cm of poly(methyl methacrylate) (PMMA). Within this range, EOSedge is also comparable to DR for 10 and 20 cm of PMMA, and better than DR for 30 and 40 cm of PMMA, both in terms of spatial resolution and low-contrast detection. The anatomical landmarks of interest in the follow-up of spinal deformities can be detected in all tested modalities. Conclusion: Results showed that EOSedge provides significant dose reduction factors for full spine imaging in both adults and children compared to the other tested modalities, without compromising image quality. We believe that this work could help raise awareness on the capabilities of modern X-ray systems, when equipped with appropriate AEC strategies, to perform ultra-low-dose, long-axis images. [ABSTRACT FROM AUTHOR]

Wrist cracks are the most common sort of cracks with an excessive occurrence rate. For the routine detection of wrist cracks, conventional radiography (X-ray medical imaging) is used but periodically issues are presented by crack depiction. Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping. Indeed, many hospitals lack experienced clinicians to diagnose wrist cracks. Therefore, an automated system is required to reduce the burden on clinicians and identify cracks. In this study, we have designed a novel residual network-based convolutional neural network (CNN) for the crack detection of the wrist. For the classification of wrist cracks medical imaging, the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning (TL) models such as Inception V3, Vgg16, ResNet-50, and Vgg19, to assist the medical imaging technologist in identifying the cracks that occur due to wrist fractures. The RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor's approaches. The results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time. [ABSTRACT FROM AUTHOR]

This study presents a methodology to develop an X‐ray technique chart for portable chest and abdomen imaging which utilizes patient data available in the modality worklist (MWL) to reliably achieve a predetermined exposure index (EI) at the detector for any patient size. The method assumes a correlation between the patients' tissue equivalent thickness and the square root of the ratio of the patient's weight to height. To assess variability in detector exposures, the EI statistics for 75 chest examinations and 99 abdominal portable X‐ray images acquired with the new technique chart were compared to those from a single portable unit (chest: 3877 images; abdomen: 200 images) using a conventional technique chart with three patient sizes, and to a stationary radiography room utilizing automatic exposure control (AEC) (chest: 360 images; abdomen: 112 images). The results showed that when using the new technique chart on a group of portable units, the variability in EI was significantly reduced (p < 0.01) for both AP chest and AP abdomen images compared to the single portable using a standard technique chart with three patient sizes. The variability in EI for the images acquired with the new chart was comparable to the stationary X‐ray room with an AEC system (p > 0.05). This method could be used to streamline the entire imaging chain by automatically selecting an X‐ray technique based on patient demographic information contained in the MWL to provide higher quality examinations to clinicians by eliminating outliers. In addition, patient height and weight can be used to estimate the patients' tissue equivalent thickness. [ABSTRACT FROM AUTHOR]

Previous studies explored the feasibility of using gamma-ray as an alternative to X-ray in medical imaging. The feasibility studies reported that gamma radiography could be an effective alternative to X-ray radiography. Exposure is the factor that decides noise level and contrast quality in the produced image; the two crucial factors for the diagnosis and patient safety. This research aims to foster the feasibility of gamma-ray mammography by proposing a method for exposure determination. Geant4 Application for Tomographic Emission toolkit was used to simulate the required exposure on the American College of Radiology Mammography phantom using photon energies of 17.5, 19, 20, 22, 23.5, 25, 26, 27.5, and 30 keV. The quality of the produced image was evaluated against a reference experimental X-ray mammographic image. A visual assessment procedure was adopted to determine the minimum contrast-to-noise ratio required to detect the various sets of phantom features. Based on the visual assessment, a method for exposure determination was established and verified. The results will support the application of radioisotopes for mammography. [ABSTRACT FROM AUTHOR]

Lumbar herniated nucleus pulposus (HNP) is difficult to diagnose using lumbar radiography. HNP is typically diagnosed using magnetic resonance imaging (MRI). This study developed and validated an artificial intelligence model that predicts lumbar HNP using lumbar radiography. A total of 180,271 lumbar radiographs were obtained from 34,661 patients in the form of lumbar X-ray and MRI images, which were matched together and labeled accordingly. The data were divided into a training set (31,149 patients and 162,257 images) and a test set (3512 patients and 18,014 images). Training data were used for learning using the EfficientNet-B5 model and four-fold cross-validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) for the prediction of lumbar HNP was 0.73. The AUC of the ROC for predicting lumbar HNP in L (lumbar) 1-2, L2-3, L3-4, L4-5, and L5-S (sacrum)1 levels were 0.68, 0.68, 0.63, 0.67, and 0.72, respectively. Finally, an HNP prediction model was developed, although it requires further improvements. [ABSTRACT FROM AUTHOR]

Three dimensional freeze printing (3DFP) combines the advantages of freeze casting and additive manufacturing to fabricate multifunctional aerogels. Freeze casting is a cost-effective, efficient, and versatile method capable of fabricating micro-scale porous structures inside the aerogels for many different applications. The 3DFP provided the capability of fabricating highly customized geometries with controlled microporous structures as well. However, there are still many unexplained phenomena and features because of the complexity of post-processes and indirect observation methods. This study demonstrates the design and construction of the in situ imaging systems, which use the x-ray synchrotron radiography to observe freeze casting and 3DFP processes. With the advantages provided by the in situ x-ray imaging techniques, the ice crystal growth with its unique lamellar structures can be observed during the freeze casting process. The movement of freeze front, material deposition, and growth of ice crystals can also be visualized during the inkjet-based 3DFP process. [ABSTRACT FROM AUTHOR]

Objectives: In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals. Methods: In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image. Results: Our model achieved accuracy of 90.3%, (95% CI: 86.3–93.7%) specificity of 90% (95% CI: 84.3–94%), and sensitivity of 90.5% (95% CI: 85–94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93–0.97). Conclusion: We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. Key Points: • A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. • A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings. [ABSTRACT FROM AUTHOR]

The dental radiographic comparison is one of the most reliable and scientifically accepted methods for body identification (ID). The heterogeneity between AM (ante mortem) and PM (postmortem) x-rays images continues to stand as an issue for the forensic odontologist. Casual dental findings on X-rays for investigation of other structures than teeth or maxillaries, could eventually be a relevant source of dental data for the ID especially when AM dental files or X-rays are lacking. Two cases are reported in which the body ID was achieved through the comparison of PM dental X-rays with dental images obtained by radiographies of other structures (e.g. X-rays of the skull or cervical spine). These cases highlight that these occasional dental findings might provide sufficient evidence for a body identification. In the collection of AM data of missing people, the collection of all available records and radiographies of the head, neck and chest should be carefully reviewed by forensic odontologists, seeking for any available dental data. [ABSTRACT FROM AUTHOR]

Experiments performed at the Laboratory for Laser Energetics with a continuous-wave (cw) x-ray source and on the OMEGA and OMEGA EP Laser Systems [Boehly et al., Opt. Commun. 133, 495 (1997) and Waxer et al., Opt. Photonics News 16, 30 (2005)] have utilized a Fresnel zone plate (FZP) to obtain x-ray images with a spatial resolution as small as ∼1.5 μm. Such FZP images were obtained with a charge-coupled device or a framing camera at energies ranging from 4.5 keV to 6.7 keV using x-ray line emission from both the cw source and high-intensity, laser-beam–illuminated metal foils. In all cases, the resolution test results are determined from patterns and grids backlit by these sources. The resolutions obtained are shown to be due to a combination of the spectral content of the x-ray sources and detector resolution limited by the magnification of the images (14× to 22×). High-speed framing cameras were used to obtain FZP images with frame times as short as ∼30 ps. Double-shell implosions on OMEGA were backlit by laser-irradiated Fe foils, thus obtaining a framing-camera–limited, FZP-image resolution of ∼3 μm–4 μm. [ABSTRACT FROM AUTHOR]

Material decomposition in X-ray imaging uses the energy-dependence of attenuation to digitally decompose an object into specific constituent materials, generally at the cost of enhanced image noise. Propagation-based X-ray phase-contrast imaging is a developing technique that can be used to reduce image noise, in particular from weakly attenuating objects. In this paper, we combine spectral phase-contrast imaging with material decomposition to both better visualize weakly attenuating features and separate them from overlying objects in radiography. We derive an algorithm that performs both tasks simultaneously and verify it against numerical simulations and experimental measurements of ideal two-component samples composed of pure aluminum and poly(methyl methacrylate). Additionally, we showcase first imaging results of a rabbit kitten’s lung. The attenuation signal of a thorax, in particular, is dominated by the strongly attenuating bones of the ribcage. Combined with the weak soft tissue signal, this makes it difficult to visualize the fine anatomical structures across the whole lung. In all cases, clean material decomposition was achieved, without residual phase-contrast effects, from which we generate an un-obstructed image of the lung, free of bones. Spectral propagation-based phase-contrast imaging has the potential to be a valuable tool, not only in future lung research, but also in other systems for which phase-contrast imaging in combination with material decomposition proves to be advantageous. [ABSTRACT FROM AUTHOR]

Recent material advancements in organic plastic scintillators enable marked increases in material detection efficiency, light yield, and pulse shape discrimination properties. These advances may resolve significant capability gaps for low-cost, portable, and durable dual-particle imaging (DPI) systems for nuclear safety, security, and safeguard purposes. One such material, a 21% bismuth-loaded polyvinyl toluene (BiPVT), is computationally evaluated as a small, pixelated radiographic array using Monte Carlo N-Particle (MCNP) and Zemax OpticStudio, and it is compared to identical evaluations of EJ-200 and EJ-256 arrays. MCNP software enables estimates of particle interaction and energy deposition, while OpticStudio computes optical light transport within each material. Computational estimates of spatial resolution and relative light collection at 370 kVp are found to agree with experimental results for both EJ-200 and EJ-256 arrays, thereby validating predictions of the same for the BiPVT array. As such, for equivalent exposures at 370 kVp, a BiPVT array may provide ~ 20× the light collection expected from EJ-200 and ~10× that expected from EJ-256. Similar comparisons of estimated light collection are also computed at 150 and 270 kVp, and these results suggest that BiPVT will provide significantly improved performance over EJ-200 and EJ-256 across all energies practical for portable X-ray radiography. [ABSTRACT FROM AUTHOR]

The technique of spectral radiography has been shown to accurately discriminate a material’s elemental composition by using known X-ray physics. In previous work, we presented a system for determining the total compositional mass of UO2 powder. This is achieved with a state-of-the-art pixelated spectrometer and a high-fidelity physical model. Similar to other radiographs, the system suffers from partial-volume attenuation where the sample projection onto a pixel is only a fraction of the pixel area, leaving the rest unattenuated. This study updates the physical model to account for fractional area which challenges the existing algorithm. We present how this update is made and explore approaches to stabilize the algorithm through parameter scaling, constrained optimization, and hyperparameter selection. We applied the updated algorithm to calculate the total uranium and oxygen mass of UO2 powder simulations. The average relative error on total uranium mass was reduced from 1.4% to 0.068% and on total oxygen mass from 122.2% to 4.2%. [ABSTRACT FROM AUTHOR]

Blur in X-ray radiographs not only reduces the sharpness of image edges but also reduces the overall contrast. The effective blur in a radiograph is the combined effect of blur from multiple sources such as the detector panel, X-ray source spot, and system motion. In this paper, we use a systems approach to model the point spread function (PSF) of the effective radiographic blur as the convolution of multiple PSFs, where each PSF models one of the various sources of blur. In particular, we model the combined contribution of X-ray source and detector blurs while assuming negligible contribution from other forms of blur. Then, we present a numerical optimization algorithm for estimating the source and detector PSFs from multiple radiographs acquired at different X-ray source to object (SOD) and object to detector distances (ODD). Finally, we computationally reduce blur in radiographs using deblurring algorithms that use the estimated PSFs from the previous step. Our approach to estimate and reduce blur is called SABER, which is an acronym for systems approach to blur estimation and reduction. [ABSTRACT FROM AUTHOR]

The wetting behavior and affinity to side reactions of carbon‐based electrodes in vanadium redox flow batteries (VRFBs) are highly dependent on the physical and chemical surface structures of the material, as well as on the cell design itself. To investigate these properties, a new cell design was proposed to facilitate synchrotron X‐ray imaging. Three different flow geometries were studied to understand the impact on the flow dynamics, and the formation of hydrogen bubbles. By electrolyte injection experiments, it was shown that the maximum saturation of carbon felt was achieved by a flat flow field after the first injection and by a serpentine flow field after continuous flow. Furthermore, the average saturation of the carbon felt was correlated to the cyclic voltammetry current response, and the hydrogen gas evolution was visualized in 3D by X‐ray tomography. The capabilities of this cell design and experiments were outlined, which are essential for the evaluation and optimization of cell components of VRFBs. [ABSTRACT FROM AUTHOR]

We investigate the combination of two emerging X-ray imaging technologies, namely spectral imaging and differential phase contrast imaging. By acquiring spatially and temporally registered images with several different X-ray spectra, spectral imaging can exploit differences in the energy-dependent attenuation to generate material selective images. Differential phase contrast imaging uses an entirely different contrast generation mechanism: The phase shift that an X-ray wave exhibits when traversing an object. As both methods can determine the (projected) electron density, we propose a novel material decomposition algorithm that uses the spectral and the phase contrast information simultaneously. Numerical experiments show that the combination of these two imaging techniques benefits from the strengths of the individual methods while the weaknesses are mitigated: Quantitatively accurate basis material images are obtained and the noise level is strongly reduced, compared to conventional spectral X-ray imaging. [ABSTRACT FROM AUTHOR]

Results of experimental studies of relativistic plasma bunches obtained under gyromagnetic autoresonance (GA) and confined in a magnetic mirror trap are provided. The main attention is paid to observations of the radiation generated by plasma bunches at different stages of their lives in different spectral ranges using streak photography and X-ray imaging techniques. The original method of accounting for the instrument function in X-ray spectroscopic analysis and radiometric measurements is applied within the quantum energy range of 0.2–2 MeV to take account of considerable distortion caused by the Compton scattering in the detector. [ABSTRACT FROM AUTHOR]

We use X-ray imaging to study viscous resuspension. In a Taylor-Couette geometry, we shear an initially settled layer of spherical glass particles immersed in a Newtonian fluid and measure the local volume fraction profiles. In this configuration, the steady-state profiles are simply related to the normal viscosity defined in the framework of the suspension balance model. These experiments allow us to examine this fundamental quantity over a wide range of volume fractions, in particular, in the semidilute regime where experimental data are sorely lacking. Our measurements strongly suggest that the particle stress is quadratic with respect to the volume fraction in the dilute limit. Strikingly, they also reveal a nonlinear dependence on the Shields number, in contrast with previous theoretical and experimental results. This likely points to shear-thinning particle stresses and to a non-Coulomb or velocity-weakening friction between the particles, as also evidenced from shear reversal experiments. [ABSTRACT FROM AUTHOR]

Cargo inspection is essential for material discrimination and security aims such as threat detection/identification. In this paper, dual-energy X-ray imaging for cargo inspection is studied. Dual-energy imaging challenges such as improving image quality (i.e. noise reduction), automatically removing image defects and errors, and developing algorithms for image detection and identification are investigated. Selecting the right method for image formation, selecting the right source, choosing appropriate X-ray energies, and the appropriate detectors are also discussed. Cargo inspection using dual-energy X-ray shows that not many techniques have been presented for denoising, and there is no comparative comparison between them. Material discrimination using dual-energy X-ray has had significant success in cargo, especially when convolutional neural network (CNN) is integrated with traditional techniques. However, it will have limitations on performance due to the existence of challenges such as the need for large data and the possibility of noise presence in the extracted features. It seems that future studies should cover these issues. • Dual-energy X-ray radiography has been developed for industrial cargo inspection. • Material discrimination only based on the transparency is not so appropriate. • CNN with traditional techniques gives acceptable success in cargo inspection. • Noise reduction techniques for cargo inspection have a higher research priority. [ABSTRACT FROM AUTHOR]

In common radiography, image contrast is often limited due mainly to scattered x-rays and noise, decreasing the quantitative usefulness of x-ray images. Several scatter reduction methods based on software correction schemes have been extensively investigated in an attempt to overcome these difficulties, most of which are based on measurement, mathematical-physical modeling, or a combination of both. However, those methods require special equipment, system geometry, and extra manual work to measure scatter characteristics. In this study, we investigated a new software scheme for scatter correction based on a simple radiographic scattering model where the intensity of the scattered x-rays was directly estimated from a single x-ray image using a weighted l1-norm contextual regularization framework. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. We also conducted some clinical image studies using patient's image data of breast and L-spine to verify the clinical effectiveness of the proposed scheme. Our results indicate that the degradation of image characteristics by scattered x-rays and noise was effectively recovered by using the proposed software scheme, thus improving radiographic visibility considerably. Graphical abstract The schematic illustrations of scatter suppression methods by using a an antiscatter grid and b a scatter estimation algorithm. [ABSTRACT FROM AUTHOR]

[Display omitted] • Dual-energy X-ray radiography is utilized for material discrimination. • Modular radial basis networks are applied for training of X-ray images. • The approach is based on identification of quasi-radial distributions of spectrums. • The results show acceptable discrimination of material-thicknesses. Challenges, especially, identification of correlation of attenuation coefficients at high/low energy X-ray, stochastic noise on images, source energy fluctuation, and angular drop-off of X-ray energy/dose rate affect the quality of the material discrimination using dual-energy X-ray radiography. In this study, a new approach using radial basis network (RBN) is applied for the identification of materials. A modular structure, in which each module is responsible for the identification of a single material-thickness is developed. Each module with its specific RBN not only is trained to identify its own material-thickness but also is trained to reject the other ones. The activation/radial function of each network is with standard deviation proportional to the distribution of a specific material-thickness transparency. This technique tries to handle the effects of the mentioned challenges which have quasi-radial distribution on the constructed image. Moreover, the cross-correlation between attenuation coefficients at high/low energy X-ray is detected by the neural network. The results show an acceptable performance of the proposed approach. The noticeable advantages are: 1- Instead of utilizing qualitative discrimination techniques such as pseudo-colouring, the materials are discriminated quantitatively which is more important when the number of material- thicknesses is increased. 2- The modular structure makes it possible to add new material- thickness without unfavourably affecting the existing networks (i.e., there is no need to retrain them). 3- The exact percentage of correct identification, incorrect identification, and misidentification are given 4- Bipolar representation makes possible to identify material-thickness without need/with less need to human operators 5- Material discrimination may be done by a more straightforward computational tool because classical machine learning is applied instead of deep learning. [ABSTRACT FROM AUTHOR]

• X-ray radiographs give projected mass information that may be degraded due to blur. • Proposed framework allows for correcting mass information in degraded radiographs. • Penalized regression model trained with artificially-blurred synthetic objects. • Experimental feasibility showcased on real radiographs of solid objects. • Blind construction allows for correcting mass without knowing the system PSF. The quantitative impact of image blur on calculated object mass in X-ray radiography is explored. Radiographed object masses are initially estimated through experimental calibration and compared against their known true mass, with the deviation, or "apparent mass loss". Apparent mass loss occurs due to the effect of spatial blur on X-ray transmission images that reduces the measured mass because while the image intensity is conserved with spatial blur, the nonlinear conversion to path length is not. A synthetic model is proposed where an object's apparent mass loss is estimated through the amount of blur present within the radiograph image. The image blur is inferred through a combination of readily available image parameters related to the signal levels, object shape, and experimental extrinsics. A regularized regression model is built that correlates these blur parameters to the expected mass loss. The model is first trained on 2100 and tested on 140 synthetically created objects, and then later verified on two separate experimental setups of type 304 stainless steel objects imaged with a portable tube source at 150 kVp and type 6061 aluminum objects at 80 kVp. The proposed model allows for a simple post hoc reduction in errors of approximately 20% in object mass through X-ray radiography. [ABSTRACT FROM AUTHOR]

Aim: To investigate image interpretation performance by diagnostic radiography students, diagnostic radiographers and reporting radiographers by computing eye gaze metrics using eye tracking technology.Methods: Three groups of participants were studied during their interpretation of 8 digital radiographic images including the axial and appendicular skeleton, and chest (prevalence of normal images was 12.5%). A total of 464 image interpretations were collected. Participants consisted of 21 radiography students, 19 qualified radiographers and 18 qualified reporting radiographers who were further qualified to report on the musculoskeletal (MSK) system.Outcome Measures: Eye tracking data was collected using the Tobii X60 eye tracker and subsequently eye gaze metrics were computed. Voice recordings, confidence levels and diagnoses provided a clear demonstration of the image interpretation and the cognitive processes undertaken by each participant. A questionnaire afforded the participants an opportunity to offer information on their experience in image interpretation and their opinion on the eye tracking technology.Results: Reporting radiographers demonstrated a 15% greater accuracy rate (p≤0.001), were more confident (p≤0.001) and took a mean of 2.4s longer to clinically decide on all features compared to students. Reporting radiographers also had a 15% greater accuracy rate (p≤0.001), were more confident (p≤0.001) and took longer to clinically decide on an image diagnosis (p=0.02) than radiographers. Reporting radiographers had a greater mean fixation duration (p=0.01), mean fixation count (p=0.04) and mean visit count (p=0.04) within the areas of pathology compared to students. Eye tracking patterns, presented within heat maps, were a good reflection of group expertise and search strategies. Eye gaze metrics such as time to first fixate, fixation count, fixation duration and visit count within the areas of pathology were indicative of the radiographer's competency.Conclusion: The accuracy and confidence of each group could be reflected in the variability of their eye tracking heat maps. Participants' thoughts and decisions were quantified using the eye tracking data. Eye tracking metrics also reflected the different search strategies that each group of participants adopted during their image interpretations. This is the first study to use eye tracking technology to assess image interpretation skills between various groups of different levels of experience in radiography, especially on a combination of the MSK system, chest cavity and a variety of pathologies. [ABSTRACT FROM AUTHOR]

Background: The Australian Synchrotron Imaging and Medical Beamline (IMBL) was designed as the world's widest synchrotron X-ray beam, enabling both clinical imaging and therapeutic applications for humans as well as the imaging of large animal models. Our group is developing methods for imaging the airways of newly developed CF animal models that display human-like lung disease, such as the CF pig, and we expect that the IMBL can be utilised to image airways in animals of this size.Methods: This study utilised samples of excised tracheal tissue to assess the feasibility, logistics and protocols required for airway imaging in large animal models such as pigs and sheep at the IMBL. We designed an image processing algorithm to automatically track and quantify the tracheal mucociliary transport (MCT) behaviour of 103 μm diameter high refractive index (HRI) glass bead marker particles deposited onto the surface of freshly-excised normal sheep and pig tracheae, and assessed the effects of airway rehydrating aerosols.Results: We successfully accessed and used scavenged tracheal tissue, identified the minimum bead size that is visible using our chosen imaging setup, verified that MCT could be visualised, and that our automated tracking algorithm could quantify particle motion. The imaging sequences show particles propelled by cilia, against gravity, up the airway surface, within a well-defined range of clearance speeds and with examples of 'clumping' behaviour that is consistent with the in vivo capture and mucus-driven transport of particles.Conclusion: This study demonstrated that the wide beam at the IMBL is suitable for imaging MCT in ex vivo tissue samples. We are now transitioning to in vivo imaging of MCT in live pigs, utilising higher X-ray energies and shorter exposures to minimise motion blur. [ABSTRACT FROM AUTHOR]

The recent development of portable, battery-charged x-ray tubes and digital detectors has made digital radioscopic examination of wooden buildings a realistic option. Lack of experience with the often very complex radiographs of building structures makes the interpretation of such radiographs difficult. A set of radiographs of wood in different, identified conditions was established for use when interpreting radiographs of wood in unknown condition. This article presents radiographs of a trestle-framed building. The recordings were done in areas where the condition of the wooden elements was known in advance, either on the basis of dendrochronological drill samples or because the building element had been replaced recently and the wood was sound. The radiographs were compared with NIKU´s collected guideline radiographs and discussed with respect to the information they provide about the wood’s condition. Finally, we discuss radioscopy as a supplement to traditional methods of building surveying. The authors took all of the photos and radiographs (©NIKU). The radiographs are shown as “shadow” images—materials of higher density are seen lighter than materials of less density, as long as they are of same thickness. [ABSTRACT FROM AUTHOR]

Purpose: X-ray dark-field imaging promises information on the small angle scattering properties even of large samples. However, the dark-field image is correlated with the object's attenuation and phase-shift if a polychromatic x-ray spectrum is used. A method to remove part of these correlations is proposed. Methods: The experimental setup for image acquisition was modeled in a wave-field simulation to quantify the dark-field signals originating solely from a material's attenuation and phase-shift. A calibration matrix was simulated for ICRU46 breast tissue. Using the simulated data, a dark-field image of a human mastectomy sample was corrected for the finger print of attenuation- and phase-image. Results: Comparing the simulated, attenuation-based dark-field values to a phantom measurement, a good agreement was found. Applying the proposed method to mammographic dark-field data, a reduction of the dark-field background and anatomical noise was achieved. The contrast between microcalcifications and their surrounding background was increased. Conclusions: The authors show that the influence of and dispersion can be quantified by simulation and, thus, measured image data can be corrected. The simulation allows to determine the corresponding dark-field artifacts for a wide range of setup parameters, like tube-voltage and filtration. The application of the proposed method to mammographic dark-field data shows an increase in contrast compared to the original image, which might simplify a further image-based diagnosis. [ABSTRACT FROM AUTHOR]

The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x-ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self-teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs. [ABSTRACT FROM AUTHOR]

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. • This paper explores deep learning (DL) based studies on chest x-ray classification. • This systematic review discusses the DL techniques used in the related literature. • We summarise chest x-ray public datasets of pneumonia and COVID-19 conditions. • We suggest criteria to select DL techniques based on different considerations. • This paper presents the open challenges and possible future research directions. [ABSTRACT FROM AUTHOR]

Complex hollow structures have been observed in the cores of single exploded wires and multiwire arrays using high-resolution X-ray radiography and laser probing. The radiographs show features supporting earlier research that suggested the core is a foamlike liquid–vapor mixture. [ABSTRACT FROM PUBLISHER]

Purpose: To fabricate organic photovoltaic (OPV) cells with nanometric active layers sensitive to ionizing radiation and measure their dosimetric characteristics in clinical x-ray beams in the diagnostic tube potential range of 60-150 kVp. Methods: Experiments were designed to optimize the detector's x-ray response and find the best parameter combination by changing the active layer thickness and the area of the electrode. The OPV cell consisted of poly (3-hexylthiophene-2,5-diyl): [6,6]-phenyl C61 butyric acid methyl ester photoactive donor and acceptor semiconducting organic materials sandwiched between an aluminum electrode as an anode and an indium tin oxide electrode as a cathode. The authors measured the radiation-induced electric current at zero bias voltage in all fabricated OPV cells. Results: The net OPV current as a function of beam potential (kVp) was proportional to kVp-0.5 when normalized to x-ray tube output, which varies with kVp. Of the tested configurations, the best combination of parameters was 270 nm active layer thicknesses with 0.7 cm² electrode area, which provided the highest signal per electrode area. For this cell, the measured current ranged from approximately 0.7 to 2.4 nA/cm² for 60-150 kVp, corresponding to about 0.09 nA-0.06 nA/mGy air kerma, respectively. When compared to commercial amorphous silicon thin film photovoltaic cells irradiated under the same conditions, this represents 2.5 times greater sensitivity. An additional 40% signal enhancement was observed when a 1 mm layer of plastic scintillator was attached to the cells' beam-facing side. Conclusions: Since both OPVs can be produced as flexible devices and they do not require external bias voltage, they open the possibility for use as thin film in vivo detectors for dose monitoring in diagnostic x-ray imaging. [ABSTRACT FROM AUTHOR]

Purpose: The authors note that the concept of the Talbot self-image distance in x-ray phase grating interferometry is indeed not well defined for polychromatic x-rays, because both the grating phase shift and the fractional Talbot distances are all x-ray wavelength-dependent. For x-ray interferometry optimization, there is a need for a quantitative theory that is able to predict if a good intensity modulation is attainable at a given grating-to-detector distance. In this work, the authors set out to meet this need. Methods: In order to apply Fourier analysis directly to the intensity fringe patterns of twodimensional and one-dimensional phase grating interferometers, the authors start their derivation from a general phase space theory of x-ray phase-contrast imaging. Unlike previous Fourier analyses, the authors evolved the Wigner distribution to obtain closed-form expressions of the Fourier coefficients of the intensity fringes for any grating-to-detector distance, even if it is not a fractional Talbot distance. Results: The developed theory determines the visibility of any diffraction order as a function of the grating-to-detector distance, the phase shift of the grating, and the x-ray spectrum. The authors demonstrate that the visibilities of diffraction orders can serve as the indicators of the underlying interference intensity modulation. Applying the theory to the conventional and inverse geometry configurations of single-grating interferometers, the authors demonstrated that the proposed theory provides a quantitative tool for the grating interferometer optimization with or without the Talbotdistance constraints. Conclusions: In this work, the authors developed a novel theory of the interference intensity fringes in phase grating x-ray interferometry. This theory provides a quantitative tool in design optimization of phase grating x-ray interferometers. [ABSTRACT FROM AUTHOR]

Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimages using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time. [ABSTRACT FROM AUTHOR]

Purpose: The authors describe a design for prepatient region of interest attenuators (ROIAs) to reduce dose area product (DAP) for clinical use. The authors describe a model to predict DAP values from x-ray technique parameters recorded during a clinical procedure for image sequences obtained in the presence or absence of ROIAs. The model was developed primarily to determine what the DAP to a patient undergoing cardiac catheterization with a ROIA would have been if no ROIA had been used allowing a determination of DAP reduction. Methods: Copper ROIAs with thicknesses that vary gradually so as not to cause significant image artifacts were constructed. X-ray image sequences were acquired on a clinical catheterization system with and without ROIAs with varying x-ray technique parameters. DAP values were measured for all said exposures using an ionization chamber and compared to a model the authors developed. Results: The model can predict DAP values within 3.5% on average with or without ROIAs when compared to ionization chamber measurements. Conclusions: The proposed experimental design is adequate for measuring DAP reductions on the order of 1.5-3.5 that are expected when introducing a ROIA during patient catheterization imaging. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) is a radiographic scanning technique for visualising cross-sectional images of an object non-destructively. From these cross-sectional images it is possible to evaluate internal dimensional features of a workpiece which may otherwise be inaccessible to tactile and optical instruments. Beam hardening is a physical process that degrades the quality of CT images and has previously been suggested to influence dimensional measurements. Using a validated simulation tool, the influence of spectrum pre-filtration and beam hardening correction are evaluated for internal and external dimensional measurements. Beam hardening is shown to influence internal and external dimensions in opposition, and to have a greater influence on outer dimensions compared to inner dimensions. The results suggest the combination of spectrum pre-filtration and a local gradient-based surface determination method are able to greatly reduce the influence of beam hardening in X-ray CT for dimensional metrology. [ABSTRACT FROM AUTHOR]