Ray Imaging Research Articles

A REVIEW OF CLINICAL FEATURES AND X-RAY IMAGES IN PATIENTS WITH ANKYLOSING SPONDYLITIS WHO UNDERWENT TOTAL HIP ARTHROPLASTY

Objective: To describe some clinical features and X - ray images of patients with ankylosing spondylitis who underwent total hip arthroplasty. Subject and method: This is a descriptive and cross-sectional study. 10 male patients with ankylosing spondylitis including 13 hips were underwent total hip arthroplasty using cement-less cup at The Joint Surgery Department of 108 Military Central Hospital from May 2015 to May 2019. The mean follow-up period was 33 months (range from 6 to 56 months). Result and conclusion: The combined form accounts for 9/10 patients. 10/10 patients in the research group were operated at the onset of the disease. Limitation and loss of movement of the cervical spine occurred to 7/10 patients, 8/10 patients with thoracic spine and 9/10 patients with lumbar spine. All 10 patients had injured hip in both sides. 9/13 hips suffered intense and consecutive pain. 11/13 patients suffered from loss of hip movement before surgery). 10/10 patients had signs of loss of spinal curvature, railway spine found in 4/10 patients, images of bone bridges and bamboo spine were presented in 5/10 patients. Damages to pelvic joint mainly occurred during stage III - IV with 12/13 joints, 1/13 joint during stage II. Injury to stage III and IV hips, according to BASRI - h accounted for 11/13 hip joints, 2/13 in stage II. The quality of the femur classified by Dorr type A accounted for 7/13 femur, type B accounted for 3/13 femur, type C accounted for 3/13 femur.

Statistical Analysis of X-ray Images and Laboratory Studies of COVID-19 Patients

The large increase in COVID-19 cases that continue occurring in Mexico has generated the need to learn more about the damage caused by this virus, for which it has been proposed to carry out a statistical study of the 164 ray images database X and 29 laboratory studies provided by the Salamanca State Center for Critical Care (CECCS) of patients diagnosed with COVID-19 and not COVID-19, the purpose is to analyze if there is any correlation between X-rays and laboratory studies. First, the entire database was organized by patient, both for X-ray and laboratory studies. Afterwards, the statistical process of the laboratory studies was carried out. Subsequently, we worked on the selection of patients with a greater number of X-rays, segment of these the area of the lungs, since it was our area of interest. In these radiographs, the statistical process of entropy and box-and-whisker plot is performed. Finally, the results of some laboratory studies are correlated with the entropy and box-and-whisker plot results.

Covid-19 Detection & Classification of chest X-rays using Deep Learning

Abstract: The deadly Covid-19 virus, also known as the Coronavirus has affected the entire world in a short period of time. This pandemic has affected a lot of people in the entire world and caused many deaths. In these difficult times, it is important for the doctors and the medical researchers to differentiate accurately between positive cases and negative cases. This CNN (Convolutional Neural Network) model will allow us to classify X-ray images into positive cases and the normal ones. This dataset is collected from different public sources as well as from some hospitals and physicians. Our goal is to take help from these X- ray images and develop a model where it predicts and classifies the infected cases. Keywords: CNN, Prediction, Classification, Features, Training, Testing, Deep Learning

3D Printed Patient-Specific Complex Hip Arthroplasty Models Streamline the Preoperative Surgical Workflow: A Pilot Study.

Introduction: Surgical planning for complex total hip arthroplasty (THA) often presents a challenge. Definitive plans can be difficult to decide upon, requiring unnecessary equipment to be ordered and a long theatre list booked. We present a pilot study utilising patient-specific 3D printed models as a method of streamlining the pre-operative planning process.Methods: Complex patients presenting for THA were referred to the research team. Patient-specific 3D models were created from routine Computed Tomography (CT) imaging. Simulated surgery was performed to guide prosthesis selection, sizing and the surgical plan.Results: Seven patients were referred for this pilot study, presenting with complex conditions with atypical anatomy. Surgical plans provided by the 3D models were more detailed and accurate when compared to 2D CT and X ray imaging. Streamlined equipment selection was of great benefit, with augments avoided post simulation in three cases. The ability to tackle complex surgical problems outside of the operating theatre also flagged potential complications, while also providing teaching opportunities in a low risk environment.Conclusion: This study demonstrated that 3D printed models can improve the surgical plan and streamline operative logistics. Further studies investigating the optimal 3D printing material and workflow, along with cost-benefit analyses are required before this process is ready for routine use.

Classification of Covid-19 patients using efficient fine-tuned deep learning DenseNet model

As COVID-19 pandemic caused completely spoils the livings, almost more than one year passed, still lives were not on the track. It is important to diagnose the COVID-19 patients earlier and provide the prompt treatment. The Convolutional Neural Network (CNN), a deep neural network that specializes in image processing and image classification. In this paper, a fine tuned DenseNet201 model was proposed which is used to classify Chest X ray images. Firstly, different DenseNet121, DenseNet169 and DenseNet201 model trained and tested on the same dataset. With the experiment, it is observed that DenseNet201 model performs well as compared to other dense models. Furthermore, DenseNet201 experiments over different optimizers and it is noticed that RMSprop, Adagrad and Adamax performs better. Proposed model achieves accuracy of 95.2% as compared to other models. We experimentally determine that RMSprop optimizer with DenseNet201 produces better results as similar to Adam and Adamax widely used optimizers.

Use of Full-Field X- ray Imaging and Ptychographic X-ray Computed Tomography for the Investigation of 3D Morphology of Micro-Nano Silver Materials for Advanced Electronics Packaging Applications

Use of Full-Field X- ray Imaging and Ptychographic X-ray Computed Tomography for the Investigation of 3D Morphology of Micro-Nano Silver Materials for Advanced Electronics Packaging Applications - Volume 27 Issue S1

Generalized chromatic aberrations in non-rotationally symmetric optical systems–Part I: mathematical approach

In non-rotationally symmetric optical systems, chromatic aberrations must be defined in a generalized way with reference to the optical axis ray and optimal tilted parabasal image plane of the central wavelength. In this paper, the definition of generalized chromatic aberrations is clarified. The mathematical calculation in optical systems with arbitrary symmetry and surface shape is realized by ray- and wavefront-based methods, respectively, that are originally identical. The additivity of wavefront after each surface ensures the surface decomposition of chromatic aberrations. In the end, the influence of pupil aberration, which is of a higher order, is discussed. The consistency of our methods with Seidel aberrations in the lowest aberration order of the rotationally symmetric system and the application of our methods in diverse non-rotationally symmetric refractive systems will be addressed in detail in Part II.

The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator

Both X-ray imaging and neutron imaging are essential methods in non-destructive testing. In this work, a bimodal imaging method combining neutron and X-ray imaging is introduced. The experiment is based on a small electron accelerator-based photoneutron source that can simultaneously generate the following two kinds of radiations: X-ray and neutron. This identification method utilizes the attenuation difference of the two rays’ incidence on the same material to determine the material’s properties based on dual-imaging fusion. It can enhance the identification of the materials from single ray imaging and has the potential for widespread use in on-site, non-destructive testing where metallic materials and non-metallic materials are mixed.

Impact of Threading Dislocations in GaN Power Switching Devices

Thick epitaxial (3-15 microns) GaN power switching devices are known to contain a high density of crystal defects, and especially threading dislocations in the epitaxial layer. The impact of these defects on device performance and reliability is a topic of ongoing research. This paper presents state-of-the-art methods for analyzing threading dislocation density, growth, and quantification of their impact on leakage current, blocking voltage, and peak E field. The effects of substrate type, epitaxial thickness, and processing techniques on dislocation density have been investigated to provide a roadmap for design and performance optimization of switching two terminal devices. Experimental and simulated data are used in conjunction to model dislocation impact on electrical performance characteristics and to identify techniques to minimize defect density.The experimental results have been achieved for 3 and 10 micron thick epitaxy as well as initial results for 15 micron epitaxy. The substrate was bulk GaN, and the epitaxy was grown by HPVPE by Kyma Corporation. The DMOS test structure investigated showed state of the art current voltage characteristics for a 10 micron n- epitaxy. Significant deviations were observed between the measured doping profile and the simulated profile, using a critical field of 3 MV/cm. For the DMOS device with epi doping of 8 × 1015 \U0001d450\U0001d45a−3 and epi thickness of 8 \U0001d707\U0001d45a. The device breaks down at approximately 2020 \U0001d449. At 2000 \U0001d449, the peak field is roughly 3.11 \U0001d440\U0001d449/\U0001d450\U0001d45a. For a device with epi doping of 8 × 1015 \U0001d450\U0001d45a−3 and epi thickness of 15 \U0001d707\U0001d45a. The device breaks down at approximately 3020 \U0001d449. At 3000 \U0001d449, the peak field is roughly 3.14 \U0001d440\U0001d449/\U0001d450\U0001d45a. The observed trend is significant since it shows that by increasing the epi thickness, also results in an increase in the peak field. For thin GaN epi of 3 microns, a peak breakdown field of 2.77 MV/cm was measured. These three date points will allow one to determine a relationship between peak critical field and epi thickness, which is a major requirement for designing vertical GaN high voltage switches.We report images from transmission x-ray topographs and TEM investigations as well as the correlation with critical breakdown field measurements. The transmission x-ray topograph shows that the GaN is characterized by a nearly uniform distribution of strain centers which are likely bundles of threading screw and edge dislocations. Strain free centers are then regions of relatively low dislocation densities. The threading dislocations are part of dislocation loops which are present in the GaN substrate and migrate through the interface. The cross-sectional TEM image shows partial dislocation loops through the thickness of the GaN sample.Figure: X-Ray Topography of GaN/GaN showing a nearly uniform distribution of strain centers. The Grazing incidence topograph is similar to the transmission image, the grazing image also shows the nearly uniform distribution of strain centers. The X ray image shows that individual threading dislocations are not resolved showing a relatively low dislocation density in those regions. Figure 1

Gamma Camera Imaging with Rotating Multi-Pinhole Collimator. A Monte Carlo Feasibility Study.

In this work, we propose and analyze a new concept of gamma ray imaging that corresponds to a gamma camera with a mobile collimator, which can be used in vivo, during surgical interventions for oncological patients for localizing regions of interest such as tumors or ganglia. The benefits are a much higher sensitivity, better image quality and, consequently, a dose reduction for the patient and medical staff. This novel approach is a practical solution to the overlapping problem which is inherent to multi-pinhole gamma camera imaging and single photon emission computed tomography and which translates into artifacts and/or image truncation in the final reconstructed image. The key concept consists in introducing a relative motion between the collimator and the detector. Moreover, this design could also be incorporated into most commercially available gamma camera devices, without any excessive additional requirements. We use Monte Carlo simulations to assess the feasibility of such a device, analyze three possible designs and compare their sensitivity, resolution and uniformity. We propose a final design of a gamma camera with a high sensitivity ranging from 0.001 to 0.006 cps/Bq, and a high resolution of 0.5–1.0 cm (FWHM), for source-to-detector distances of 4–10 cm. Additionally, this planar gamma camera provides information about the depth of source (with approximate resolution of 1.5 cm) and excellent image uniformity.

A Radiolucent Electromagnetic Tracking System for Use with Intraoperative X-ray Imaging.

In recent times, the use of electromagnetic tracking for navigation in surgery has quickly become a vital tool in minimally invasive surgery. In many procedures, electromagnetic tracking is used in tandem with X-ray technology to track a variety of tools and instruments. Most commercially available EM tracking systems can cause X-ray artifacts and attenuation due to their construction and the metals that form them. In this work, we provide a novel solution to this problem by creating a new radiolucent electromagnetic navigation system that has minimal impact on -ray imaging systems. This is a continuation of our previous work where we showed the development of the Anser open-source electromagnetic tracking system. Typical electromagnetic tracking systems operate by generating low frequency magnetic fields from coils that are located near the patient. These coils are typically made from copper, steel, and other dense radiopaque materials. In this work, we explore the use of low density aluminum to create these coils and we demonstrate that the effect on X-ray images is significantly reduced as a result of these novel changes in the materials used. The resulting field generator is shown to give at least a 60% reduction in the X-ray attenuation in comparison to our earlier designs. We verify that the system accuracy of approximately 1.5 mm RMS error is maintained with this change in design.

Deep learning algorithms for COVID 19 data analytics

Covid-19 is a viral disease that affects the humans very fastly where by the death rate also increasing day by day. Recently a vaccine for COVID-19 is identified and being used by a particular age of people. There are different types of tests are being used to identify whether a person is having a corona or not. Mainly, the lung is being affected by people and unable to breath continuously. For diagnosing lunge related problems, Chest x-ray and chest CT imaging techniques are widely used. Various researches are being conducted to identify corona virus by using chest X ray images. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep Learning techniques are being applied for analyzing a large amount of chest x-ray images which could screen the COVID-19.This paper analysis the various deep learning techniques applied for identifying corona virus symptoms using chest x ray images. Convolutional Neural Network algorithm has achieved 95% accuracy for analyzing COVID 19 and Normal patients. © 2021 Karadeniz Technical University. All rights reserved.

Novel approach to determine age and gender from wrist x-ray images

Human gender and age prediction in field of forensic department is a very important and crucial stage in means of criminal and judicial law. Human identification is essential when it required for recognising a body in case of mass disaster and natural disaster like earth quake, floods, tsunamis, hurricanes and other geological process that causes huge damage for mankind and loss of life. Human bones during the growth stages undergo few substantial changes of size and shapes. In diagnosing growth of bones, x ray images are frequently used. Hand x ray images in particular has been chosen as a part of x ray imaging, since hand has more unique features and more number of parts. Manual technique of identification is also attainable, but this process can be adopted when medical practitioners, assistants and basic tools are available. Manual method can be carried out based on the availability of bone like skull, long bones, short bones, hand, pelvis bone etc. It requires ample time to process the accurate outcome of the available samples. Hence hand operated technique is not feasible for identification. A machine driven automated system for gender and age identification is essential to overcome the flaws occurred in manual technique. This would facilitate better outcome in lesser time, without intervention of labour and also enables quantitative and accurate assessments. In the proposed system, we have identified most important features from wrist bone which contributes in age and gender identification. Main aim of our study is to identify gender and estimation of age of Middle East population of Karnataka state in India by analysing digital images of wrist bone. Random forest classification algorithm is used to deploy this system by considering 76 male samples and 50 female samples in total 126 wrist radiographs of age group between 06 to 78 years old. Random forest classifier belongs to decision tree family, each decision tree when executed may tends to overfit that training data, but random forest avoids this over fitting problems and it will try to capture maximum amount of pattern. Since multiple decision trees are implemented in RFC, this makes it a power full classification algorithm that will predict results with higher accuracy most of the time. Accuracy of 97% is achieved in the present work for age and gender prediction.

Type Radiation Resisting High Density Concrete - A General Review

Exposure to very high levels of radiation, can cause acute health effects such as skin burns and acute radiation syndrome (“radiation sickness”). It can also result in long-term health effects such as cancer and cardiovascular disease. The Intensity of the radiation which a body receives is inversely proportional to the Thickness or density of the shielding material. Concrete is already an effective Shielding material but if its density is increased, it can be even more effective. Thus, producing high density concrete provides good resistant towards radiation. High density concrete is widely used in nuclear Powerplants and reactors to provide shielding against harmful radiations Emitting from the process of nuclear fission. It can be widely used in hospitals Where radioactive materials are used for medicinal purposes such as radiation Therapy, x ray imaging etc.

Basic study of mobile gamma ray imaging using a digital camera and scintillator

Gamma cameras are used in nuclear medicine examinations involving radioisotopes; however, they do not provide real-time feedback. We propose a real-time imaging method based on a commercially available digital camera and a scintillator array to provide simple and accurate measurements of radioisotope accumulation and contamination. We evaluate the sensitivity and resolution of the proposed device using x-rays as a proxy for gamma-rays. The performance of the device is demonstrated using PENTAX KP and ORCA-spark C11440-36U digital cameras. A caesium iodide scintillator array is irradiated with x-rays, with the state of light emission confirmed using live view images. The pixel value is evaluated as a function of dose rate. Furthermore, we investigate the state of light emission in response to amplifying the light signal using an image intensifier. For the PENTAX KP, luminescence is observable for a dose rate of approximately 10 mSv h−1, which changes to 2.1 mSv h−1 when an image intensifier is used. Notably, the ORCA-spark detected emission at a low dose rate of 0.06 mSv h−1. However, using an image intensifier resulted in noisier images. Therefore, although the ORCA-spark can observe luminescence at a suitable predicted dose rate for application in nuclear medicine examinations, a collimator is required to control the spread of gamma rays. However, as this causes the sensitivity to decrease, increasing the amount of light emitted by the scintillator and improving the sensitivity of the camera is vital.

Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network.

In this paper, we describe the convolutional neural network (CNN)-based approach to the problems of categorization and artefact reduction of cosmic ray images obtained from CMOS sensors used in mobile phones. As artefacts, we understand all images that cannot be attributed to particles’ passage through sensor but rather result from the deficiencies of the registration procedure. The proposed deep neural network is composed of a pretrained CNN and neural-network-based approximator, which models the uncertainty of image class assignment. The network was trained using a transfer learning approach with a mean squared error loss function. We evaluated our approach on a data set containing 2350 images labelled by five judges. The most accurate results were obtained using the VGG16 CNN architecture; the recognition rate (RR) was 85.79% ± 2.24% with a mean squared error (MSE) of 0.03 ± 0.00. After applying the proposed threshold scheme to eliminate less probable class assignments, we obtained a RR of 96.95% ± 1.38% for a threshold of 0.9, which left about 62.60% ± 2.88% of the overall data. Importantly, the research and results presented in this paper are part of the pioneering field of the application of citizen science in the recognition of cosmic rays and, to the best of our knowledge, this analysis is performed on the largest freely available cosmic ray hit dataset.

Radiographic differences in the concomitant deformities in two types of medial ankle osteoarthritis.

ObjectivesMotion preserving surgeries could be unsuccessful because of underestimation of deformities of the foot and knee in ankle osteoarthritis. This study aimed to investigate the concomitant deformities in medial ankle osteoarthritis and the difference between the two types, varus angulation and medial translation.MethodsA retrospective study was conducted using medical records and radiographic data. Patients with medial ankle osteoarthritis that underwent weight-bearing X ray imaging and radiographic measurements including tibial plafond inclination (TPI), tibiotalar tilt angle (TT), lateral talo-first metatarsal angle, naviculo-cuboid overlap, and mechanical tibiofemoral angle (mTFA) were studied. The patients were categorized into two groups, the varus angulation group (TT ≥4°) and medial translation group (TT <4°). The radiographic measurements were compared between the two groups.ResultsA total of 102 patients (male = 44; female = 58) were included; the mean age was 64.9 years (SD 8.3 years). The varus rotation group (N = 66) showed a significantly smaller lateral talo-first metatarsal angle (p<0.001), naviculo-cuboid overlap (p<0.001), and mTFA (p = 0.019) compared to the medial displacement group (N = 36). The TT showed a significant correlation with lateral talo-first metatarsal angle (r = -0.520, p<0.001), naviculo-cuboid overlap (r = -0.501, p<0.001), and mTFA (r = -0.243, p = 0.014). Lateral talo-first metatarsal angle was found to be the significant factor (p = 0.018) discriminating varus angulation and medial translation types in the binary logistic analysis.ConclusionsVarus angulation of the ankle was correlated with knee alignment and foot deformity. Radiographic indices were different between the varus angulation and medial translation groups. The role of concomitant deformities needs to be further investigated in terms of a causal relationship. Surgeons need to pay attention to concomitant deformities in the treatment of medial ankle osteoarthritis.

Wavelet Based CNN for Diagnosis of COVID 19 using Chest X Ray

Human race has overcome numerous pandemic and epidemics like Spanish flu, SARS, cholera, plague, etc since ages and COVID 19 pandemic is one among them. COVID 19 being the recent one, is much different than the others due to the contribution of AI in diagnosis and prediction of COVID 19 patients. Among the various use cases, one widely used area is medical diagnosis. AI and deep learning based algorithms are exploited enormously by data scientist to support radiologist in early prediction and detection of corona patients. Subsequently, in this work, we utilize wavelet based Convolutional Neural Networks for detecting and recognizing of COVID 19 cases from chest X ray images. Currently, previous works utilize existing CNN architectures for classification of healthy and affected chest X rays, however these networks process the image in a single resolution and loss the potential features present in other resolutions of the input image. Wavelets are known to decompose the image into different spatial resolutions based on the high pass and low pass frequency components and extract valuable features from the affected portion of lung X ray efficiently. Henceforth, in this article, we utilize a hybrid CNN model of wavelet and CNN to diagnose the lung X rays. The proposed CNN model is trained and validated on open source COVID 19 chest X ray images and performs better than existing state of the art CNN models with an accuracy of 99.25%, ROC-AUC value of 1.00 and very less false negative values. Further, the performance of our model is validated by Gradient Class Activation Map visualization technique. The visualization of feature maps clearly indicates that our proposed network has perfectly extracted features from the corona virus affected portion of the lung.

Development and Evaluation of the Fourier Spectral Distortion Metric.

A spatial resolution metric is presented for tomosynthesis. The Fourier spectral distortion metric (FSD) was developed to evaluate specific resolution properties of different imaging techniques for digital tomosynthesis using a star pattern image to plot modulation in the frequency domain. The FSD samples the spatial resolution of a star-pattern image tangentially over an acute angle and for a range of spatial frequencies in a 2D image or 3D image reconstruction slice. The FSD graph portrays all frequencies present in a star pattern quadrant. In addition to the fundamental input frequency of the star pattern, the FSD graph shows spectral leakage, square wave harmonics, and residual noise. The contrast transfer function (CTF) is obtained using the FSD graph. The CTF is analogous to the modulation transfer function (MTF), but it is not normalized to unity at zero spatial frequency. Unlike the MTF, this metric separates the fundamental input-frequency from the other signals in the Fourier domain. This metric helps determine optimal image reconstruction parameters, the in-plane limit of spatial resolution with respect to aliased signals, and a threshold criterion for an image to support super resolution and reduce aliasing artifacts. Various sampling parameters were evaluated to optimize this metric and ascertain measurement accuracy. The FSD adequately compares resolution properties of 2D images and 3D image reconstruction slices for various x ray imaging modes without suppressing aliased signals.

Feasibility study of fast neutron-induced gamma ray imaging of large sample based on D-T neutron generator

Elemental imaging plays an important role in characterizing the chemical and physical properties of large samples. In this work, a position-sensitive technique using prompt gamma ray neutron activation analysis (PGNAA) was applied to image the internal composition of large samples with 10–20 cm thicknesses. An inelastic neutron scattering system consisting of a D-T neutron generator and a high-purity germanium (HPGe) detector was built and used for elemental distribution analysis of a large metal sample containing iron, titanium, and copper. The 2D elemental imaging of the three elements was performed using the designed system. The effect of inhomogeneous neutron field was also corrected using Monte Carlo simulations. The results indicate that ~4 cm spatial resolution can be achieved. The position-sensitive elemental analysis based on inelastic neutron scattering reaction is efficient for bulk metal sample analysis.