Results for “Distributed Learning” — Page 87

DOE OSTI2026

Active learning enables generation of molecules that advance the known Pareto front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Chemistry↗

DOE OSTI2023

Novel machine-learning method for spin classification of neutron resonances

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonant structure whose shape is determined in part by the angular-momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is, therefore, paramount. In this project, we apply machine learning to automate the quantum number assignments using only the resonances' energies and widths and not relying on detailed transmission or capture measurements. The classifier used for quantum number assignment is trained using stochastically generated resonance sequences whose distributions mimic those of real data. Here we explore the use of several physics-motivated features for training our classifier. These features amount to out-of-distribution tests of a given resonance's widths and resonance-pair spacings. We pay special attention to situations where either capture widths cannot be trusted for classification purposes or where there is insufficient information to classify resonances by the total spin J. We demonstrate the efficacy of our classification approach using simulated and actual 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Distributed Workflows for Modeling Experimental Data

Modeling helps explain the fundamental physics hidden behind experimental data. In the case of material modeling, running one simulation rarely results in output that reproduces the experimental data. Often one or more of the force field parameters are not precisely known and must be optimized for the output to match that of the experiment. Since the simulations require high performance computing (HPC) resources and there are usually many simulations to run, a workflow is very useful to prevent errors and assure that the simulations are identical except for the parameters that need to be varied. These workflows are usually distributed because the simulations require HPC, but the optimization and steps to compare the simulation results and experimental data are done on a local workstation. We will present results from force field refinement of data collected at the Spallation Neutron Source using Kepler, Pegasus, and Beam workflows and discuss what we have learned from using these workflows.

Lynch, Vickie E.↗

DOE OSTI2024

Evidence of short chains in liquid sulfur

High energy x-ray pair distribution function measurements show the average coordination number of the first shell in liquid sulfur is 1.86 ± 0.04 across the λ-transition, not precisely 2.0 as widely accepted. This indicates that upon melting, liquid sulfur does not comprise solely of S 8 rings but also possesses a significant number of short chains. Intensities of the pre-peak and first diffraction peak of the x-ray structure factor and third peak height of the pair distribution function all show deviations at the λ-transition temperature T λ , associated with the break-up of S 8 rings and the start of oligomer polymerization. A significant number of non-bonded or loosely bonded “interstitial atoms,” with an average coordination number of 0.20 ± 0.005, are also observed in the so-called “forbidden zone” between the first and second shells upon melting. The number of interstitial atoms is found to decrease to a minimum at the λ-transition, but the majority persist into the high temperature polymerized liquid. Furthermore, the existence of short chains and nearby interstitial atoms represent the two main factors required to initiate the S 8 -ring to chain transition, as proposed by recent molecular dynamics simulations.

Chemical bonding↗

DOE OSTI2024

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗

DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Full-Column Greenhouse Gas Sampling 2012-2014 Final Campaign Report

The vertical distributions of CO 2 , CH 4 , and other gases provide important constraints when determining terrestrial and ocean sources and sinks of carbon and other biogeochemical processes in the Earth system. The U.S. Department of Energy's (DOE) Office of Biological and Environmental Research and the National Oceanic and Atmospheric Administration's Earth System Research Laboratory to quantify the vertically resolved distribution of atmospheric carbon-cycle gases(CO 2 , CH 4 ) within approximately 99% of the atmospheric column at the DOE ’s Atmospheric Radiation Measurement Southern Great Plains (SGP) site in Oklahoma . During the 2012 to 2014 campaign period, 12 successful Air C ore flights were conducted from the SGP site . In addition to providing critical data for evaluating remote sensing and earth system models, valuable lessons were learned that motivate improvements to the sampling and recovery systems and campaign logistics . With the launch of the Orbiting Carbon Observatory - 2 (OCO - 2) and Greenhouse gases Observing Satellite ( GOSAT ) satellites, we look forward to proposing additional sampling and analysis efforts at the SGP site and at other sites to characterize the vertical distribution of CO 2 , CH 4 over time and space.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Learning Based Bidding Strategy for HVAC Systems in Double Auction Retail Energy Markets

In this paper, a bidding strategy is proposed using reinforcement learning for HVAC systems in a double auction market. The bidding strategy does not require a specific model-based representation of behavior, i.e., a functional form to translate indoor house temperatures into bid prices. The results from reinforcement learning based approach are compared with the HVAC bidding approach used in the AEP gridSMART® smart grid demonstration project and it is shown that the model-free (learning based) approach tracks well the results from the model-based behavior. Successful use of model-free approaches to represent device-level economic behavior may help develop similar approaches to represent behavior of more complex devices or groups of diverse devices, such as in a building. Distributed control requires an understanding of decision making processes of intelligent agents so that appropriate mechanisms may be developed to control and coordinate their responses, and model-free approaches to represent behavior will be extremely useful in that quest.

energy market↗

DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗

DOE OSTI2018

Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides

The pursuit of more advanced electronics, and finding solutions to energy needs often hinges upon the discovery and optimization of new functional materials. However, the discovery rate of these materials is alarmingly low. Much of the information that could drive this rate higher is scattered across tens of thousands of papers in the extant literature published over several decades but is not in an indexed form, and cannot be used in entirety without substantial effort. Many of these limitations can be circumvented if the experimentalist has access to systematized collections of prior experimental procedures and results. Here, we investigate the property-processing relationship during growth of oxide films by pulsed laser deposition. To do so, we develop an enabling software tool to (1) mine the literature of relevant papers for synthesis parameters and functional properties of previously studied materials, (2) enhance the accuracy of this mining through crowd sourcing approaches, (3) create a searchable repository that will be a community-wide resource enabling material scientists to leverage this information, and (4) provide through the Jupyter notebook platform, simple machine-learning-based analysis to learn the complex interactions between growth parameters and functional properties. Here, the results allow visualization of growth windows, trends and outliers, which can serve as a template for analyzing the distribution of growth conditions, provide starting points for related compounds and act as a feedback for first-principles calculations. Such tools will comprise an integral part of the materials design schema in the coming decade.

36 MATERIALS SCIENCE↗

DOE OSTI2015

TU-EF-207-00: Advances in Breast Imaging

Breast imaging technology is advancing on several fronts. In digital mammography, the major technological trend has been on optimization of approaches for performing combined mammography and tomosynthesis using the same system. In parallel, photon-counting slot-scan mammography is now in clinical use and more efforts are directed towards further development of this approach for spectral imaging. Spectral imaging refers to simultaneous acquisition of two or more energy-windowed images. Depending on the detector and associated electronics, there are a number of ways this can be accomplished. Spectral mammography using photon-counting detectors can suppress electronic noise and importantly, it enables decomposition of the image into various material compositions of interest facilitating quantitative imaging. Spectral imaging can be particularly important in intravenously injected contrast mammography and eventually tomosynthesis. The various approaches and applications of spectral mammography are discussed. Digital breast tomosynthesis relies on the mechanical movement of the x-ray tube to acquire a number of projections in a predefined arc, typically from 9 to 25 projections over a scan angle of +/−7.5 to 25 degrees depending on the particular system. The mechanical x-ray tube motion requires relatively long acquisition time, typically between 3.7 to 25 seconds depending on the system. Moreover, mechanical scanning may have an effect on the spatial resolution due to internal x-ray filament or external mechanical vibrations. New x-ray source arrays have been developed and they are aimed at replacing the scanned x-ray tube for improved acquisition time and potentially for higher spatial resolution. The potential advantages and challenges of this approach are described. Combination of digital mammography and tomosynthesis in a single system places increased demands on certain functional aspects of the detector and overall performance, particularly in the tomosynthesis mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-EF-207-01: Introductory Remarks on Recent Advances in Breast Imaging

Breast imaging technology is advancing on several fronts. In digital mammography, the major technological trend has been on optimization of approaches for performing combined mammography and tomosynthesis using the same system. In parallel, photon-counting slot-scan mammography is now in clinical use and more efforts are directed towards further development of this approach for spectral imaging. Spectral imaging refers to simultaneous acquisition of two or more energy-windowed images. Depending on the detector and associated electronics, there are a number of ways this can be accomplished. Spectral mammography using photon-counting detectors can suppress electronic noise and importantly, it enables decomposition of the image into various material compositions of interest facilitating quantitative imaging. Spectral imaging can be particularly important in intravenously injected contrast mammography and eventually tomosynthesis. The various approaches and applications of spectral mammography are discussed. Digital breast tomosynthesis relies on the mechanical movement of the x-ray tube to acquire a number of projections in a predefined arc, typically from 9 to 25 projections over a scan angle of +/−7.5 to 25 degrees depending on the particular system. The mechanical x-ray tube motion requires relatively long acquisition time, typically between 3.7 to 25 seconds depending on the system. Moreover, mechanical scanning may have an effect on the spatial resolution due to internal x-ray filament or external mechanical vibrations. New x-ray source arrays have been developed and they are aimed at replacing the scanned x-ray tube for improved acquisition time and potentially for higher spatial resolution. The potential advantages and challenges of this approach are described. Combination of digital mammography and tomosynthesis in a single system places increased demands on certain functional aspects of the detector and overall performance, particularly in the tomosynthesis mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-EF-207-02: Spectral Mammography Based on Photon Counting Detectors

Breast imaging technology is advancing on several fronts. In digital mammography, the major technological trend has been on optimization of approaches for performing combined mammography and tomosynthesis using the same system. In parallel, photon-counting slot-scan mammography is now in clinical use and more efforts are directed towards further development of this approach for spectral imaging. Spectral imaging refers to simultaneous acquisition of two or more energy-windowed images. Depending on the detector and associated electronics, there are a number of ways this can be accomplished. Spectral mammography using photon-counting detectors can suppress electronic noise and importantly, it enables decomposition of the image into various material compositions of interest facilitating quantitative imaging. Spectral imaging can be particularly important in intravenously injected contrast mammography and eventually tomosynthesis. The various approaches and applications of spectral mammography are discussed. Digital breast tomosynthesis relies on the mechanical movement of the x-ray tube to acquire a number of projections in a predefined arc, typically from 9 to 25 projections over a scan angle of +/−7.5 to 25 degrees depending on the particular system. The mechanical x-ray tube motion requires relatively long acquisition time, typically between 3.7 to 25 seconds depending on the system. Moreover, mechanical scanning may have an effect on the spatial resolution due to internal x-ray filament or external mechanical vibrations. New x-ray source arrays have been developed and they are aimed at replacing the scanned x-ray tube for improved acquisition time and potentially for higher spatial resolution. The potential advantages and challenges of this approach are described. Combination of digital mammography and tomosynthesis in a single system places increased demands on certain functional aspects of the detector and overall performance, particularly in the tomosynthesis mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-EF-207-04: Advances in Detector Technology for Breast Tomosynthesis

Breast imaging technology is advancing on several fronts. In digital mammography, the major technological trend has been on optimization of approaches for performing combined mammography and tomosynthesis using the same system. In parallel, photon-counting slot-scan mammography is now in clinical use and more efforts are directed towards further development of this approach for spectral imaging. Spectral imaging refers to simultaneous acquisition of two or more energy-windowed images. Depending on the detector and associated electronics, there are a number of ways this can be accomplished. Spectral mammography using photon-counting detectors can suppress electronic noise and importantly, it enables decomposition of the image into various material compositions of interest facilitating quantitative imaging. Spectral imaging can be particularly important in intravenously injected contrast mammography and eventually tomosynthesis. The various approaches and applications of spectral mammography are discussed. Digital breast tomosynthesis relies on the mechanical movement of the x-ray tube to acquire a number of projections in a predefined arc, typically from 9 to 25 projections over a scan angle of +/−7.5 to 25 degrees depending on the particular system. The mechanical x-ray tube motion requires relatively long acquisition time, typically between 3.7 to 25 seconds depending on the system. Moreover, mechanical scanning may have an effect on the spatial resolution due to internal x-ray filament or external mechanical vibrations. New x-ray source arrays have been developed and they are aimed at replacing the scanned x-ray tube for improved acquisition time and potentially for higher spatial resolution. The potential advantages and challenges of this approach are described. Combination of digital mammography and tomosynthesis in a single system places increased demands on certain functional aspects of the detector and overall performance, particularly in the tomosynthesis mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-EF-207-05: Dedicated Cone-beam Breast CT

Breast imaging technology is advancing on several fronts. In digital mammography, the major technological trend has been on optimization of approaches for performing combined mammography and tomosynthesis using the same system. In parallel, photon-counting slot-scan mammography is now in clinical use and more efforts are directed towards further development of this approach for spectral imaging. Spectral imaging refers to simultaneous acquisition of two or more energy-windowed images. Depending on the detector and associated electronics, there are a number of ways this can be accomplished. Spectral mammography using photon-counting detectors can suppress electronic noise and importantly, it enables decomposition of the image into various material compositions of interest facilitating quantitative imaging. Spectral imaging can be particularly important in intravenously injected contrast mammography and eventually tomosynthesis. The various approaches and applications of spectral mammography are discussed. Digital breast tomosynthesis relies on the mechanical movement of the x-ray tube to acquire a number of projections in a predefined arc, typically from 9 to 25 projections over a scan angle of +/−7.5 to 25 degrees depending on the particular system. The mechanical x-ray tube motion requires relatively long acquisition time, typically between 3.7 to 25 seconds depending on the system. Moreover, mechanical scanning may have an effect on the spatial resolution due to internal x-ray filament or external mechanical vibrations. New x-ray source arrays have been developed and they are aimed at replacing the scanned x-ray tube for improved acquisition time and potentially for higher spatial resolution. The potential advantages and challenges of this approach are described. Combination of digital mammography and tomosynthesis in a single system places increased demands on certain functional aspects of the detector and overall performance, particularly in the tomosynthesis mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Powered By ReEDS™ [Slides]

The National Renewable Energy Laboratory's flagship Regional Energy Deployment System (ReEDS) electric grid planning model is informing the answers to some of the biggest questions surrounding electricity sector research. Powered By ReEDS is the third webinar in the Powered By series. Each webinar highlights an innovative NREL grid planning and analysis tool and its real-world applications. The series is an exciting opportunity to learn directly from NREL's grid experts, so make sure to bring your questions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗