High-Quality MicroCT Rock Imaging: Methodology To Measure and Correct for X-Ray Scatter

SPE Journal ◽  
2019 ◽  
Vol 25 (01) ◽  
pp. 226-241
Author(s):  
Alexander Katsevich ◽  
Michael Frenkel ◽  
Qiushi Sun ◽  
Shannon L. Eichmann ◽  
Victor Prieto
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Scatter Correction ◽  
The United States ◽  
Accurate Estimation ◽  
High Quality ◽  
X Ray ◽  
The Core ◽  
Ct Data ◽  
The Impact

Summary Microcomputed tomography (microCT) of cores yields valuable information about rock and fluid properties at pore scale for conventional rock and at rock heterogeneity scale for unconventionals. High levels of uncorrected X-ray scatter in computed tomography (CT) data lead to strong image artifacts and erroneous Hounsfield unit (HU) values, making reconstructed images unsuitable for accurate digital rock (DR) characterization (e.g., segmentation, material decomposition, and others). MicroCT scanners do not include scatter correction techniques. To fill this gap, we developed a new methodology to measure and remove the scatter component from raw projection microCT data collected during rock core scans, and ultimately improving the image quality of scanned cores. Widely used approaches for scatter estimation, based on Monte Carlo (MC) simulations and simplified analytical models, are time-consuming and may lose accuracy when imaging complex unconventional shale cores. In this paper, we propose a more practical approach to perform scatter correction from direct scatter measurements, an approach that is based on the beam-stop array (BSA) method. The BSA method works as follows: The radiation scattered by the core sample is emitted in random directions. By placing an array of small, highly absorbing beads between the source and the core, the primary X-ray signal through the beads is blocked, but the overall object scatter signal is not affected. The observed values in the beads’ shadows on the detector are assumed to be scatter signal. Performing interpolation of the scatter signal between the shadowed by beads pixels on the detector gives an estimate of the scatter signal at every pixel on the detector. Subtracting scatter from projection data yields scatter-corrected data used for 3D CT core image reconstruction. To develop the core scatter correction methodology, we executed the following three tasks: (1) performed modeling of primary and scattered signals to optimize the BSA design (beads size and layout) and scan parameters; (2) developed and implemented an accurate scatter correction algorithm into our 3D microCT image reconstruction workflow; and (3) tested the proposed methodology using four shale core samples from the United States and the Middle East. To better assess the impact of scatter, all experiments with shale core plugs presented in this paper were conducted using source energy of 160 kVp. Our results demonstrated that in many cases, especially with higher attenuating cores, failing to correct for X-ray scatter may result in significant loss of image reconstruction accuracy. We also showed that the developed methodology allows for accurate estimation and removal of scatter from the raw (projection) CT data, enabling reconstruction of high-quality core images that are required for performing DR analysis. To assess the impact of X-ray scatter on the accuracy of DR segmentation, we compared the amount of resolved air-filled space using a stack of image slices by thresholding for the air regions. Our results showed that the amount of detected air-filled space may increase significantly when scatter correction is applied. The presented scatter correction methodology is general and can be used with any microCT scanner used by the petroleum industry to improve image quality and derive accurate HU values. This is of significant importance for quantitative characterization of highly heterogeneous rock with fine structural changes, as is the case for shale. Ultimately, this methodology should expand the operational envelope and value of microCT imaging in the exploration and production workflows.

Image Quality Analysis of X-ray Grating Interferometer Using Integrating-bucket Method

2020 ◽  
Vol 64 (2) ◽  
pp. 20503-1-20503-5
Author(s):  
Faiz Wali ◽  
Shenghao Wang ◽  
Ji Li ◽  
Jianheng Huang ◽  
Yaohu Lei ◽  
...  
Keyword(s):  
Image Quality ◽  
Phase Contrast ◽  
Quality Analysis ◽  
Phase Contrast Imaging ◽  
Phase Image ◽  
Contrast Imaging ◽  
High Quality ◽  
Image Quality Analysis ◽  
X Ray ◽  
Grating Interferometer

Abstract Grating-based x-ray phase-contrast imaging has the potential to enhance image quality and provide inner structure details non-destructively. In this work, using grating-based x-ray phase-contrast imaging system and employing integrating-bucket method, the quantitative expressions of signal-to-noise ratios due to photon statistics and mechanical error are analyzed in detail. Photon statistical noise and mechanical error are the main sources affecting the image noise in x-ray grating interferometry. Integrating-bucket method is a new phase extraction method translated to x-ray grating interferometry; hence, its image quality analysis would be of great importance to get high-quality phase image. The authors’ conclusions provide an alternate method to get high-quality refraction signal using grating interferometer, and hence increases applicability of grating interferometry in preclinical and clinical usage.

scholarly journals A High Rigidity and Precision Scanning Tunneling Microscope with Decoupled XY and Z Scans

Scanning ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Xu Chen ◽  
Tengfei Guo ◽  
Yubin Hou ◽  
Jing Zhang ◽  
Wenjie Meng ◽  
...  
Keyword(s):  
Scanning Tunneling Microscope ◽  
Atomic Resolution ◽  
Scanning Tunneling ◽  
Electronic Noise ◽  
High Quality ◽  
Tunneling Microscope ◽  
The Core ◽  
High Rigidity ◽  
The Common ◽  
The Impact

A new scan-head structure for the scanning tunneling microscope (STM) is proposed, featuring high scan precision and rigidity. The core structure consists of a piezoelectric tube scanner of quadrant type (for XY scans) coaxially housed in a piezoelectric tube with single inner and outer electrodes (for Z scan). They are fixed at one end (called common end). A hollow tantalum shaft is coaxially housed in the XY-scan tube and they are mutually fixed at both ends. When the XY scanner scans, its free end will bring the shaft to scan and the tip which is coaxially inserted in the shaft at the common end will scan a smaller area if the tip protrudes short enough from the common end. The decoupled XY and Z scans are desired for less image distortion and the mechanically reduced scan range has the superiority of reducing the impact of the background electronic noise on the scanner and enhancing the tip positioning precision. High quality atomic resolution images are also shown.

Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

2021 ◽  
Author(s):  
Khalid Labib Alsamadony ◽  
Ertugrul Umut Yildirim ◽  
Guenther Glatz ◽  
Umair bin Waheed ◽  
Sherif M. Hanafy
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Transfer Learning ◽  
Temporal Resolution ◽  
Exposure Time ◽  
Ct Images ◽  
Loss Functions ◽  
High Quality ◽  
X Ray ◽  
Training Images

Abstract Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

scholarly journals A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 944 ◽  
Author(s):  
Heesin Lee ◽  
Joonwhoan Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Imaging System ◽  
Scatter Correction ◽  
Similarity Index ◽  
Real Data ◽  
Correction Method ◽  
Training Data ◽  
X Ray ◽  
Scatter Reduction

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.

scholarly journals Dietary and herbal supplements for weight loss: assessing the quality of patient information online

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jeremy Y. Ng ◽  
Saad Ahmed ◽  
Catherine Jiayi Zhang
Keyword(s):  
Weight Loss ◽  
Health Information ◽  
Healthcare Providers ◽  
The United States ◽  
Consumer Health Information ◽  
Consumer Health ◽  
High Quality ◽  
The Mean ◽  
The Impact

Abstract Background Given the high prevalence of dietary and herbal supplement (DHS) use in tandem with the growing ease of internet access, patients commonly search online for consumer health information about these products. One common reason for DHSs use includes weight loss. Healthcare providers need to be aware of the quality of online information about DHSs for weight loss so they can adequately counsel their patients and provide them with guidance surrounding the identification of high-quality information resources. This study aimed to assess the quality of online DHSs consumer health information for weight loss that a “typical” patient might access online. Methods Six search terms were used to generate the first 20 websites on the Google search engine in four countries: Australia, Canada, the United Kingdom, and the United States (n = 480 websites). After applying exclusion criteria, eligible websites were quality assessed using the DISCERN instrument. This tool is comprised of 16 questions, each evaluated on a 5-point scale. The averages and standard deviations for each DISCERN instrument item, in addition to overall summed scores between 15 and 75 were calculated. Results Across 87 eligible websites, the mean summed score was 44.80 (SD = 11.53), while the mean overall DISCERN score of each website was 2.72 (SD = 0.99). In general, websites detailed and achieved their specified aims and described treatment benefits. However, most websites failed to describe the impact of treatment on overall quality of life and the impact of a no treatment option. The highest-scoring websites were largely government or health portal websites, while the lowest-scoring websites were largely commercial in nature. Conclusion High variability in DISCERN instrument scores was found across all websites assessed. Healthcare providers should be aware of the fact that their patients may be accessing misinformation online surrounding the use of DHSs for weight loss. Therefore, it is important for healthcare providers to ensure that they are providing their patients with guidance on how to identify high-quality resources online, in order that safe, effective, and evidence-based decisions are made surrounding the use of DHSs for weight loss.

scholarly journals The Impact of Center-Based Childcare Attendance on Early Child Development: Evidence From the French Elfe Cohort

Demography ◽  
2021 ◽  
Author(s):  
Lawrence M. Berger ◽  
Lidia Panico ◽  
Anne Solaz
Keyword(s):  
Child Development ◽  
Motor Skills ◽  
Negative Impact ◽  
Positive Impact ◽  
Language Skills ◽  
The United States ◽  
Small Scale ◽  
High Quality ◽  
Disadvantaged Children ◽  
The Impact

Abstract Proponents of early childhood education and care programs cite evidence that high-quality center-based childcare has positive impacts on child development, particularly for disadvantaged children. However, much of this evidence stems from randomized evaluations of small-scale intensive programs based in the United States and other Anglo/English-speaking countries. Evidence is more mixed with respect to widespread or universal center-based childcare provision. In addition, most evidence is based on childcare experiences of 3- to 5-year-old children; less is known about the impact of center-based care in earlier childhood. The French context is particularly suited to such interrogation because the majority of French children who attend center-based care do so in high-quality, state-funded, state-regulated centers, known as crèches, and before age 3. We use data from a large, nationally representative French birth cohort, the Étude Longitudinale Français depuis l'Enfance (Elfe), and an instrumental variables strategy that leverages exogenous variation in both birth quarter and local crèche supply to estimate whether crèche attendance at age 1 has an impact on language, motor skills, and child behavior at age 2. Results indicate that crèche attendance has a positive impact on language skills, no impact on motor skills, and a negative impact on behavior. Moreover, the positive impact on language skills is particularly concentrated among disadvantaged children. This implies that facilitating increased crèche access among disadvantaged families may hold potential for decreasing early socioeconomic disparities in language development and, given the importance of early development for later-life outcomes, thereby have an impact on long-term population inequalities.

scholarly journals The Impact of COVID-19 Across Nursing Homes That Disproportionally Serve Minority Residents

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 241-241
Author(s):  
Amit Kumar ◽  
Indrakshi Roy ◽  
Amol Karmarkar ◽  
Kimberly Erler ◽  
James Rudolph ◽  
...  
Keyword(s):  
Older Adults ◽  
Nursing Homes ◽  
The United States ◽  
Structural Racism ◽  
High Quality ◽  
Total Death ◽  
The Life Course ◽  
The Impact ◽  
Related Mortality

Abstract The Coronavirus-2019 (COVID-19) pandemic has disproportionally affected communities of color and older adults in the United States. Nursing homes (NHs) have reported over 130,000 COVID-19 deaths (or one-fourth of all US deaths) circa March 2021, a high share of the nation’s total death count (CMS COVID-19 NH Data). These inequities partially driven by barriers to care, segregation and structural racism have resulted in the unequal impact of COVID-19 across NHs (Li et al., 2020). In this presentation, I will describe NHs that disproportionally care for minority residents and the effect of NH composition on COVID-19-related mortality and outcomes. In 2020, minority older adults were less likely to have access to high quality facilities. From June – August, NHs with a high proportion of minority residents reported higher COVID-19 mortality rates per 1000 residents. Equal access to high quality of care across the life-course among racial and ethnic groups is needed.

Universal Pre-Kindergarten: Supporting High Quality and Broad Access at a Time of Federal Disengagement and “School Choice”

2019 ◽  
Author(s):  
Natalie Gomez-Velez
Keyword(s):  
School Choice ◽  
Federal Policy ◽  
Universal Access ◽  
State Level ◽  
The United States ◽  
Local Policy ◽  
Policy Goal ◽  
High Quality ◽  
State And Local ◽  
The Impact

At the turn of the twenty-first century, public universal pre-kindergarten (UPK) gained momentum across the United States as a widely popular public policy goal. More recently, however, implementing high-quality UPK has been hampered by federal disengagement from the issue, fiscal constraints, and conflicting state policy approaches. This chapter examines the growth of UPK as an important policy goal on the federal and state levels; the impact of the Every Student Succeeds Act (ESSA) and a new presidential administration on federal policy disengagement from the issue; and varying degrees of state fiscal support and state and local policy approaches in thwarting the full realization of UPK’s promise. This examination notes the urgent need for a policy focus on the equitable delivery of high-quality programs if UPK is to improve future educational outcomes and help close opportunity gaps. Universal access will not achieve pre-kindergarten’s benefits without high-quality programming. If universal pre-kindergarten’s promise is to be achieved, the federal and state governments must work in tandem to ensure that all children, starting with those most in need of early education, have access to high-quality, age-appropriate UPK. Because ESSA limits federal policy prescriptions, advocacy must focus on the state level, using evidence-based lessons from research and experience.

Deep Chandra observations of the core of the Perseus cluster

2018 ◽  
Vol 14 (S342) ◽  
pp. 127-132
Author(s):  
Jeremy S. Sanders
Keyword(s):  
Spatial Scales ◽  
High Quality ◽  
X Ray ◽  
The Core ◽  
Spatially Resolved ◽  
Spatially Resolved Spectroscopy ◽  
Perseus Cluster ◽  
Metallicity Distribution

AbstractThe Perseus cluster is the X-ray brightest cluster in the sky and with deep Chandra observations we are able to map its central structure on very short spatial scales. In addition, the high quality of X-ray data allows detailed spatially-resolved spectroscopy. In this paper I review what these deep observations have told us about AGN feedback in clusters, sloshing and instabilities, and the metallicity distribution.

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