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DOE OSTI2019

Orthopedic metallic hardware in routine abdomino-pelvic CT scans: occurrence and clinical significance

Purpose: To study the occurrence of orthopedic metallic hardware in routine abdomen/pelvic computed tomography (CT) scans and their impact on image quality (IQ) and diagnostic evaluation. Material and methods: In this retrospective single institution study, we analyzed 3500 consecutive abdomen/pelvis CT scans for occurrence of orthopedic metallic hardware. In the cohort of patients with metallic hardware detected on CT scans, subjective and objective IQ analysis was performed to estimate diagnostic acceptability (DA, 4-point scale), subjective noise (SN, 3-point scale), presence of artifacts (PA, 4-point scale) and objective noise. The clinical significance of metallic hardware was determined by evaluating the impact of artifacts on radiological diagnosis according to the clinical indication and disease type. Results: Orthopedic metallic hardware was encountered in 4.97% of abdomino-pelvic CT scans (n = 174/3500), and artifacts related to the hardware in the region of clinical interest were identified in 82% (n = 144/174) of scans. The overall mean DA was 2.66 (n = 174), and it was severely limited (score < 2) in 32% of cases particularly affecting patients with bilateral hip implants (92.6%, n = 25/27). The artifacts due to hardware significantly limited diagnostic evaluation in 58.6% of cases (PA score ≥ 3), and the image noise was unacceptable in 71% of cases (SN score > 2) in the region of clinical interest. Conclusion: Orthopedic metallic hardware is encountered in nearly 5% of abdomino-pelvic CT scans and causes significant image degradation limiting diagnostic evaluation in the region of clinical interest.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Laboratory Foamed-Cement-Curing Evolution Using CT Scanning: Insights From Elevated-Pressure Generation

Computed-tomography (CT) scanning has become a mainstay among scientists (Wildenschild and Sheppard 2013) because it enables nondestructive observation of material processes and formation in real time. Foamed cement is a high-strength, low-density material containing nitrogen gas, and is used to stabilize wellbore casings both onshore and offshore. In-situ foamed cement is subject to pressure changes because the slurry is pumped downhole and cures at depth. To correlate the influence of pressure to the gas-void size and the curing evolution of a foamed cement, two laboratory foamed cements were generated and CT scanned as they cured. One cement was generated following the American Petroleum Institute (API) industry standard API RP 10B-4 (2015) at atmospheric conditions using a blender, and the other cement was created using a foamed-cement generator (FCG) (resembling that of de Rozières and Ferrière 1991) to produce a sample at an elevated pressure. FCG-generated cement qualities (in the range of 20, 25, 30, 35, and 40%) were scanned at their cured state for comparison. From the CT-image time series and the single FCG scans, the volumetric void properties were characterized and compared. The time-series blender voids were an order of magnitude larger than the FCG voids, and void growth was stagnant after a curing period of 100 minutes, whereas the FCG voids gradually increased in volume after 100 minutes. The Hsü-Nadai plots (Brandon 1995) reveal that the FCG and blender voids are weakly prolate, and all FCG voids, regardless of generation quality, relax to a greater final-magnitude strain than the blender voids. Furthermore, these findings confirm that both the void size and the curing process are influenced by the pressure at which a foamed cement is generated.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗

DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗

DOE OSTI2021

Secondary porosity prediction in complex carbonate reefs using 3D CT scan image analysis and machine learning

Development and preservation of carbonate porosity and permeability are critical to characterizing reservoirs. However, secondary porosity, such as vugs and fractures, are difficult to identify and require collection of expensive wireline tools or core sampling. Wireline logs and cores have traditionally been used to identify the presence of secondary porosity but fail to quantify the contribution to total reservoir porosity. Additionally, advanced wireline logs and core are not readily available for most wells. Dual energy CT scans were collected on whole core from the A-1 Carbonate and Brown Niagaran formations drawn from six wells in northern Michigan. 3D analysis techniques were applied to identify and isolate secondary porosity features. A series of machine learning and data analytics techniques were applied to the dataset to predict secondary porosity features on basic wireline logs. The predictive model successfully predicted secondary porosity with high confidence.

02 PETROLEUM↗

DOE OSTI2020

Automated Segmentation of Porous Thermal Spray Material CT Scans with Geometric Uncertainty Estimation

Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗

NASA NTRSDate not supplied

Enhanced Quantification of 3D Woven Composites Via Fourier Analysis and Structure Tensors Applied to CT Scans

NASA's development of advanced 3D woven carbon composites, such as HEEET and 3MDCP, represents a significant leap in materials designed to endure extreme atmospheric entry conditions. However, the internal complexity of these materials introduces a variety of structures and novel defect types. Traditional characterization methods rely on labor-intensive, manual examination of X-ray CT scans, demanding considerable time and expertise. This process, while thorough, is not sustainable for large-scale analysis and defect tracking. Addressing this critical challenge, this presentation introduces an innovative suite of automated tools and analytical techniques, pioneered by the Mars Sample Return (MSR) Earth Entry System (EES) Thermal Protection System (TPS) project. These advancements offer a more efficient and accurate approach for quantifying CT data and characterizing the defects of 3D woven composites.

Magnus Haw↗

NASA NTRSDate not supplied

Quantitative Characterization of 3D Woven Composites Via Fourier Analysis and Structure Tensors Applied to CT Scans

3D Woven↗

NASA NTRSDate not supplied

CT Scanning of Commercial Cells for Space Flight Applications

Explore the source record for details and available documents.

Battery↗

DOE OSTI2019

Virtual monoenergetic reconstruction of contrast-enhanced CT scans of the abdomen and pelvis at 40 keV improves the detection of peritoneal metastatic deposits

Purpose: To evaluate the role of virtual monoenergetic imaging (VMI) in the detection of peritoneal metastatic disease in contrast-enhanced computed tomography (CT) of the abdomen and pelvis and to compare this technique to the conventional 120 kV mixed dataset. Materials and methods: Institutional review board approval was obtained with no informed consent required for this retrospective analysis. 43 consecutive patients with histopathologically confirmed peritoneal disease were scanned using a standard protocol on a 128-section dual-source, dual-energy CT system (100/140 keV). Scans were retrospectively reconstructed at VMI energy levels from 40–110 keV in 10 keV increments and were analyzed both quantitatively and qualitatively. CNR values for peritoneal metastatic deposits were recorded using region of interest (ROI) analysis at each energy level for all VMI datasets. Subjective analysis was performed by two independent fellowship-trained readers with combined experience of greater than 15 years. Qualitative parameters included diagnostic acceptability, subjective noise, and contrast resolution and confidence. Results: The contrast-to-noise ratios (CNRs) for peritoneal metastatic deposits at the different VMI energy levels were compared using a one-way ANOVA with Tukey Post Test, and the optimal CNR was observed at 40 keV (p < 0.0001). Qualitative parameters were compared using a Paired T Test. Subjective noise, diagnostic acceptability, and contrast resolution was significantly better on the conventional images, but readers reported increased confidence on VMI at 40 keV (p < 0.001). Conclusion: VMI reconstruction of contrast-enhanced dual-energy CT scans of the abdomen and pelvis at 40 keV maximizes the conspicuity of metastatic peritoneal deposits and improves radiologists’ diagnostic confidence compared with conventional CT images. We recommend using virtual monoenergetic datasets at 40 keV as a tool for improving the detection of these lesions in routine clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

CT Scanning and Geophysical Measurements of the Marcellus Formation from the Tippens 6HS Well

The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) Morgantown, West Virginia site were used to characterize core of the Marcellus Shale from a vertical well drilled in Eastern Ohio. The core is from the Tippens 6HS Well in Monroe County, Ohio and is comprised primarily of the Marcellus Shale from depths of 5550 to 5663 ft.

58 GEOSCIENCES↗

DOE OSTI2019

Investigating Machine Learning Based X-Ray Computed Tomography Reconstruction Methods to Enhance the Accuracy of CT Scans

Development of new materials and predictive capabilities of component performance hinges on the ability to accurately digitize "as-built" geometries. X-ray computed tomography (CT) offers a non-destructive method of capturing these details but current methodologies are unable to produce the required fidelity for critical component certification. This project focused on discovering the limitations of existing CT reconstruction algorithms and exploring machine learning (ML) methodologies to overcome these limitations. We found that existing CT reconstruction methods are insufficient for Sandia's critical component certification process and that ML algorithms are a viable path forward to improving the quality of CT images.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Pore-scale observations of natural hydrate-bearing sediments via pressure core sub-coring and micro-CT scanning

Abstract Both intra-pore hydrate morphology and inter-pore hydrate distribution influence the physical properties of hydrate-bearing sediments, yet there has been no pore-scale observations of hydrate habit under pressure in preserved pressure core samples so far. We present for the first time a pore-scale micro-CT study of natural hydrate-bearing cores that were acquired from Green Canyon Block 955 in UT-GOM2-1 Expedition and preserved within hydrate pressure–temperature stability conditions throughout sub-sampling and imaging processes. Measured hydrate saturation in the sub-samples, taken from units expected to have in-situ saturation of 80% or more, ranges from 3 ± 1% to 56 ± 11% as interpreted from micro-CT images. Pore-scale observations of gas hydrate in the sub-samples suggest that hydrate in silty sediments at the Gulf of Mexico is pore-invasive rather than particle displacive, and hydrate particles in these natural water-saturated samples are pore-filling with no evidence of grain-coating. Hydrate can form a connected 3D network and provide mechanical support for the sediments even without cementation. The technical breakthrough to directly visualize particle-level hydrate pore habits in natural sediments reported here sheds light on future investigations of pressure- and temperature-sensitive processes including hydrate-bearing sediments, dissolved gases, and other biochemical processes in the deep-sea environment.

58 GEOSCIENCES↗

DOE OSTI2025

CT Scans of Cores Metadata, Utqiagvik (Barrow), Alaska, 2015

Individual ice cores were collected from Barrow Environmental Observatory in Barrow, Alaska, throughout 2013 and 2014. Cores were drilled along different transects to sample polygonal features (i.e. the trough, center and rim of high, transitional and low center polygons). Most cores were drilled around 1 meter in depth and a few deep cores were drilled around 3 meters in depth. Three-dimensional images of the frozen cores were constructed using a medical X-ray computed tomography (CT) scanner. TIFF files can be uploaded to ImageJ (an open-source imaging software) to examine soil structure and soil densities within each core.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Prevalence and variability in reporting of clinically actionable incidental findings on attenuation-correction CT scans in a veteran population

Background: Myocardial perfusion imaging (MPI) often employs attenuation-correction computed tomography (CTAC) to reduce attenuation artifacts and improve specificity. While there is no specific guideline on how they should be reported, incidental noncardiac findings identified on these scans may be clinically significant. The prevalence of these findings in veterans is not currently known. In addition, variability in reporting these findings may depend on the interpreting physician’s specialty. Methods: To guide future decision-making, CTACs in veterans referred for MPI were prospectively evaluated in a quality-control project for a set of prespecified actionable incidental findings by cardiologists and a radiologist. Results: On the 771 scans performed over eight months, 285 incidental noncardiac findings were identified by the interpreting cardiologists and 378 were identified by the interpreting radiologist. Pulmonary nodules were the most common occurring in 20% of studies read by the radiologist. Interreader agreements between cardiologists and the radiologist were poor for pulmonary nodules ≥ 10 mm and hiatal hernias; fair for pulmonary nodules < 10 mm, extracardiac masses, and aortic aneurysms; and moderate for pleural plaques. Conclusion: Incidental noncardiac findings on CTACs are common in our veteran population. Overall interobserver agreement in identifying these findings between cardiologists and radiologists is fair. Specific guidelines are needed on how CTACs should be read and reported.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

CT scanning and flow measurements of shale fractures after multiple shearing events

A shearing apparatus was used in conjunction with a Hassler-style core holder to incrementally shear fractured shale cores while maintaining various confining pressures. Computed tomography scans were performed after each shearing event, and were used to obtain information on evolving fracture geometry. Fracture transmissivity was measured after each shearing event to understand the hydrodynamic response to the evolving fracture structure. The digital fracture volumes were used to perform laminar single phase flow simulations (local cubic law with a tapered plate correction model) to qualitatively examine small scale flow path variations within the altered fractures. Fractures were found to generally increase in aperture after several shear slip events, with corresponding transmissivity increases. Lower confining pressure resulted in a fracture more prone to episodic mechanical failure and sudden changes in transmissivity. Conversely, higher confining pressures resulted in a system where, after an initial setting of the fracture surfaces, changes to the fracture geometry and transmissivity occurred gradually. Flow paths within the fractures are largely controlled by the location and evolution of zero aperture locations. Lastly, a reduction in the number of primary flow pathways through the fracture, and an increase in their width, was observed during all shearing tests.

04 OIL SHALES AND TAR SANDS↗