scholarly journals Research on X-ray Fluorescence Enhanced Fluoroscopy Imaging Technology

Photonics ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 441
Author(s):  
Zhenyao Yan ◽  
Liang Li ◽  
Rui Qiu ◽  
Zhiqiang Chen
Keyword(s):  
Material Selection ◽  
Surrounding Tissue ◽  
Imaging Method ◽  
Attenuation Coefficients ◽  
Scanning Method ◽  
Low Contrast ◽  
X Ray ◽  
Wide Range ◽  
Chest X Ray ◽  
Beam Collimation

Chest X-ray fluoroscopy is a commonly used medical imaging method, which has a wide range of applications in the diagnosis of lung diseases and other fields. However, due to low contrast and relatively close linear attenuation coefficients, some early small lesions are difficult to detect in time. Using the X-ray fluorescent effect of high atomic number metal elements and metal atom-containing agents that can be enriched in the lesion, the fluoroscopy signal and the fluorescent signal emitted by the metal atoms can be detected at the same time during the fluoroscopy, and the images of the two can be integrated, which can theoretically enhance the contrast between the lesion and the surrounding tissue. Based on GEANT4, this paper conducts Monte Carlo simulations to explore the feasibility and enhancement effects of three enhancement schemes: the pencil beam spot scanning method, cone-beam collimation method, and slit scanning method, and discusses the specific geometric structure and material selection.

Early Acoustic Warning for the Onset of Acute Chest Syndrome in Sickle Cell Patients

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Brian Henry ◽  
Gardner Yost ◽  
Robert Molokie ◽  
Thomas J. Royston
Keyword(s):  
Sickle Cell ◽  
Hospital Setting ◽  
Pulmonary Infiltrate ◽  
Acute Chest Syndrome ◽  
Computational Techniques ◽  
X Ray ◽  
Wide Range ◽  
Chest X Ray ◽  
Computer Based ◽  
In Silico Models

Acute chest syndrome (ACS) is a leading cause of death for those with sickle cell disease (SCD). ACS is defined by the development of a new pulmonary infiltrate on chest X-ray, with fever and respiratory symptoms. Efforts have been made to apply various technologies in the hospital setting to provide earlier detection of ACS than X-ray, but they are expensive, increase radiation exposure to the patient, and are not technologies that are easily transferrable for home use to help with early diagnosis. We present preliminary studies on patients suggesting that acoustical measurements recorded quantitatively with contact sensors (electronic stethoscopes) and analyzed using advanced computational analysis methods may provide an earlier diagnostic indicator of the onset of ACS than is possible with current clinical practice. Novel in silico models of respiratory acoustics utilizing image-based and algorithmically developed lungs with full conducting airway trees support and help explain measured acoustic trends and provide guidance on the next steps in developing and translating a diagnostic approach. More broadly, the experimental and computational techniques introduced herein, while focused on monitoring and predicting the onset of ACS, could catalyze further advances in mobile health (mhealth)-enabled, computer-based auscultative diagnoses for a wide range of cardiopulmonary pathologies.

scholarly journals Theoretical study of the properties of X-ray diffraction moiré fringes. II. Illustration of angularly integrated moiré images

2019 ◽  
Vol 75 (4) ◽  
pp. 610-623
Author(s):  
Jun-ichi Yoshimura
Keyword(s):  
Incident Wave ◽  
Angular Width ◽  
X Ray Diffraction ◽  
Experimental Conditions ◽  
Acta Cryst ◽  
Low Contrast ◽  
X Ray ◽  
Moiré Fringes ◽  
Wide Range ◽  
Fringe Patterns

Using a theory of X-ray diffraction moiré fringes developed in a previous paper, labelled Part I [Yoshimura (2015). Acta Cryst. A71, 368–381], the X-ray moiré images of a silicon bicrystal having a weak curvature strain and an interspacing gap, assumed to be integrated for an incident-wave angular width, are simulation-computed over a wide range of crystal thicknesses and incident-wave angular width, likely under practical experimental conditions. Along with the simulated moiré images, the graphs of characteristic quantities on the moiré images are presented for a full understanding of them. The treated moiré images are all of rotation moiré. Mo Kα1 radiation and the 220 reflection were assumed in the simulation. The results of this simulation show that fringe patterns, which are significantly modified from simple straight fringes of rotation moiré, appear in some ranges of crystal thicknesses and incident-wave angular width, due to a combined effect of Pendellösung oscillation and an added phase difference from the interspacing gap, under the presence of a curvature strain. The moiré fringes which slope to the perpendicular direction to the diffraction vector in spite of the assumed condition of rotation moiré, and fringe patterns where low-contrast bands are produced with a sharp bend of fringes arising along the bands are examples of the modified fringe pattern. This simulation study provides a wide theoretical survey of the type of bicrystal moiré image produced under a particular condition.

Imaging of retina cellular and subcellular structures using ptychographic hard X-ray tomography

2021 ◽  
Vol 134 (19) ◽  
Author(s):  
Valerie Panneels ◽  
Ana Diaz ◽  
Cornelia Imsand ◽  
Manuel Guizar-Sicairos ◽  
Elisabeth Müller ◽  
...  
Keyword(s):  
Focused Ion Beam ◽  
Ion Beam ◽  
Plexiform Layer ◽  
Wild Type Mouse ◽  
Small Region ◽  
Mouse Retina ◽  
Imaging Method ◽  
Scanning Method ◽  
X Ray ◽  
Correlative Imaging

ABSTRACT Ptychographic hard X-ray computed tomography (PXCT) is a recent method allowing imaging with quantitative electron-density contrast. Here, we imaged, at cryogenic temperature and without sectioning, cellular and subcellular structures of a chemically fixed and stained wild-type mouse retina, including axons and synapses, with complete isotropic 3D information over tens of microns. Comparison with tomograms of degenerative retina from a mouse model of retinitis pigmentosa illustrates the potential of this method for analyzing disease processes like neurodegeneration at sub-200 nm resolution. As a non-destructive imaging method, PXCT is very suitable for correlative imaging. Within the outer plexiform layer containing the photoreceptor synapses, we identified somatic synapses. We used a small region inside the X-ray-imaged sample for further high-resolution focused ion beam/scanning electron microscope tomography. The subcellular structures of synapses obtained with the X-ray technique matched the electron microscopy data, demonstrating that PXCT is a powerful scanning method for tissue volumes of more than 60 cells and sensitive enough for identification of regions as small as 200 nm, which remain available for further structural and biochemical investigations.

scholarly journals One Shot Cluster Based Approach for the Detection of COVID-19 from Chest X-Ray Images

2020 ◽  
Author(s):  
V.N. Manjunath Aradhya ◽  
Mufti Mahmud ◽  
Basant Agarwal ◽  
D.S. Guru ◽  
M. Shamim Kaiser
Keyword(s):  
Virus Disease ◽  
X Rays ◽  
Probabilistic Neural Networks ◽  
X Ray ◽  
The Core ◽  
Wide Range ◽  
Test Instance ◽  
Chest X Ray ◽  
Detection And Diagnosis ◽  
The Way

Corona virus disease (COVID-19) has infected over more than 10 million people around the globe and killed at least 500K worldwide by the end of June 2020. As this disease continues to evolve and scientists and researchers around the world now trying to find out the way to combat this disease in most effective way. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. Precise detection and diagnosis of COVID-19 from these chest X-rays would be practical for the current situation. This paper proposes one shot cluster based approach for the accurate detection of COVID-19 chest x-rays. The main objective of one shot learning (OSL) is to mimic the way humans learn in order to make classification or prediction on a wide range of similar but novel problems. The core constraint of this type of task is that the algorithm should decide on the class of a test instance after seeing just one test example. For this purpose we have experimented with widely known Generalized Regression and Probabilistic Neural Networks. Experiments conducted with publicly available chest x-ray images demonstrate that the method can detect COVID-19 accurately with high precision. The obtained results have outperformed many of the convolutional neural network based existing methods proposed in the literature.

scholarly journals Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

MENDEL ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 9-17
Author(s):  
Hiam Alquran ◽  
Mohammad Alsleti ◽  
Roaa Alsharif ◽  
Isam Abu Qasmieh ◽  
Ali Mohammad Alqudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Features ◽  
Automated System ◽  
Rt Pcr ◽  
False Negatives ◽  
X Ray ◽  
Organ Systems ◽  
Wide Range ◽  
Chest X Ray

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks

2020 ◽  
Vol 39 (3) ◽  
pp. 2893-2907 ◽  
Author(s):  
Huaiguang Wu ◽  
Pengjie Xie ◽  
Huiyi Zhang ◽  
Daiyi Li ◽  
Ming Cheng
Keyword(s):  
Median Filter ◽  
Recognition Rate ◽  
Identification System ◽  
X Rays ◽  
Learning Networks ◽  
Low Contrast ◽  
X Ray ◽  
Detection Model ◽  
Chest X Ray ◽  
Fitting In

The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.

scholarly journals Lung Ultrasonography Decreases Radiation Exposure in Newborns with Respiratory Distress

2021 ◽  
Author(s):  
Umit Ayse Tandircioglu ◽  
Sule Yigit ◽  
Berna Oguz ◽  
Gozdem Kayki ◽  
Hasan Tolga Celik ◽  
...  
Keyword(s):  
Respiratory Distress ◽  
Radiation Exposure ◽  
Congenital Heart ◽  
Newborn Infants ◽  
Imaging Method ◽  
Lung Ultrasonography ◽  
First Line ◽  
Catheter Position ◽  
X Ray ◽  
Chest X Ray

Abstract Chest X-ray(CXR) is commonly used as a first line imaging method to diagnose the reason of respiratory distress in NICUs.Lung ultrasound is a new diagnostic tool for lung imaging. We aimed to determine the decrease in the number of CXRs on the first day of life in newborns with respiratory distress,with the use of lung ultrasonography. From January 2019 to June 2020,104 newborn infants hospitalized in the NICU with respiratory distress on the first day of life enrolled in this study(ClinicalTrials.govIdentifier NCT04722016).We used ultrasound as the first line technique for lung imaging.CXR was taken to determine endotracheal tube and umbilical catheter position or if considered necessary by the physician in charge of the infant.We calculated decreased number of CXR for every patient and evaluated the estimated decrease in radiation exposure. 104 neonates with median 36 weeks(25–40)gestational age and birth weight 2410gr(600–4100) enrolled in the study.Seventy(67,3%)of these babies were male.In the study group,24(23,1 %)patients were diagnosed with respiratory distress syndrome(RDS),49(47,1 %) patients with transient tachypnea of newborn(TTN),27(26 %) with pneumonia,4(3,8 %)with congenital heart diseases.Lung ultrasonography were performed 210 times for all infants,but CXRs were performed a total of only 107 times.CXR wasnot taken in 27 of the patients with a diagnosis of TTN,in 2 of the patients with a diagnosis of congenital pneumonia,and in one of the patients with congenital heart disease.The rate of patients who have never had a chest x-ray was 28,8%.Conclusions:We observed that usage of lung ultrasonography decreased the number of chest X-ray and radiation exposure in newborns with respiratory distress.

scholarly journals A rib abnormality mimicking pulmonary nodule: a pitfall in the plain chest x-ray

2016 ◽  
Vol 81 (1-2) ◽  
Author(s):  
Yeliz Akturk ◽  
Serra Ozbal Günes ◽  
Baki Hekimoglu
Keyword(s):  
Pulmonary Nodule ◽  
Lung Nodule ◽  
Middle Part ◽  
Computerised Tomography ◽  
Diagnostic Quality ◽  
X Ray ◽  
Wide Range ◽  
Plain Chest ◽  
Metastatic Lung ◽  
Chest X Ray

The ribs show a wide range of normal and pathologic radiographic appearences as well as congenital variations. Intrathoracic ribs are isolated and rare anomalies. They are usually super-numerary, more often right-sided, and involve the middle part of the thorax. We describe a case with intrathorasic rib abnormality mimicking a peripheral metastatic lung nodule in the plain chest x-ray and emphasize the use of coronal and sagittal reformatted images in thorasic imaging.  Utilisation of multiplanar reformatted images in chest computerised tomography increase diagnostic quality.

Study on Pretreatment Method of X-Ray Real-Time Imaging Digital Image

2013 ◽  
Vol 815 ◽  
pp. 854-859
Author(s):  
Peng Lin Zhang ◽  
Zhi Qiang Zhao ◽  
Peng Kong
Keyword(s):  
Digital Image ◽  
Materials Testing ◽  
Low Contrast ◽  
Geometric Transformations ◽  
X Ray ◽  
Wide Range ◽  
Automotive Parts ◽  
Image Edge ◽  
Pretreatment Technology ◽  
Scale Transformations

X-ray nondestructive testing has a wide range of applications, which in materials testing, food testing, manufacturing, instrumentation, automotive parts and other fields having good performance. The paper mainly deals with low contrast X-ray digital images, image edge blur features and digital image preprocessing techniques of contrast. By a crack image taking geometric transformations, gray-scale transformations and image enhancement processing such as pretreatment technology airspace transforms, getting three options that have been able to effectively realize image denoising and enhancement. These three sets of processing solutions, to some extened, opening the image intensity distribution and making cracks sharper image segmentation is the foundation of subsequentence.

scholarly journals Instrumentation of SAXS for the Analysis of Heterogeneous Structure in Polymer

2014 ◽  
Vol 70 (a1) ◽  
pp. C1325-C1325
Author(s):  
Hiroyasu Masunaga ◽  
Hiroki Ogawa ◽  
Akihiko Fujiwara ◽  
Masaki Takata ◽  
Atsushi Takahara ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Structure Characterization ◽  
Polymer Materials ◽  
Imaging Method ◽  
Heterogeneous Structure ◽  
X Ray ◽  
X Ray Scattering ◽  
Wide Range ◽  
The Hierarchical Structure ◽  
Ray Scattering

Polymer materials have hierarchal structure in the very wide range of scale. It is well known that the property is dependent on the hierarchical structure. In order to improve the performance of the materials, clarifying the hierarchical structure in a wide range and the feed back to the manufacturing process are important. However, it is difficult to clarify the hierarchical heterogeneous structure of polymer materials using only single method. Therefore the combination of microbeam small- and wide- angle X-ray scattering (SAXS/WAXS) is useful for evaluation of the hierarchical heterogeneous structural of polymer materials. The BL03XU, in alias, FSBL, in SPring-8 was constructed by consortium of industrial and academic groups and has been used from 20101),2). Structure characterization of advanced materials in the industrial field has been carried out using microbeam SAXS/WAXS method. In addition to the description of the SAXS/WAXS measurement system at BL03XU, we will report on the local structural evaluation of carbon fiber (CF). A hierarchal heterogeneous structure of CFs was visualized in the space resolution of 1 μm using a microbeam and an X-ray imaging technique. The image contrasts were identified by the difference in peak positions corresponding to the void size, the peak width corresponding to the crystallite size, and intensities corresponding to the amount of crystallites and voids. The X-ray scattering images of high-modulus CF are shown in a figure. Nanometer-size voids estimated by SAXS are abundant in the center of a fiber, on the other hand, the crystallite is abundant in the vicinity of a surface was revealed. It is suggested that the voids were generated near the center of the fiber to relax the strain during the crystallization process from the surface during the graphitization of fibers. We succeeded in visualizing the distribution of voids and crystallite of a few nanometers, which cannot be observed by an X-ray transmission imaging method.

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