scholarly journals When will Low-Contrast Features be Visible in a STEM X-Ray Spectrum Image?

2015 ◽  
Vol 21 (3) ◽  
pp. 706-724 ◽  
Author(s):  
Chad M. Parish
Keyword(s):  
Phase Particle ◽  
General Procedure ◽  
Second Phase ◽  
Steel Matrix ◽  
Shell Nanoparticles ◽  
Ferritic Alloy ◽  
Electron Dose ◽  
Data Set ◽  
Low Contrast ◽  
X Ray

AbstractWhen will a small or low-contrast feature, such as an embedded second-phase particle, be visible in a scanning transmission electron microscopy (STEM) X-ray map? This work illustrates a computationally inexpensive method to simulate X-ray maps and spectrum images (SIs), based upon the equations of X-ray generation and detection. To particularize the general procedure, an example of nanostructured ferritic alloy (NFA) containing nm-sized Y2Ti2O7 embedded precipitates in ferritic stainless steel matrix is chosen. The proposed model produces physically appearing simulated SI data sets, which can either be reduced to X-ray dot maps or analyzed via multivariate statistical analysis. Comparison to NFA X-ray maps acquired using three different STEM instruments match the generated simulations quite well, despite the large number of simplifying assumptions used. A figure of merit of electron dose multiplied by X-ray collection solid angle is proposed to compare feature detectability from one data set (simulated or experimental) to another. The proposed method can scope experiments that are feasible under specific analysis conditions on a given microscope. Future applications, such as spallation proton–neutron irradiations, core-shell nanoparticles, or dopants in polycrystalline photovoltaic solar cells, are proposed.

Simulation of Damage Percolation Within Aluminum Alloy Sheet

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
O. S. Orlov ◽  
M. J. Worswick ◽  
E. Maire ◽  
D. J. Lloyd
Keyword(s):  
Aluminum Alloy ◽  
Void Nucleation ◽  
Phase Particle ◽  
Three Dimensional ◽  
Alloy Sheet ◽  
Void Coalescence ◽  
Second Phase ◽  
X Ray ◽  
Damage Initiation

A combined experimental and analytical approach is used to study damage initiation and evolution in three-dimensional second phase particle fields. A three-dimensional formulation of a damage percolation model is developed to predict damage nucleation and propagation through random-clustered second phase particle fields. The proposed approach is capable of capturing the three-dimensional character of damage phenomena and the three stages of ductile fracture, namely, void nucleation, growth, and coalescence, at the level of discrete particles. An in situ tensile test with X-ray tomography is utilized to quantify material damage during deformation in terms of the number of nucleated voids and porosity. The results of this experiment are used for both the development of a clustering-sensitive nucleation criterion and the validation of the damage percolation predictions. The evolution of damage in aluminum alloy AA5182 has been successfully predicted to match that in the in situ tensile specimen. Two forms of second phase particle field input data were considered: (1) that measured directly with X-ray tomography and (2) fields reconstructed statistically from two-dimensional orthogonal sections. It is demonstrated that the adoption of a cluster-sensitive void nucleation criterion, as opposed to a cluster-insensitive nucleation criterion, has a significant effect in promoting predicted void nucleation to occur within particle clusters. This behavior leads to confinement of void coalescence to within clusters for most of the duration of deformation followed by later development of a macrocrack through intracluster coalescence. The measured and reconstructed second phase particle fields lead to similar rates of predicted damage accumulation and can be used interchangeably in damage percolation simulations.

Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

1982 ◽  
Vol 40 ◽  
pp. 74-75
Author(s):  
Jules S. Jaffe ◽  
Robert M. Glaeser
Keyword(s):  
Purple Membrane ◽  
Data Set ◽  
High Resolution Data ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fourier Techniques ◽  
Versus Protein ◽  
The Difference ◽  
Difference Fourier ◽  
Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

Estimation of the noise in TEM image recorded on “imaging plate”

1987 ◽  
Vol 45 ◽  
pp. 748-749
Author(s):  
T. Oikawa ◽  
N. Mori ◽  
T. Katoh ◽  
Y. Harada ◽  
J. Miyahara ◽  
...  
Keyword(s):  
Low Dose ◽  
Quantum Noise ◽  
Laser Light ◽  
Imaging Plate ◽  
Total System ◽  
Electron Dose ◽  
X Ray ◽  
Image Recording ◽  
Recording Material ◽  
Highly Sensitive

The “Imaging Plate”(IP) is a highly sensitive image recording plate for X-ray radiography. It has been ascertained that the IP has superior properties and high practicability as an image recording material in a TEM. The sensitivity, one of the properties, is about 3 orders higher than that of conventional photo film. The IP is expected to be applied to low dose techniques. In this paper, an estimation of the quantum noise on the TEM image which appears in case of low electron dose on the IP is reported.In this experiment, the JEM-2000FX TEM and an IP having the same size as photo film were used.Figure 1 shows the schematic diagram of the total system including the TEM used in this experiment. In the reader, He-Ne laser light is scanned across the IP, then blue light is emitted from the IP.

Analytical Electron Microscopy Study of Second-Phase Particles in 7075 and 7475 Aluminum Alloys

1985 ◽  
Vol 43 ◽  
pp. 348-349
Author(s):  
M. Raghavan ◽  
J. Y. Koo ◽  
J. W. Steeds ◽  
B. K. Park
Keyword(s):  
Aluminum Alloys ◽  
Silicon Oxide ◽  
Analytical Electron Microscopy ◽  
Microscopy Study ◽  
Electron Microscopy Study ◽  
Partial Substitution ◽  
Second Phase ◽  
X Ray ◽  
Second Phase Particles ◽  
Almost All

X-ray microanalysis and Convergent Beam Electron Diffraction (CBD) studies were conducted to characterize the second phase particles in two commercial aluminum alloys -- 7075 and 7475. The second phase particles studied were large (approximately 2-5μm) constituent phases and relatively fine ( ∼ 0.05-1μn) dispersoid particles, Figures 1A and B. Based on the crystal structure and chemical composition analyses, the constituent phases found in these alloys were identified to be Al7Cu2Fe, (Al,Cu)6(Fe,Cu), α-Al12Fe3Si, Mg2Si, amorphous silicon oxide and the modified 6Fe compounds, in decreasing order of abundance. The results of quantitative X-ray microanalysis of all the constituent phases are listed in Table I. The data show that, in almost all the phases, partial substitution of alloying elements occurred resulting in small deviations from the published stoichiometric compositions of the binary and ternary compounds.

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

A synchrotron X-ray study of the electron density in C-type rare earth oxides

1996 ◽  
Vol 52 (3) ◽  
pp. 414-422 ◽  
Author(s):  
E. N. Maslen ◽  
V. A. Streltsov ◽  
N. Ishizawa
Keyword(s):  
Heavy Metal ◽  
Rare Earth ◽  
Electron Density ◽  
Approximate Symmetry ◽  
Data Set ◽  
X Ray ◽  
Metal Atoms ◽  
Structure Factors ◽  
Deformation Electron Density ◽  
Synthetic Crystals

Structure factors for small synthetic crystals of the C-type rare earth (RE) sesquioxides Y2O3, Dy2O3 and Ho2O3 were measured with focused λ = 0.7000 (2) Å, synchrotron X-radiation, and for Ho2O3 were re-measured with an MoKα (λ = 0.71073 Å) source. Approximate symmetry in the deformation electron density (Δρ) around a RE atom with pseudo-octahedral O coordination matches the cation geometry. Interactions between heavy metal atoms have a pronounced effect on the Δρ map. The electron-density symmetry around a second RE atom is also perturbed significantly by cation–anion interactions. The compounds magnetic properties reflect this complexity. Space group Ia{\bar 3}, cubic, Z = 16, T = 293 K: Y2O3, Mr = 225.82, a = 10.5981 (7) Å, V = 1190.4 (2) Å3, Dx = 5.040 Mg m−3, μ 0.7 = 37.01 mm−1, F(000) = 1632, R = 0.067, wR = 0.067, S = 9.0 (2) for 1098 unique reflections; Dy2O3, Mr = 373.00, a = 10.6706 (7) Å, V = 1215.0 (2) Å3, Dx = 8.156 Mg m−3, μ 0.7 = 44.84 mm−1, F(000) = 2496, R = 0.056, wR = 0.051, S = 7.5 (2) for 1113 unique reflections; Ho2O3, Mr = 377.86, a = 10.606 (2) Å, V = 1193.0 (7) Å3, Dx = 8.415 Mg m−3, μ 0.7 = 48.51 mm−1 F(000) = 2528, R = 0.072, wR = 0.045, S = 9.2 (2) for 1098 unique reflections of the synchrotron data set.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

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