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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data Mining – Image Analysis of Radiography for Zr Redistribution

The research effort described in this report represents a first attempt to investigate the radial redistribution of Zr in ternary fuel alloys U-xPu-10Zr (x = 0, 8, 19) irradiated in the in-reactor fuel experiments during the operation of the U.S. Department of Energy’s (DOE’s) Experimental Breeder Reactor II (EBR-II) at Idaho National Laboratory (INL) using methods of image processing on post-irradiation neutron radiographs. Approximately 130,000 metal fuel pins were irradiated in EBR-II during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed historically now allowing application of modern image analysis techniques to characterize fuel behavior, such as fuel swelling, fluff formation, and now, fuel alloy constituent redistribution. The redistribution of fuel components depends on the temperature field, radially, within the fuel. Specifically, Zr is expected to redistribute radially towards the center of the pin, as well as towards the outer zones. Currently, direct imaging of a pin cross-section through optical methods or scanning electron microscopy (SEM) is used to study the constituents’ redistribution, which is very time-consuming and can only be applied to a limited number of pins. An automated image processing technique allowing for the investigation of fuel radial redistribution zones would significantly accelerate data analysis. In general, if the fuel temperature is hot enough, three redistribution zones are expected, corresponding to the main fuel components (e.g., U, Pu, Zr). While more assessment will be performed in fiscal year (FY)-2023, it seems possible to differentiate the pins according to their fuel composition using image analysis techniques on neutron radiographs of metallic fuel pins based on the analysis to date.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

Image Annotation and Database Mining to Create a Novel Screen for the Chemotype-Dependent Crystallization of HCV NS3 Protease

An effective process for screening, imaging, and optimizing crystallization trials using a combination of external and internal hardware and software has been deployed. The combination of this infrastructure with a vast annotated crystallization database enables the creation of custom crystallization screening strategies. Because of the strong chemotype-dependent crystallization observed with HCV NS3 protease (HCVPr), this strategy was applied to a chemotype resistant to all prior crystallization efforts. The crystallization database was mined for ingredients used to generate earlier HCVPr/inhibitor co-crystals. A random screen was created from the most prolific ingredients. A previously untested combination of proven ingredients was identified that led to a successful crystallization condition for the resistant chemotype.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Hubble Asteroid Hunter

Context: The Hubble Space Telescope (HST) archives constitute a rich dataset of high-resolution images to mine for strong gravitational lenses. While many HST programmes specifically target strong lenses, they can also be present by coincidence in other HST observations. Aims: Our aim is to identify non-targeted strong gravitational lenses, without any prior selection on the lens properties, in almost two decades of images from the ESA HST archive (eHST). Methods: We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, along with asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. Results: We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. A total of 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high-resolution HST imaging is ideal to follow up with spectroscopy for lens modelling and scientific analyses. Conclusions: This paper presents the unbiased search of lenses that enabled us to find a wide variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA’s Euclid mission or in James Webb Space Telescope (JWST) archival images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Percutaneous Management of Accidentally Retained Foreign Bodies During Image-Guided Non-vascular Procedures: Novel Technique Using a Large-Bore Biopsy System

ObjectiveTo describe a novel percutaneous image-guided technique using a large-bore biopsy system to retrieve foreign bodies (FBs) accidentally retained during non-vascular interventional procedures.Materials and MethodsBetween May 2013 and October 2015, five patients underwent percutaneous retrieval of five iatrogenic FBs, including a biopsy needle tip in the femoral head following osteoblastoma biopsy and radiofrequency ablation (RFA); a co-axial needle shaft within a giant desmoid tumour following cryoablation; and three post-vertebroplasty cement tails within paraspinal muscles. All FBs were retrieved immediately following original procedures under local or general anaesthesia, using combined computed tomography (CT) and fluoroscopic guidance. The basic technique involved positioning a 6G trocar sleeve around the FB long axis and co-axially advancing an 8G biopsy needle to retrieve the FB within the biopsy core. Retrospective chart review facilitated analysis of procedures, FBs, technical success, and complications.ResultsMean FB size was 23 mm (range 8–74 mm). Four FBs were located within 10 mm of non-vascular significant anatomic structures. The basic technique was successful in 3 cases; 2 cases required technical modifications including using a stiff guide-wire to facilitate retrieval in the case of the post-cryoablation FB; and using the central mandrin of the 6G trocar to push a cement tract back into an augmented vertebra when initial retrieval failed. Overall technical success (FB retrieval or removal to non-hazardous location) was 100 %, with no complications.ConclusionPercutaneous image-guided retrieval of iatrogenic FBs using a large-bore biopsy system is a feasible, safe, effective, and versatile technique, with potential advantages over existing methods.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Noise-Resilient and Reduced Depth Approximate Adders for NISQ Quantum Computing

The "Noisy intermediate-scale quantum" NISQ machine era primarily focuses on mitigating noise, controlling errors, and executing high-fidelity operations, hence requiring shallow circuit depth and noise robustness. Approximate computing is a novel computing paradigm that produces imprecise results by relaxing the need for fully precise output for error-tolerant applications including multimedia, data mining, and image processing. We investigate how approximate computing can improve the noise resilience of quantum adder circuits in NISQ quantum computing. We propose five designs of approximate quantum adders to reduce depth while making them noise-resilient, in which three designs are with carryout, while two are without carryout. We have used novel design approaches that include approximating the Sum only from the inputs (pass-through designs) and having zero depth, as they need no quantum gates. The second design style uses a single CNOT gate to approximate the SUM with a constant depth of O(1). We performed our experimentation on IBM Qiskit on noise models including thermal, depolarizing, amplitude damping, phase damping, and bitflip: (i) Compared to exact quantum ripple carry adder without carryout the proposed approximate adders without carryout have improved fidelity ranging from 8.34% to 219.22%, and (ii) Compared to exact quantum ripple carry adder with carryout the proposed approximate adders with carryout have improved fidelity ranging from 8.23% to 371%. Further, the proposed approximate quantum adders are evaluated in terms of various error metrics.

Gaur, Bhaskar↗
DOE OSTI2017

Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses on a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Aerial Scene Recognition using Efficient Sparse Representation

Advanced scene recognition systems for processing large volumes of high-resolution aerial image data are in great demand today. However, automated scene recognition remains a challenging problem. Efficient encoding and representation of spatial and structural patterns in the imagery are key in developing automated scene recognition algorithms. We describe an image representation approach that uses simple and computationally efficient sparse code computation to generate accurate features capable of producing excellent classification performance using linear SVM kernels. Our method exploits unlabeled low-level image feature measurements to learn a set of basis vectors. We project the low-level features onto the basis vectors and use simple soft threshold activation function to derive the sparse features. The proposed technique generates sparse features at a significantly lower computational cost than other methods~\cite{Yang10, newsam11}, yet it produces comparable or better classification accuracy. We apply our technique to high-resolution aerial image datasets to quantify the aerial scene classification performance. We demonstrate that the dense feature extraction and representation methods are highly effective for automatic large-facility detection on wide area high-resolution aerial imagery.

Computer Vision↗
DOE OSTI2018

Advanced data mining in field ion microscopy

Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Retrieving Biomedical Images through Content-Based Learning from Examples Using Fine Granularity

Traditional content-based image retrieval methods based on learning from examples analyze and attempt to understand high-level semantics of an image as a whole. They typically apply certain case-based reasoning technique to interpret and retrieve images through measuring the semantic similarity or relatedness between example images and search candidate images. The drawback of such a traditional content-based image retrieval paradigm is that the summation of imagery contents in an image tends to lead to tremendous variation from image to image. Hence, semantically related images may only exhibit a small pocket of common elements, if at all. Such variability in image visual composition poses great challenges to content-based image retrieval methods that operate at the granularity of entire images. In this study, we explore a new content-based image retrieval algorithm that mines visual patterns of finer granularities inside a whole image to identify visual instances which can more reliably and generically represent a given search concept. We performed preliminary experiments to validate our new idea for content-based image retrieval and obtained very encouraging results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Localization of polyhydroxybutyrate in sugarcane using Fourier-transform infrared microspectroscopy and multivariate imaging

Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focal plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Performance evaluation of cosmic ray muon trajectory estimation algorithms

Muons, being elementary particles with minimal interaction with nuclear materials and abundant at sea level, have sparked interest in utilizing them for imaging various applications, such as mining [Borselli et al., Sci. Rep. 12, 22329 (2022)], volcano imaging [Nagamine et al., Nucl. Instrum. Meth. A, 356, 585(1995)], and underground tunnel detection [Guardincerri et al., Pure Appl. Geophys. 174, 2133 (2017)]. Recently, their use in nuclear nonproliferation and safeguard verification has gained attention, particularly in cargo screening for nuclear waste smuggling [Baesso et al., J. Instrum. 9, C10041 (2014)], source localization [L. J. Schultz et al., Nucl. Instrum. Meth. A 519, 687 (2004)], and locating nuclear fuel debris in reactors [Borozdin et al., Phys. Rev. Let. 109, 152501 (2012)]. However, the resolution of muon image reconstruction techniques is limited due to multiple Coulomb scattering (MCS) within the target object. To achieve robust muon tomography, it is crucial to develop efficient and flexible physics-based algorithms that can model the MCS process accurately and estimate the most probable trajectory of muons as they pass through the target object. To address this limitation, in this study, a novel algorithmic approach utilizing the Bayesian probability theory and Gaussian approximation of MCS is chosen. Different energy levels, materials, and target sizes were considered in the evaluations. The results demonstrate that the Generalized Muon Trajectory Estimation (GMTE) algorithm offers significant improvements over currently used algorithms. Across all test scenarios, the GMTE algorithm demonstrated ~50% and 38% increase in precision compared to Straight Line Path (SLP) and Point of Closest Approach (PoCA) algorithms, respectively. Furthermore, it exhibited 10%–35% and 10%–15% increases in muon flux utilization for high and medium Z materials, respectively, compared to the PoCA algorithm. In conclusion, the extensive simulations confirm the enhanced performance and efficiency of the GMTE algorithm, offering improved resolution and reduced measurement time for cosmic ray muon imaging compared to the current SLP and PoCA algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

WE-E-17A-03: FDG-PET-Based Radiomics to Predict Local Control and Survival Following Radiotherapy

Purpose: An exploding field in cancer research is “radiomics,” based on the hypothesis that there is statistical (hidden) information in medical images that is prognostic or predictive of outcomes. Our group has developed an efficient pipeline to extract and analyze quantitative image features from medical images as related to outcomes or diagnosis. In this work, we summarize our previous studies with positron emission tomography (PET) images and show the potential of the use of radiomics for outcomes research. Methods: We analyzed two cancer datasets, each consisting of pre-radiotherapy-treatment PET scans: 163 T1-2N0M0 non-small cell lung cancer (NSCLC) patients and 174 head and neck (H and N) cancer patients with stage III–IV. The PET scans were converted to Computational Environment for Radiological Research (CERR) format, and CERR was used to generate 24 shape, texture, and intensity-histogram based image features. Data-mining and logistic regression methods were then used to model local failure (LF) and overall survival (OS). Unbiased estimates of performance were generated using leave-one-out cross-validation (LOOCV). Results: For predicting LF, the models with biologically equivalent dose (BED) and TLG (metabolic tumor volume (MTV) x SUVmean) in NSCLC, and skewness and MTV in H and N, achieved the best performance with AUC=0.818 (p<0.0001) and AUC=0.826 (p=0.0002), respectively. For predicting OS, the models with kurtosis and volume in NSCLC and SUVmax and homogeneity in H and N achieved the best performance with AUC=0.706 (p<0.0001) and AUC=0.656 (p=0.0003), respectively. On LOOCV, all these models retained significant predictive power. Interestingly, MTV was highly correlated with LF in both sites. Conclusion: PET-based imaged features are promising tools for improving treatment management decision making. Much more research is needed to identify optimal radiomics metrics and to correlate imaging phenotype with other clinical or genomic information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-A-12A-10: Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Purpose: Recent advances in medical imaging technologies provide opportunities to quantify the tumor phenotype throughout the course of treatment non-invasively. The emerging field of Radiomics addresses this by converting medical images into minable data by applying a large number of quantitative imaging algorithms. Accurate tumor segmentation is one of the main challenges of Radiomics. It has been shown that semiautomatic segmentation approaches efficiently reduce inter-observer variability as compared to the time consuming manual delineations. In this study, a semiautomatic volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for Radiomics features quantification. Methods: Fifty-six 3D-Radiomics features, quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These Radiomics features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Results: Radiomics features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC= 0.85 0.15, p=0.0009) compared to the features extracted from the manual segmentations (ICC= 0.77 0.17). Furthermore, the features extracted from 3D-Slicer based segmentations, spread over significantly smaller range across observers as compared to those of the manual delineations (p= 3.819e-07). Moreover, the features derived from 3D-Slicer segmentations overlapped in range with those of the manual delineations, as the lower(higher) limit(s) being significantly higher(lower) for the 3D-Slicer features (p= 0.007, p= 5.863e-06). Conclusion: 3D-Slicer based semiautomatic segmentation significantly improves the robustness of radiomics features and thus could serve as a potential alternative to the time consuming manual segmentation process. So 3D-Slicer can have a large application in Radiomics, and be employed for high-throughput data mining research of medical imaging in clinical oncology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

EGS Collab Experiment 2: Testbed Well Orientation Data

This dataset contains the north seeking gyro data for each of the 11 boreholes drilled at the Experiment 2 testbed on the 4100 foot level of the SURF (Sanford Underground Research Facility). Each gyro file is in individual folders for each well. A single folder called 'E2 All Well Trajectories' contains the trajectories for all the wells but in the mine coordinate system. An image of the well layout is provided in a PowerPoint.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Passive seismic tomography application for cave monitoring in DOZ underground mine PT. Freeport Indonesia

It is already known that tomography has a great impact for analyzing and mapping unknown objects based on inversion, travel time as well as waveform inversion. Therefore, tomography has used in wide area, not only in medical but also in petroleum as well as mining. Recently, tomography method is being applied in several mining industries. A case study of tomography imaging has been carried out in DOZ ( Deep Ore Zone ) block caving mine, Tembagapura, Papua. Many researchers are undergoing to investigate the properties of DOZ cave not only outside but also inside which is unknown. Tomography takes a part for determining this objective.The sources are natural from the seismic events that caused by mining induced seismicity and rocks deformation activity, therefore it is called as passive seismic. These microseismic travel time data are processed by Simultaneous Iterative Reconstruction Technique (SIRT). The result of the inversion can be used for DOZ cave monitoring. These information must be used for identifying weak zone inside the cave. In addition, these results of tomography can be used to determine DOZ and cave information to support mine activity in PT. Freeport Indonesia.

58 GEOSCIENCES↗
DOE OSTI2014

Quantitative multi-modal NDT data analysis

A single NDT technique is often not adequate to provide assessments about the integrity of test objects with the required coverage or accuracy. In such situations, it is often resorted to multi-modal testing, where complementary and overlapping information from different NDT techniques are combined for a more comprehensive evaluation. Multi-modal material and defect characterization is an interesting task which involves several diverse fields of research, including signal and image processing, statistics and data mining. The fusion of different modalities may improve quantitative nondestructive evaluation by effectively exploiting the augmented set of multi-sensor information about the material. It is the redundant information in particular, whose quantification is expected to lead to increased reliability and robustness of the inspection results. There are different systematic approaches to data fusion, each with its specific advantages and drawbacks. In our contribution, these will be discussed in the context of nondestructive materials testing. A practical study adopting a high-level scheme for the fusion of Eddy Current, GMR and Thermography measurements on a reference metallic specimen with built-in grooves will be presented. Results show that fusion is able to outperform the best single sensor regarding detection specificity, while retaining the same level of sensitivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Hyperspectral Cathodoluminescence of Bastnäsite at the Mountain Pass Mine, USA: Implications for REE Mineralisation and Mining

This study uses micro-analyses including hyperspectral cathodoluminescence to image and obtain mineral spectral to help evaluate REE deportment via from Mountain Pass Mine in California. It presents the first known CL-based spectra for bastnäsite, new information regarding baryte CL spectra, providing insights on the coeval and spatial relationship between REE, Ba, and S in carbonatite systems. Bastnäsite, baryte, and monazite are often intergrown in both brecciated, “blue ore” samples, and these minerals occur as an apparent intercumulus texture in the dolomite-rich, “white ore” samples. Bastnäsite luminescence is present, but very low compared to baryte in the visible spectrum. Bastnäsite illustrates some textural variability related to its main luminescent peak ~1.4 eV, which is indicative of Nd3+. It also exhibits several other small peaks comprised of multiple activators related to various REE, including Ce3+, Sm3+, Eu3+, and potentially Eu2+. Baryte displays textures of variable complexity but often with darker rims near crystal margins. It luminesces mainly at 1.4-1.52 eV, 2.05 eV, and 3.59-4.08 eV. The 2.05 eV peak is likely attributed to Ag+, but the other peaks are more enigmatic.

58 - GEOSCIENCES↗