Saturated water conductivity estimation based on X-ray CT images – evaluation of the impact of thresholding errors

X-ray computational tomography (CT) is becoming more and more popular research tool in geosciences. Estimation of the saturated conductivity of the porous media based on X-ray CT images is an example of its application.&#160;In case of simulations for the pore media, which are approximated by the very complicated meshes, problems might arise when mesh does not follow the shape of pore-space ideally, which may happen due to limitations imposed (e.g. due to some technical constraints) on minimum mesh cell size which usually is bigger than CT scan resolution used for determination of the pore space. If this is the case, the mesh can&#8217;t be generated properly in the narrow regions of the pore-space.The work tries to quantify the impact of the limited mesh quality on estimation of the saturated conductivity coefficient. Four mesh generation parameters, resulting in different sizes of the minimum mesh cell size, were compared. For comparison five different pore media (three sandpacks prepared from different sand fractions and two types of sandstones) were used, all of them were used in two repetitions which resulted in 10 studied samples in total. First samples were X-ray CT scanned with resolution 2um. Than images were thresholded to obtain information about pore-space. In the next step, for all of 10 3D images of pore-space, mesh was generated in four repetitions differing with minimum mesh cell size: 2.56, 3.41, 5.12 and 10.25 times greater than voxel size used for CT scanning.Saturated conductivity was simulated based on prepared meshes using finite volume based solver of the Navier-Stokes equations. Estimated for each sample saturated conductivity differed from 12% for coarse media to 200% for fine grain media for different numerical meshes representing with different accuracy pore space geometry.Based on samples studied, one may conclude that for optimal results of saturated conductivity numerical estimation, the smallest numerical mesh&#8217;s cell size should be of the level of pore media CT scan resolution. &#160;&#160;Acknowledgments:This work was partially supported by a grant from the Polish National Centre for Research and Development within the contract no.: PL-TW/IV/5/2017.

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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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Analyzing the Impact of X-Ray Tomography on the Reliability of Integrated Circuits

ISTFA 2015: Conference Proceedings from the 41st International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2015p0154 ◽

2015 ◽

Author(s):

Halit Dogan ◽

Md Mahbub Alam ◽

Navid Asadizanjani ◽

Sina Shahbazmohamadi ◽

Domenic Forte ◽

...

Keyword(s):

Integrated Circuits ◽

Integrated Circuit ◽

3D Imaging ◽

Three Dimensional ◽

Serial Sectioning ◽

Promising Technique ◽

Flash Memories ◽

X Ray ◽

3D Information ◽

The Impact

Abstract X-ray tomography is a promising technique that can provide micron level, internal structure, and three dimensional (3D) information of an integrated circuit (IC) component without the need for serial sectioning or decapsulation. This is especially useful for counterfeit IC detection as demonstrated by recent work. Although the components remain physically intact during tomography, the effect of radiation on the electrical functionality is not yet fully investigated. In this paper we analyze the impact of X-ray tomography on the reliability of ICs with different fabrication technologies. We perform a 3D imaging using an advanced X-ray machine on Intel flash memories, Macronix flash memories, Xilinx Spartan 3 and Spartan 6 FPGAs. Electrical functionalities are then tested in a systematic procedure after each round of tomography to estimate the impact of X-ray on Flash erase time, read margin, and program operation, and the frequencies of ring oscillators in the FPGAs. A major finding is that erase times for flash memories of older technology are significantly degraded when exposed to tomography, eventually resulting in failure. However, the flash and Xilinx FPGAs of newer technologies seem less sensitive to tomography, as only minor degradations are observed. Further, we did not identify permanent failures for any chips in the time needed to perform tomography for counterfeit detection (approximately 2 hours).

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The role of point-of-care ultrasound in the diagnosis of pericardial effusion: a single academic center retrospective study

The Ultrasound Journal ◽

10.1186/s13089-021-00205-x ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Matthew G. Hanson ◽

Barry Chan

Keyword(s):

Pericardial Effusion ◽

Point Of Care ◽

Physical Exam ◽

Right Atrial ◽

Point Of Care Ultrasound ◽

X Ray ◽

Average Systolic Blood Pressure ◽

Time To Diagnosis ◽

Clinically Significant ◽

The Impact

Abstract Background Symptomatic pericardial effusion (PCE) presents with non-specific features and are often missed on the initial physical exam, chest X-ray (CXR), and electrocardiogram (ECG). In extreme cases, misdiagnosis can evolve into decompensated cardiac tamponade, a life-threatening obstructive shock. The purpose of this study is to evaluate the impact of point-of-care ultrasound (POCUS) on the diagnosis and therapeutic intervention of clinically significant PCE. Methods In a retrospective chart review, we looked at all patients between 2002 and 2018 at a major Canadian academic hospital who had a pericardiocentesis for clinically significant PCE. We extracted the rate of presenting complaints, physical exam findings, X-ray findings, ECG findings, time-to-diagnosis, and time-to-pericardiocentesis and how these were impacted by POCUS. Results The most common presenting symptom was dyspnea (64%) and the average systolic blood pressure (SBP) was 120 mmHg. 86% of people presenting had an effusion > 1 cm, and 89% were circumferential on departmental echocardiogram (ECHO) with 64% having evidence of right atrial systolic collapse and 58% with early diastolic right ventricular collapse. The average time-to-diagnosis with POCUS was 5.9 h compared to > 12 h with other imaging including departmental ECHO. Those who had the PCE identified by POCUS had an average time-to-pericardiocentesis of 28.1 h compared to > 48 h with other diagnostic modalities. Conclusion POCUS expedites the diagnosis of symptomatic PCE given its non-specific clinical findings which, in turn, may accelerate the time-to-intervention.

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X-ray Shielding, Mechanical, Physical, and Water Absorption Properties of Wood/PVC Composites Containing Bismuth Oxide

Polymers ◽

10.3390/polym13132212 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2212

Author(s):

Worawat Poltabtim ◽

Ekachai Wimolmala ◽

Teerasak Markpin ◽

Narongrit Sombatsompop ◽

Vichai Rosarpitak ◽

...

Keyword(s):

Impact Strength ◽

Water Absorption ◽

Attenuation Coefficient ◽

Bismuth Oxide ◽

Morphological Properties ◽

Linear Attenuation Coefficient ◽

X Rays ◽

Equivalent Thickness ◽

X Ray ◽

The Impact

The potential utilization of wood/polyvinyl chloride (WPVC) composites containing an X-ray protective filler, namely bismuth oxide (Bi2O3) particles, was investigated as novel, safe, and environmentally friendly X-ray shielding materials. The wood and Bi2O3 contents used in this work varied from 20 to 40 parts per hundred parts of PVC by weight (pph) and from 0 to 25, 50, 75, and 100 pph, respectively. The study considered X-ray shielding, mechanical, density, water absorption, and morphological properties. The results showed that the overall X-ray shielding parameters, namely the linear attenuation coefficient (µ), mass attenuation coefficient (µm), and lead equivalent thickness (Pbeq), of the WPVC composites increased with increasing Bi2O3 contents but slightly decreased at higher wood contents (40 pph). Furthermore, comparative Pbeq values between the wood/PVC composites and similar commercial X-ray shielding boards indicated that the recommended Bi2O3 contents for the 20 pph (40 ph) wood/PVC composites were 35, 85, and 40 pph (40, 100, and 45 pph) for the attenuation of 60, 100, and 150-kV X-rays, respectively. In addition, the increased Bi2O3 contents in the WPVC composites enhanced the Izod impact strength, hardness (Shore D), and density, but reduced water absorption. On the other hand, the increased wood contents increased the impact strength, hardness (Shore D), and water absorption but lowered the density of the composites. The overall results suggested that the developed WPVC composites had great potential to be used as effective X-ray shielding materials with Bi2O3 acting as a suitable X-ray protective filler.

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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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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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The impact of point defects in n-type GaN layers on thermal decomposition of InGaN/GaN QWs

Scientific Reports ◽

10.1038/s41598-021-81017-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mikolaj Grabowski ◽

Ewa Grzanka ◽

Szymon Grzanka ◽

Artur Lachowski ◽

Julita Smalc-Koziorowska ◽

...

Keyword(s):

Quantum Wells ◽

High Temperatures ◽

Point Defects ◽

X Ray Diffraction ◽

Helium Ions ◽

X Ray ◽

Sapphire Substrates ◽

Transmission Electron ◽

The Impact ◽

Ingan Quantum Wells

AbstractThe aim of this paper is to give an experimental evidence that point defects (most probably gallium vacancies) induce decomposition of InGaN quantum wells (QWs) at high temperatures. In the experiment performed, we implanted GaN:Si/sapphire substrates with helium ions in order to introduce a high density of point defects. Then, we grew InGaN QWs on such substrates at temperature of 730 °C, what caused elimination of most (but not all) of the implantation-induced point defects expanding the crystal lattice. The InGaN QWs were almost identical to those grown on unimplanted GaN substrates. In the next step of the experiment, we annealed samples grown on unimplanted and implanted GaN at temperatures of 900 °C, 920 °C and 940 °C for half an hour. The samples were examined using Photoluminescence, X-ray Diffraction and Transmission Electron Microscopy. We found out that the decomposition of InGaN QWs started at lower temperatures for the samples grown on the implanted GaN substrates what provides a strong experimental support that point defects play important role in InGaN decomposition at high temperatures.

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Deep Dive Into the Effects of Food Processing on Limiting Starch Digestibility and Lowering the Glycemic Response

Nutrients ◽

10.3390/nu13020381 ◽

2021 ◽

Vol 13 (2) ◽

pp. 381

Author(s):

Gautier Cesbron-Lavau ◽

Aurélie Goux ◽

Fiona Atkinson ◽

Alexandra Meynier ◽

Sophie Vinoy

Keyword(s):

Food Processing ◽

Imaging Techniques ◽

Food Products ◽

International Standards ◽

Starch Digestibility ◽

Deep Dive ◽

Processing Technologies ◽

X Ray ◽

The Impact

During processing of cereal-based food products, starch undergoes dramatic changes. The objective of this work was to evaluate the impact of food processing on the starch digestibility profile of cereal-based foods using advanced imaging techniques, and to determine the effect of preserving starch in its native, slowly digestible form on its in vivo metabolic fate. Four different food products using different processing technologies were evaluated: extruded products, rusks, soft-baked cakes, and rotary-molded biscuits. Imaging techniques (X-ray diffraction, micro-X-ray microtomography, and electronic microscopy) were used to investigate changes in slowly digestible starch (SDS) structure that occurred during these different food processing technologies. For in vivo evaluation, International Standards for glycemic index (GI) methodology were applied on 12 healthy subjects. Rotary molding preserved starch in its intact form and resulted in the highest SDS content (28 g/100 g) and a significantly lower glycemic and insulinemic response, while the three other technologies resulted in SDS contents below 3 g/100 g. These low SDS values were due to greater disruption of the starch structure, which translated to a shift from a crystalline structure to an amorphous one. Modulation of postprandial glycemia, through starch digestibility modulation, is a meaningful target for the prevention of metabolic diseases.

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