Digital X-ray Imaging Research Articles

Prototype of Flexible Digital X-Ray Imaging Detector

Prototype of Flexible Digital X-Ray Imaging Detector

A novel frameshift deletion variant of ARSL associated with X-linked recessive chondrodysplasia punctata 1: a case report and literature review of prenatal, confirmed cases

BackgroundX-linked recessive chondrodysplasia punctata 1 (CDPX1) is a rare congenital skeletal dysplasia characterized by stippled epiphyses, nasal hypoplasia, and brachytelephalangy. ARSL (formerly known as ARSE), a member of the sulfatase gene family located on Xp22.3, has been identified as the causative gene for CDPX1. The high clinical and genetic heterogeneity of CDPX1 presents a challenge to prenatal diagnosis.Case presentationA G1P0 woman in her 30s with an unremarkable prenatal course presented in the second trimester. Maternal diseases, tobacco, alcohol, and drug history during pregnancy were denied. Obstetrical ultrasound examination revealed a flattened nose and a flattened midface with echogenic alterations of lumbar spinous process in the fetus. Amniocentesis was performed for genetic testing. A normal karyotype and a negative result of CNV-seq were obtained. However, Whole exome sequencing (WES) in trios revealed a hemizygous ARSL variant [NM_000047.3:c.1108del p.(Trp370Glyfs*35)] in the fetus, which was maternally inherited as confirmed by Sanger sequencing. This variant was absent from the genomAD and HGMD databases. According to the ACMG guidelines, this variant was interpreted as likely pathogenic (PVS1 + PM2_Supporting). The couple decided to terminate the pregnancy. After induction of labour, a severe nasal hypoplasia was noted; and brachytelephalangy was not remarkable. Postmortem digital X-ray imaging revealed symmetrical stippled epiphyses of the vertebrae in all spine regions and enlargement of spinous process of L1-L4 vertebrae.ConclusionA novel frameshift deletion variant of ARSL and the associated fetal phenotype have been identified. This study provides useful information for prenatal diagnosis and genetic counseling of CDPX1.

Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study.

Artificial intelligence (AI) based computer-aided detection devices are recommended for screening and triaging of pulmonary tuberculosis (TB) using digital chest x-ray (CXR) images (soft copies). Most AI algorithms are trained using input data from digital CXR Digital Imaging and Communications in Medicine (DICOM) files. There can be scenarios when only digital CXR films (hard copies) are available for interpretation. A smartphone-captured photo of the digital CXR film may be used for AI to process in such a scenario. There is a gap in the literature investigating if there is a significant difference in the performance of AI algorithms when digital CXR DICOM files are used as input for AI to process as opposed to photos of the digital CXR films being used as input. The primary objective was to compare the agreement of AI in detecting radiological signs of TB when using DICOM files (denoted as CXRd) as input versus when using smartphone-captured photos of digital CXR films (denoted as CXRp) with human readers. Pairs of CXRd and CXRp images were obtained retrospectively from patients screened for TB. AI results were obtained using both the CXRd and CXRp files. The majority consensus on the presence or absence of TB in CXR pairs was obtained from a panel of 3 independent radiologists. The positive and negative percent agreement of AI in detecting radiological signs of TB in CXRd and CXRp were estimated by comparing with the majority consensus. The distribution of AI probability scores was also compared. A total of 1278 CXR pairs were analyzed. The positive percent agreement of AI was found to be 92.22% (95% CI 89.94-94.12) and 90.75% (95% CI 88.32-92.82), respectively, for CXRd and CXRp images (P=.09). The negative percent agreement of AI was 82.08% (95% CI 78.76-85.07) and 79.23% (95% CI 75.75-82.42), respectively, for CXRd and CXRp images (P=.06). The median of the AI probability score was 0.72 (IQR 0.11-0.97) in CXRd and 0.72 (IQR 0.14-0.96) in CXRp images (P=.75). We did not observe any statistically significant differences in the output of AI in digital CXRs and photos of digital CXR films.

Integration of Vertical Graphene Onto a Tunnelling Cathode for Digital X-Ray Imaging.

As an alternative to thermionic X-ray generators, cold-cathode X-ray tubes are being developed for portable and multichannel tomography. Field emission propagating from needle structures such as carbon nanotubes or Si tips currently dominates related research and development, but various obstacles prevent the widespread of this technology. An old but simple electron emission design is the planar tunnelling cathode using a metal-oxide-semiconductor (MOS) structure, which achieves narrow beam dispersion and low supplying voltage. Directly grown vertical graphene (VG) is employed as the gate electrode of MOS and tests its potential as a new emission source. The emission efficiency of the device is initially ≈1% because of unavoidable fabrication damage during the patterning processes; it drastically improves to >40% after ozone treatment. The resulting emission current obeys the Fowler-Nordheim tunnelling model, and the enhanced emission is attributed to the effective gate thickness reduction by ozone treatment. As a proof-of-concept experiment, a clustered array of 2140 cells is integrated into a system that provides mA-class emission current for X-ray generation. With pulsed digital excitations, X-ray imaging of a chest phantom, demonstrating the feasibility of using a VG MOS electron emission source as a new and innovative X-ray generator is realized.

Chest X-Ray-Based Telemedicine Platform for Pediatric Tuberculosis Diagnosis in Low-Resource Settings: Development and Validation Study.

Tuberculosis (TB) remains a major cause of morbidity and death worldwide, with a significant impact on children, especially those under the age of 5 years. The complex diagnosis of pediatric TB, compounded by limited access to more accurate diagnostic tests, underscores the need for improved tools to enhance diagnosis and care in resource-limited settings. This study aims to present a telemedicine web platform, BITScreen PTB (Biomedical Image Technologies Screen for Pediatric Tuberculosis), aimed at improving the evaluation of pulmonary TB in children based on digital chest x-ray (CXR) imaging and clinical information in resource-limited settings. The platform was evaluated by 3 independent expert readers through a retrospective assessment of a data set with 218 imaging examinations of children under 3 years of age, selected from a previous study performed in Mozambique. The key aspects assessed were the usability through a standardized questionnaire, the time needed to complete the assessment through the platform, the performance of the readers to identify TB cases based on the CXR, the association between the TB features identified in the CXRs and the initial diagnostic classification, and the interreader agreement of the global assessment and the radiological findings. The platform's usability and user satisfaction were evaluated using a questionnaire, which received an average rating of 4.4 (SD 0.59) out of 5. The average examination completion time ranged from 35 to 110 seconds. In addition, the study on CXR showed low sensitivity (16.3%-28.2%) but high specificity (91.1%-98.2%) in the assessment of the consensus case definition of pediatric TB using the platform. The CXR finding having a stronger association with the initial diagnostic classification was air space opacification (χ21>20.38, P<.001). The study found varying levels of interreader agreement, with moderate/substantial agreement for air space opacification (κ=0.54-0.67) and pleural effusion (κ=0.43-0.72). Our findings support the promising role of telemedicine platforms such as BITScreen PTB in enhancing pediatric TB diagnosis access, particularly in resource-limited settings. Additionally, these platforms could facilitate the multireader and systematic assessment of CXR in pediatric TB clinical studies.

Lossless compression-based detection of osteoporosis using bone X-ray imaging.

Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging. This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images. A dataset of bone X-ray images was analyzed using a newly proposed procedure. This procedure involves segregating the images into regions of interest (ROI) and non-ROI, thereby reducing data redundancy. The images were then processed to enhance both spatial and statistical features. For classification, a Support Vector Machine (SVM) classifier was employed to distinguish between osteoporotic and non-osteoporotic cases. The proposed method demonstrated a promising Area under the Curve (AUC) of 90.8% in diagnosing osteoporosis, benchmarking favorably against existing techniques. This signifies a high level of accuracy in distinguishing osteoporosis patients from healthy controls. The proposed method effectively distinguishes between osteoporotic and non-osteoporotic cases using bone X-ray images. By enhancing image features and employing SVM classification, the technique offers a promising tool for efficient and accurate osteoporosis diagnosis.

A new method for deep learning detection of defects in X-ray images of pressure vessel welds.

Given that defect detection in weld X-ray images is a critical aspect of pressure vessel manufacturing and inspection, accurate differentiation of the type, distribution, number, and area of defects in the images serves as the foundation for judging weld quality, and the segmentation method of defects in digital X-ray images is the core technology for differentiating defects. Based on the publicly available weld seam dataset GDX-ray, this paper proposes a complete technique for fault segmentation in X-ray pictures of pressure vessel welds. The key works are as follows: (1) To address the problem of a lack of defect samples and imbalanced distribution inside GDX-ray, a DA-DCGAN based on a two-channel attention mechanism is devised to increase sample data. (2) A convolutional block attention mechanism is incorporated into the coding layer to boost the accuracy of small-scale defect identification. The proposed MAU-Net defect semantic segmentation network uses multi-scale even convolution to enhance large-scale features. The proposed method can mask electrostatic interference and non-defect-class parts in the actual weld X-ray images, achieve an average segmentation accuracy of 84.75% for the GDX-ray dataset, segment and accurately rate the valid defects with a correct rating rate of 95%, and thus realize practical value in engineering.

Application of Control Algorithm in the Design of Automatic Crimping Device for Connecting Pipe and Ground Wire

Due to the low effectiveness and poor quality of manual crimping of grounding wires, the author proposes the design of an automatic crimping device for connecting tube grounding wires based on intelligent fully automatic technology. The device consists of a microcontroller, an upper computer control interface, an electric push rod, an infrared sensor, a pressure transmitter, and other devices. The staff used the upper computer monitoring interface to set the relevant parameters for grounding wire crimping, and used X-ray digital imaging technology to measure the crimping size of the grounding wire. The size met the set parameter conditions. Through the PID control algorithm in the microcontroller, the stepper motor was controlled to push the clamp to move, completing the automatic crimping of the grounding wire. The X-ray detection method was introduced to detect the quality of the grounding wire after the crimping was completed. The experimental results show that the average deviation between the measured crimping size of the grounding wire and the actual measurement size by the automatic crimping device is only 0.06 mm, indicating that its measurement results are accurate; The success rate of crimping exceeds 95%. The above experimental results verify that the designed crimping device has high stability and reliability, and good quality detection effect.

Advances in Medical Imaging Technologies and Their Impact on Clinical Practices

Medical imaging technologies have revolutionized the field of medicine, providing unprecedented insights into the human body that are essential for accurate diagnosis and effective treatment planning. This comprehensive review explores the latest advancements in medical imaging technologies, including digital X-ray imaging, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound imaging, and their impact on clinical practices. Key technological innovations such as artificial intelligence, machine learning, quantum computing, and augmented and virtual reality are discussed, alongside the importance of interdisciplinary collaborations and the educational and training needs for advanced imaging technologies. The review also addresses the challenges and limitations faced by these technologies, including technical challenges, legal and ethical issues, and cost and accessibility concerns. Finally, future directions are proposed to enhance the integration and application of advanced medical imaging technologies in clinical settings.

Apparent density of eucalyptus wood evaluated by digital X-ray images after storage in the field during 365 days

Apparent density of eucalyptus wood evaluated by digital X-ray images after storage in the field during 365 days

Reliability of a forensic odontology method for age-at-death estimation in adults: A Mexican case study

This study aimed to evaluate the reliability of an age estimation method based on the pulp⁄tooth area ratio by assessing intra- and inter-examiner agreement across five observers at different intervals. Using the same X-ray device and technical parameters, 96 digital periapical X-ray images of upper and lower canines were obtained from 28 deceased people in Central America, whose age at death ranged from 19 to 49 years. Excellent and good agreement of results were achieved, and there were no statistically significant differences. The R2 value for upper teeth (54.0%) was higher than the R2 value for lower teeth (45.7%). The highest intraclass correlation coefficient value was 0.995 (0.993–0.997) and the lowest 0.798 (0.545–0.895). Inter-examiner agreement was high with values of 0.975 (0.965–0.983) and 0.927 (0.879–0.955). This method is adequate for assessing age in missing and unidentified people, including victims of mass disasters.

Prevalence and distribution of sesamoid bones and accessory ossicles in the hand region in patients presenting to the emergency department: a radiographic study

Purpose: This study aimed to retrospectively examine the accessory ossicles and sesamoid bones of the patients who applied to the emergency department with any hand and wrist complaints, on radiological images according to gender, frequency of incidence and extremity side.&#x0D; Materials and Methods: This retrospective study was performed on radiological images (digital x-ray) of 500 patients (297 men, 203 women). Presence, prevalence, coexistence and distribution of accessory ossicles and sesamoids in the hand of patients without hand skeletal deformities were retrospectively analyzed.&#x0D; Results: Accessory ossicles were detected in 8 (1.6%) of 500 cases. In our study, all of the accessory ossicles seen in the wrist and the hand region were ulnar styloid ossicles. Sesamoid bone was detected in 500 cases (100%) in metacarpophalangeal I (MCP I), in 192 cases (38.4%) in metacarpophalangeal II (MCP II), in 17 cases (3.4%) in metacarpophalangeal III (MCP III), in 2 cases (0.4%) in metacarpophalangeal IV (MCP IV), and in 34 cases (6.8%) in metacarpophalangeal V (MCP V).&#x0D; Conclusion: This study will contribute to the literature in terms of Turkish population incidence by using digital x-ray images for hand sesamoid bones and accessory ossicles.

Study of Wheat Grain Structure by Digital Radiography

Introduction. Analysis of the quality of wheat grain using instrumental methods is an important task due to the permanent growth in annual wheat production and export. Digital radiography is a promising method for studying the internal structure of wheat grain. Along with other methods, digital radiography allows non-destructive testing and comprehensive description of grain parameters.Aim. To determine the capabilities of digital radiography for studying the structure of wheat grain.Materials and methods. Digital X-ray images were obtained using a multifunctional mobile x-ray diagnostic unit produced by JSC "ELTECH-Med" was used with the installation tube voltage of 9 kV. Durum wheat grain samples of different varieties were used. Operations for processing grain images were automated using the scripts developed in Python using the numpy and opencv libraries.Results. The attenuation of X-ray radiation by grain material was studied. Wheat bran was found to exhibit a greater attenuation coefficient than the endosperm, assumably due to the higher concentration of potassium and phosphorus in the wheat outer layer. The calculated ratio of the attenuation coefficients of the bran to the endosperm was 1.27, compared to the experimentally obtained value of 1.36. The influence of the geometric shape of wheat grain on the formation of its X-ray image was determined. A method for describing the geometric shape of wheat grain using second-order curves was presented. A mathematical model for the transmission of X-ray radiation in wheat grain was constructed, taking into account its complex shape and the heterogeneous distribution of macroelements therein. This model allows estimation of the attenuation coefficients of the grain endosperm and outer layer using a non-destructive method.Conclusion. Digital radiography is an effective method for studying wheat when carrying out research and breeding tasks. The proposed model for the transmission of X-ray radiation in wheat grain can be used to numerically determine some indicators of grain quality.

Towards Enhancing Automated Defect Recognition (ADR) in Digital X-ray Radiography Applications: Synthesizing Training Data through X-ray Intensity Distribution Modeling for Deep Learning Algorithms

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.

Patient Satisfaction and Radiologic Assessability After Treatment of Complex Skull Defects With a Custom-Made Cranioplasty From a Thin Titanium Sheet.

The cosmetically good coverage of skull defects is a challenge in neurosurgical clinics. In addition, the skull treated with implants and the underlying structures must remain radiologically assessable. In this examination, the postoperative courses of patients after implantation of CranioTop is described. Digital x-ray, computed tomography, and magnetic resonance images after implantation of CranioTop were evaluated with regard to their assessability. Between 2018 and 2020, 23 titanium cranioplasties (CranioTop) were implanted to 21 patients. The intraoperative handling, the accuracy of fit, the healing process, the cosmetic result and the physical condition of the patients were examined. In addition, digital x-rays, magnetic resonance imaging, and computed tomography scans of the cranium supplied with CranioTop were examined. The evaluation showed good to very good results regarding patients' satisfaction. There were no severe complications; thirteen patients found the cosmetic result very good; 8 patients assessed the cosmetic result as good. Because of the low thickness and density of the CranioTop plastic there was only low formation of radial stripe artifacts (streaking) and susceptibility artifacts. The assessment of digital x-ray, computed tomography, and magnetic resonance imaging images is possible after implantation of CranioTop. The patients treated with CranioTop showed a high level of satisfaction with regard to the cosmetic result and their physical condition. Furthermore, the cranium supplied with CranioTop remains well assessable in radiologic imaging with only slight limitations in magnetic resonance imaging.

Применение метода газоразрядной визуализации для оценки скрытых дефектов семян

The gas-discharge visualization (GDV, electrophotography) method allows recording and quantifying the glow that occurs near the surface of the object (seed), when placing it in an electromagnetic field of high intensity. The hidden defects – emptiness of Pinus sibirica Du Tour seeds and biogenic damage (by phytopathogenic bacteria Pantoea ananatis) of Zea mays L. seeds were revealed and analyzed by the method of gas discharge visualization in combination with automatic analysis of digital gas-discharge images. It is shown that empty seeds have reduced values of the averaged intensity of digital gas discharge images. According to the data obtained, the methods of gas-discharge imaging (electrophotography) and microfocus radiography showed high efficiency (&gt; 90%) of accuracy in the detection of empty seeds of Pinus sibirica. It was found that the sample of Zea mays seeds (hybrid Krasnodarskiy 194 MV) was characterized by reduced values of the average intensity of gas-discharge images, as well as the average brightness of digital X-ray images, compared with the other three seed samples. The seed sample of hybrid Krasnodarskiy 194 MB was characterized by the worst sowing qualities: emergence rate, germination, root length and shoot length. It was shown that the Krasnodarskiy 194 MB seeds were affected by phytopathogenic bacteria Pantoea ananatis. The average inverse correlations of a number of characteristics of gas-discharge glow (area, total intensity) with the standard deviation of brightness of digital X-ray images, as well as weak reliable correlation of most gas-discharge characteristics with seeding qualities were found. Comparative assessment of gas discharge indices and seedlings germination as integral indices for evaluation of biological completeness of Zea mays seeds was carried out. Research has confirmed that the method of gas discharge visualization in combination with automatic analysis of digital gas-discharge images can be used as an effective additional tool for rapid detection of defective seeds.

Chest X-ray Foreign Objects Detection Using Artificial Intelligence.

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Research on a method of X - ray graph analysis for power equipment

The digital X-ray imaging detection technology is becoming more and more efficient and high-precision. X-ray digital imaging detection has been widely used in the quality detection, defect assessment and other technical fields of power equipment. At present, there is no unified image processing software in the electric power industry, and the software provided by detector manufacturers only covers basic functions, and there are many problems to be solved, such as no image quality judgment, image contour fitting, distortion correction and other core functions. Therefore, this paper proposes a kind of X-ray detection prevention for power equipment, which can provide the standardized determination of image quality and realize a variety of image processing functions.

Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region

Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region

Development and Validation of a Machine Learning System for Analysis and Radiological Diagnosis of Digital Chest X-ray Images

Introduction: Medicine is identified as one of the most promising field of application for Artificial Intelligence (AI). The current research presents development and validation of a machine learning system to diagnose chest x-ray images with higher accuracy. Methodology: It was a multi-cantered, experimental study conducted from 01 July, 2021 to 30 June, 2022. The experiment was a two-step process; in the first step, a machine learning system (MLS) was developed through training, testing and tuning a specialised computer hardware &amp; software utilising 5600 chest X ray images from NIH (National Institute of Health) chest X ray dataset. In the second step, 500 unseen chest X ray images from study centres were allowed to be diagnosed by the machine learning system and results were compared with expert opinions. Result: After the system was developed, validation was done on 3 different variations of Deep Residual Network and tested for their accuracy in classifying the findings. Using ResNet50V2, an average accuracy of 84.37%% was achieved. With case-specific variation, highest accuracy was 94.84%, highest specificity was 97.23% and highest sensitivity was 88.25%. Conclusion: With utilisation of this machine learning system, a faster radiological diagnosis of huge number of X ray images will become possible using only a small computer. Dependency on manpower, logistic support as well as rate of human-made errors can be minimised. However, this machine is never meant to replace an expert human opinion and it can never think beyond the box. J MEDICINE 2023; 24(2): 112-118