High-quality quasi-parallel X-ray beam obtained by a parabolic monocapillary X-ray lens with a square beam stop

2021 ◽  
pp. 1-13
Author(s):  
Peng Zhou ◽  
Jingduo Cui ◽  
Zelin Du ◽  
Tao Zhang ◽  
Zhiguo Liu
Keyword(s):  
Optical Path ◽  
X Rays ◽  
High Quality ◽  
Optimal Position ◽  
X Ray ◽  
Proposed Model ◽  
Beam Stop ◽  
Path System ◽  
New Applications ◽  
Study Designs

Parabolic monocapillary X-ray lens (PMXRL) is an ideal optical device for constraining the point divergent X-ray beams to quasi-parallel beams, but the overlap of direct X-rays and reflected X-rays through PMXRL deteriorates the outgoing beam divergence. Aiming to solve this problem, this study designs and tests a square-shaped lead occluder (SSLO) embedded in PMXRL to block the direct X-rays passing through the PMXRL. Python simulations are employed to determine the geometric parameters of the SSLO as well as the optimal position of the SSLO in the PMXRL according to our proposed model. The PMXRL with a conic parameter p of 0.000939 mm and a length L of 60.8 mm is manufactured and the SSLO with a size of 0.472 mm×0.472 mm×3.4 mm is embedded into it. An optical path system based on this PMXRL is built to measure the divergence of the outgoing X-ray beam. The experimental results show that the quasi-parallel X-ray beam reaches a divergence of 0.36 mrad in the range from 15–45 mm at the PMXRL outlet. This divergence is 10 times lower than the theoretical divergence without SSLO. Our work provides an alternative method for obtaining highly parallel X-ray beam and is beneficial to generate or facilitate new applications of monocapillary optics in X-ray technology.

scholarly journals Optimization of X-ray Investigations in Dentistry Using Optical Coherence Tomography

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4554
Author(s):  
Ralph-Alexandru Erdelyi ◽  
Virgil-Florin Duma ◽  
Cosmin Sinescu ◽  
George Mihai Dobre ◽  
Adrian Bradu ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Radiation Dose ◽  
Imaging Techniques ◽  
Image Resolution ◽  
Optical Coherence ◽  
X Rays ◽  
High Quality ◽  
X Ray

The most common imaging technique for dental diagnoses and treatment monitoring is X-ray imaging, which evolved from the first intraoral radiographs to high-quality three-dimensional (3D) Cone Beam Computed Tomography (CBCT). Other imaging techniques have shown potential, such as Optical Coherence Tomography (OCT). We have recently reported on the boundaries of these two types of techniques, regarding. the dental fields where each one is more appropriate or where they should be both used. The aim of the present study is to explore the unique capabilities of the OCT technique to optimize X-ray units imaging (i.e., in terms of image resolution, radiation dose, or contrast). Two types of commercially available and widely used X-ray units are considered. To adjust their parameters, a protocol is developed to employ OCT images of dental conditions that are documented on high (i.e., less than 10 μm) resolution OCT images (both B-scans/cross sections and 3D reconstructions) but are hardly identified on the 200 to 75 μm resolution panoramic or CBCT radiographs. The optimized calibration of the X-ray unit includes choosing appropriate values for the anode voltage and current intensity of the X-ray tube, as well as the patient’s positioning, in order to reach the highest possible X-rays resolution at a radiation dose that is safe for the patient. The optimization protocol is developed in vitro on OCT images of extracted teeth and is further applied in vivo for each type of dental investigation. Optimized radiographic results are compared with un-optimized previously performed radiographs. Also, we show that OCT can permit a rigorous comparison between two (types of) X-ray units. In conclusion, high-quality dental images are possible using low radiation doses if an optimized protocol, developed using OCT, is applied for each type of dental investigation. Also, there are situations when the X-ray technology has drawbacks for dental diagnosis or treatment assessment. In such situations, OCT proves capable to provide qualitative images.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

scholarly journals Macromolecular Nanocrystal Structural Analysis with Electron and X-Rays: A Comparative Review

Molecules ◽  
2019 ◽  
Vol 24 (19) ◽  
pp. 3490
Author(s):  
Krishna P. Khakurel ◽  
Borislav Angelov ◽  
Jakob Andreasson
Keyword(s):  
Structural Analysis ◽  
Structural Models ◽  
X Rays ◽  
High Quality ◽  
X Ray ◽  
Detection Techniques ◽  
Comparative Review

Crystallography has long been the unrivaled method that can provide the atomistic structural models of macromolecules, using either X-rays or electrons as probes. The methodology has gone through several revolutionary periods, driven by the development of new sources, detectors, and other instrumentation. Novel sources of both X-ray and electrons are constantly emerging. The increase in brightness of these sources, complemented by the advanced detection techniques, has relaxed the traditionally strict need for large, high quality, crystals. Recent reports suggest high-quality diffraction datasets from crystals as small as a few hundreds of nanometers can be routinely obtained. This has resulted in the genesis of a new field of macromolecular nanocrystal crystallography. Here we will make a brief comparative review of this growing field focusing on the use of X-rays and electrons sources.

scholarly journals X-Ray Superbubbles

1983 ◽  
Vol 6 ◽  
pp. 675-680
Author(s):  
Webster Cash
Keyword(s):  
Interstellar Medium ◽  
Collective Effects ◽  
Selection Effects ◽  
X Rays ◽  
High Quality ◽  
X Ray ◽  
Common Structure ◽  
The Galaxy ◽  
Gum Nebula

AbstractFour regions of the galaxy, the Cygnus Superbubble, the ƞ Carina complex, the Orion/Eridanus complex, and the Gum Nebula, are discussed as examples of collective effects in the interstellar medium. All four regions share certain features, indicating a common structure. We discuss the selection effects which determine the observable x-ray properties of the superbubbles and demonstrate that only a very few more in our galaxy can be detected in x-rays. X-ray observation of extragalactic superbubbles is shown to be possible but requires the capabilities of a large, high quality, AXAF class observatory.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans

2020 ◽  
Author(s):  
Amit Kumar Jaiswal ◽  
Prayag Tiwari ◽  
Vipin Kumar Rathi ◽  
Jia Qian ◽  
Hari Mohan Pandey ◽  
...  
Keyword(s):  
False Negative ◽  
Healthcare Providers ◽  
False Negative Rate ◽  
X Rays ◽  
X Ray ◽  
Detection Model ◽  
Global Pandemic ◽  
High False Negative Rate ◽  
Proposed Model ◽  
Chest X Ray

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

scholarly journals Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

2021 ◽  
Vol 38 (3) ◽  
pp. 903-909
Author(s):  
Veeranjaneyulu Naralasetti ◽  
Reshmi Khadherbhi Shaik ◽  
Gayatri Katepalli ◽  
Jyostna Devi Bodapati
Keyword(s):  
Experimental Studies ◽  
Cross Entropy ◽  
X Rays ◽  
Weight Decay ◽  
X Ray ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Classification Tasks ◽  
Multi Class Classification ◽  
Fully Connected

Diagnosis based on chest X-rays is widely used and approved for the diagnosis of various diseases such as Pneumonia. Manually screening of theses X-ray images technician or radiologist involves expertise and time consuming. Addressing this, we propose an automated approach for the diagnosis of pneumonia by assisting doctors in spotting infected areas in the X-ray images. We propose a deep Convolutional Neural Network (CNN) model for efficiently detecting the presence of pneumonia in the X-ray images. The proposed CNN is designed with 5 convolution blocks followed by 4 fully connected layers. In order to boost the performance of the model, we incorporate batch normalization, dynamic dropout, learning rate decay, L2 regularization weight decay along with Adam optimizer and binary Cross-Entropy loss function while training the model using back propagating algorithm. The proposed model is validated on two publicly accessible benchmark datasets, and the experimental studies conducted on these datasets indicate that the proposed model is efficient. The suggested CNN architecture with specified hyper parameters allows the model to outperform several existing models by achieving accuracy of 97.73% and 91.17% respectively for binary and multi-class classification tasks of pneumonia disease.

scholarly journals IVAN PULUJ AND THE DISCOVERY OF X-RAYS

2021 ◽  
Vol 64 (1) ◽  
Author(s):  
Ulyana Pidvalna ◽  
◽  
Roman Plyatsko ◽  
Vassyl Lonchyna ◽  
◽  
...  
Keyword(s):  
International Exhibition ◽  
Bone Lesions ◽  
X Rays ◽  
High Quality ◽  
X Ray ◽  
Exact Date ◽  
Starting Point ◽  
The World ◽  
New Type ◽  
The Given

On January 5, 1896, the Austrian newspaper Die Presse published an article entitled “A Sensational Discovery”. It was dedicated to the discovery of X-rays made on November 8, 1895 by the German physicist Wilhelm Conrad Röntgen. Having taken into account the contribution of other scientists, the precondition of the given epochal, yet unexpected, discovery was, first and foremost, the work of the Ukrainian scientist Ivan Puluj. It was Puluj who laid the foundation for X-ray science. He explained the nature of X-rays, discovered that they can ionize atoms and molecules, and defined the place of X-ray emergence and their distribution in space. In 1881, Puluj constructed a cathode lamp (“Puluj’s tube”) which was fundamentally a new type of light source. In the same year, in recognition of this discovery, Puluj received an award at the International Exhibition in Paris. Investigating the processes in cathode-ray tubes, Ivan Puluj set the stage for two ground-breaking discoveries in physics, namely X-rays and electrons. Puluj used his cathode lamp in medicine as a source of intense X-rays which proved to be highly efficient. The exact date of the first X-ray images received by Puluj remains unknown. High-quality photographs of the hand of an eleven-year-old girl, taken on January 18, 1896, are preserved. Multiple X-ray images clearly visualized pathological changes in the examined structures (fractures, calluses, tuberculous bone lesions). High-quality images were obtained by means of the anticathode in the design of Puluj’s lamp, which was the first in the world. The image of the whole skeleton of a stillborn child (published on April 3, 1896 in The Photogram) is considered to be the starting point of using X-rays in anatomy.

scholarly journals Deep COVID 19: Deep Learning for COVID 19 Detection from X ray Images

2021 ◽  
Vol 11 (1) ◽  
pp. 1-6
Author(s):  
Ahmed Hashem El Fiky ◽  
Keyword(s):  
Machine Learning Algorithms ◽  
Diagnostic Process ◽  
X Rays ◽  
Virus Spread ◽  
Large Dataset ◽  
X Ray ◽  
Support Mechanism ◽  
Proposed Model ◽  
Speed Up ◽  
First Time

The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for analysis, the CNN transfer learning approach was used. We also developed a simple CNN architecture with a modest number of parameters that does a good job of differentiating COVID-19 from regular X-rays. in this paper, we are used large dataset which contained CXR images of normal patients and patients with COVID-19. the number of CXR images for normal patients are 10,192 image and the number of CXR images for COVID-19 patients are 3,616 images. The results of experiments show the effectiveness and robustness of Deep-COVID-19 and pretrained models like VGG16, VGG19, and MobileNets. Our proposed Model Deep-COVID-19 achieved over 94.5% accuracy.

scholarly journals A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad
Keyword(s):  
Deep Learning ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Global Pandemic ◽  
Proposed Model ◽  
Dental Diseases ◽  
Chest X Ray ◽  
Artery Disease ◽  
Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

Sign in / Sign up

Export Citation Format

Share Document