Formation Factor for Heterogeneous Carbonate Rocks Using Multi-scale X-ray CT images

Carbonate rocks are considered to be essential reservoirs for human development, but are known to be highly heterogeneous and difficult to fully characterize. To better understand carbonate systems, studying pore-scale is needed. For this purpose, three blocks of carbonate rocks (chalk, enthrocal limestone, and dolomite) were cored into 30 samples with diameters of 18 mm and lengths of 25 mm. They were characterized from pore to core scale with laboratory tools. These techniques, coupled with X-ray micro-tomography, enable us to quantify hydrodynamic properties (porosity, permeability), elastic and structural properties (by acoustic and electrical measurements), pore distribution (by centrifugation and calculations). The three rocks have similar properties to typical homogeneous carbonate rocks but have specific characteristics depending on the rock type. In the same rock family, sample properties are different and similarities were established between certain measured properties. For example, samples with the same hydrodynamic (porosity, permeability) and structural (formation factor, electrical tortuosity) characteristics may have different elastic properties, due to their cohesion, which itself depends on pore size distributions. Microstructure is understood as one of the essential properties of a rock and thus must be taken into account to better understand the initial characteristics of rocks.

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A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19

Scientific Reports ◽

10.1038/s41598-021-97428-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Geng Hong ◽

Xiaoyan Chen ◽

Jianyong Chen ◽

Miao Zhang ◽

Yumeng Ren ◽

...

Keyword(s):

Cross Validation ◽

Ct Images ◽

Attention Mechanism ◽

X Ray ◽

Multi Scale ◽

Specificity And Sensitivity ◽

The World ◽

Tenfold Cross Validation ◽

Model Size ◽

Feature Information

AbstractCoronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on attention mechanism and depthwise separable convolution. A multi-scale gated multi-head attention mechanism is designed to extract effective feature information from the COVID-19 X-ray and CT images for classification. Moreover, the depthwise separable convolution layers are adopted as MGMADS-CNN’s backbone to reduce the model size and parameters. The LeNet-5, AlexNet, GoogLeNet, ResNet, VGGNet-16, and three MGMADS-CNN models are trained, validated and tested with tenfold cross-validation on X-ray and CT images. The results show that MGMADS-CNN with three attention layers (MGMADS-3) has achieved accuracy of 96.75% on X-ray images and 98.25% on CT images. The specificity and sensitivity are 98.06% and 96.6% on X-ray images, and 98.17% and 98.05% on CT images. The size of MGMADS-3 model is only 43.6 M bytes. In addition, the detection speed of MGMADS-3 on X-ray images and CT images are 6.09 ms and 4.23 ms for per image, respectively. It is proved that the MGMADS-3 can detect and classify COVID-19 faster with higher accuracy and efficiency.

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Quality Improvement of X-ray CT Images by Estimation of the Mechanical Misalignment in a Projection Image Using an Iterative Method

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.137.213 ◽

2017 ◽

Vol 137 (3) ◽

pp. 213-219

Author(s):

Kenji Noguchi ◽

Satoshi Takahashi ◽

Daisuke Suzuki ◽

Atsushi Teramoto

Keyword(s):

Quality Improvement ◽

Iterative Method ◽

Ct Images ◽

X Ray ◽

Projection Image

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Multi-scale Investigation of Heterogeneous Swift Heavy Ion Tracks in Stannate Pyrochlore

Journal of Materials Chemistry A ◽

10.1039/d1ta04924k ◽

2021 ◽

Author(s):

Eric O'Quinn ◽

Cameron Tracy ◽

William F. Cureton ◽

Ritesh Sachan ◽

Joerg C. Neuefeind ◽

...

Keyword(s):

Electron Microscopy ◽

Transmission Electron Microscopy ◽

Heavy Ion ◽

X Ray Diffraction ◽

X Ray ◽

Structural Modifications ◽

Multi Scale ◽

Ion Tracks ◽

Swift Heavy Ion ◽

Transmission Electron

Er2Sn2O7 pyrochlore was irradiated with swift heavy Au ions (2.2 GeV), and the induced structural modifications were systematically examined using complementary characterization techniques including transmission electron microscopy (TEM), X-ray diffraction...

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Multi-Scale Modeling of Neural Structure in X-Ray Imagery

10.1109/icip42928.2021.9506174 ◽

2021 ◽

Author(s):

Aishwarya Balwani ◽

Joseph Miano ◽

Ran Liu ◽

Lindsey Kitchell ◽

Judy A. Prasad ◽

...

Keyword(s):

Neural Structure ◽

X Ray ◽

Multi Scale ◽

Scale Modeling ◽

Multi Scale Modeling

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Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

Applied Sciences ◽

10.3390/app11094233 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4233

Author(s):

Biprodip Pal ◽

Debashis Gupta ◽

Md. Rashed-Al-Mahfuz ◽

Salem A. Alyami ◽

Mohammad Ali Moni

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Chest Radiography ◽

High Sensitivity ◽

Ct Images ◽

Classification Performance ◽

Learning Models ◽

X Ray ◽

Image Capturing ◽

Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

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Multi-Scale Studies of 3D Printed Mn–Na–W/SiO2 Catalyst for Oxidative Coupling of Methane

Catalysts ◽

10.3390/catal11030290 ◽

2021 ◽

Vol 11 (3) ◽

pp. 290

Author(s):

Tim Karsten ◽

Vesna Middelkoop ◽

Dorota Matras ◽

Antonis Vamvakeros ◽

Stephen Poulston ◽

...

Keyword(s):

Oxidative Coupling ◽

Oxidative Coupling Of Methane ◽

Operating Conditions ◽

Homogeneous Distribution ◽

Silica Support ◽

X Ray Diffraction ◽

Cross Sectional ◽

X Ray ◽

Multi Scale ◽

3D Printed

This work presents multi-scale approaches to investigate 3D printed structured Mn–Na–W/SiO2 catalysts used for the oxidative coupling of methane (OCM) reaction. The performance of the 3D printed catalysts has been compared to their conventional analogues, packed beds of pellets and powder. The physicochemical properties of the 3D printed catalysts were investigated using scanning electron microscopy, nitrogen adsorption and X-ray diffraction (XRD). Performance and durability tests of the 3D printed catalysts were conducted in the laboratory and in a miniplant under real reaction conditions. In addition, synchrotron-based X-ray diffraction computed tomography technique (XRD-CT) was employed to obtain cross sectional maps at three different positions selected within the 3D printed catalyst body during the OCM reaction. The maps revealed the evolution of catalyst active phases and silica support on spatial and temporal scales within the interiors of the 3D printed catalyst under operating conditions. These results were accompanied with SEM-EDS analysis that indicated a homogeneous distribution of the active catalyst particles across the silica support.

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Pore Structure Evaluation of Quaternary Highly Vuggy Limestone by a Combination of X-ray CT Images of Differently Sized Cores

Rock Mechanics and Rock Engineering ◽

10.1007/s00603-020-02352-4 ◽

2021 ◽

Author(s):

Kentaro Masuoka ◽

Shinji Nakaya

Keyword(s):

Pore Structure ◽

Ct Images ◽

X Ray ◽

Structure Evaluation

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THE COMPARISION OF RADIOLOGICAL DISTANCE DIFFERENCE BETWEEN THE PARS INTER-ARTICULARIS AND PEDICLE, AS A REPRODUCIBLE AND CONSISTENT GUIDE FOR PEDICULAR SCREW FIXATION TECHNIQUE, IN SPINAL LUMBAR SURGERIES.

10.36106/9713874 ◽

2021 ◽

pp. 61-63

Author(s):

Bharath. V ◽

Hemanth Kumar ◽

Ashwanth Narayan ◽

Venkatachalam .K ◽

Ashwin. VY ◽

...

Keyword(s):

Ct Images ◽

Steady Increase ◽

Lateral Border ◽

X Ray ◽

Screw Insertion ◽

Pedicular Screw ◽

Pars Interarticularis ◽

Upward Direction ◽

Anatomical Relationship ◽

Distance Difference

The Inter-Pedicular and Inter-Pars distance was measured in a plain AP radiography (X-Ray) of 150 and 75 CT images normal patients between 18- 47 years of age. The aim of the study is to measure the normal Inter-Pedicular and Inter-Pars distance. We found that by studying the anatomical relationship between the inner or medial Pedicular border and the Pars outer or lateral border, gives the Orthopaedic Surgeon a reproducible and consistent guide towards exacting a pedicular screw placing. We found that both X-Ray and CT images shows steady increase in the Ipr and Ipd from L1 to L5, there is a minimal difference from L1-L2 and marked difference seen from L3 to L5, and showing the differences in distances are more in the males, compared to females. The Means of all the groups compared also proves that there is steady raise in the diameter of the IPR and IPD from L1 to L5, where there is dramatical and signicant change in the upward direction, noted from L3 to L5. The mean difference is almost constant from L1to L2. So this study, did essentially to help, establish that, the inner medial border of pedicle, is in near relationship to, the outer lateral border of the Pars-Interarticularis, which helps in establishing the latero-medial entry point for the pedicular screw insertion in the lumbar spine.

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