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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Location generalizability of image-based air quality models

This paper is to be submitted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Computer Vision for Earth Observation workshop. The full paper abstract is below: The ability to rapidly quantify atmospheric pollutants is important both for global emissions monitoring and for mitigating the adverse effects that follow a hazardous chemical release. In the aftermath of a chemical release, imagery is often the only available resource to assess local conditions. Recent work has demonstrated initial success in predicting particulate matter pollution from imagery; however, these results are tied to a specific site and do not generalize to new geographic locations. In this work, we seek to understand how easily deep learning models generalize to new locations in the context of image-based air quality assessments, targeting two distinct tasks: (1) broad measures of particulate matter pollution, and (2) the mass of a given chemical released in hazardous plumes. For the latter, we focus on sulfur dioxide, a toxic aerosol and a major component of particulate matter pollution caused by industrial fossil fuel consumption. To develop a model that operates in the widest possible range of environments, we test different training strategies, including the use of new geolocation foundation models. The best performing models achieve >80% accuracy when evaluating unseen imagery at previously seen sites, but we find significant drops in performance when evaluating imagery from unseen sites, at best 65%. Additionally, we present the public release of the National Parks Air Quality Index Dataset, a new medium-sized dataset that pairs imagery with sensor-based air quality measurements at 15 different national parks.

Byler, Eleanor B. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2022

Rapid multiplex ultrafast nonlinear microscopy for material characterization

We demonstrate rapid imaging based on four-wave mixing (FWM) by assessing the quality of advanced materials through measurement of their nonlinear response, exciton dephasing, and exciton lifetimes. We use a WSe 2 monolayer grown by chemical vapor deposition as a canonical example to demonstrate these capabilities. By comparison, we show that extracting material parameters such as FWM intensity, dephasing times, excited state lifetimes, and distribution of dark/localized states allows for a more accurate assessment of the quality of a sample than current prevalent techniques, including white light microscopy and linear micro-reflectance spectroscopy. We further discuss future improvements of the ultrafast FWM techniques by modeling the robustness of exponential decay fits to different spacing of the sampling points. Employing ultrafast nonlinear imaging in real-time at room temperature bears the potential for rapid in-situ sample characterization of advanced materials and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Summary: SEE4GEO

The seismoelectric effects technique (SEE) is a new and innovative approach for geothermal subsurface imaging and monitoring at reservoir scale. The objective of this project is to assess SEE in terms of data acquisition, cost and quality, and to determine its capability in comparison with classical imaging and monitoring techniques, particularly decoupled seismic and electromagnetic methods. This will be achieved by (1) development of a fast, true 3D numerical package, handling SEE imaging and subsurface properties characterization, including resistivity and permeability, (2) laboratory experiments performed in a controlled environment to define optimal deployment design, data quality, and inform field deployment, and (3) field surveys to ultimately test and draw lessons for practical use of SEE technology. There is a relatively extensive body of work in the literature on SEE, and members of this consortium have been involved in theoretical and numerical development of SEE modeling as well as laboratory experiments. Nevertheless, to our knowledge few, if any, documented efforts have been specifically targeting the use of SEE for geothermal subsurface imaging and monitoring. The strength and originality of our proposal rely on an integrated approach leveraging numerical, laboratory and field experiments, to properly document the practical use of SEE. Through this process, SEE in-hand technology for the geothermal industry will be able to progress from a TRL 1 to TRL 3.

58 GEOSCIENCES↗
DOE OSTI2022

Seismoelectric Effects for Geothermal Resources Assessment and Monitoring (SEE4GEO)

The seismoelectric effects technique (SEE) is a new and innovative approach for geothermal subsurface imaging and monitoring at reservoir scale. The objective of this project is to assess SEE in terms of data acquisition, cost and quality, and to determine its capability in comparison with classical imaging and monitoring techniques, particularly decoupled seismic and electromagnetic methods. This will be achieved by (1) development of a fast, true 3D numerical package, handling SEE imaging and subsurface properties characterization, including resistivity and permeability, (2) laboratory experiments performed in a controlled environment to define optimal deployment design, data quality, and inform field deployment, and (3) field surveys to ultimately test and draw lessons for practical use of SEE technology. There is a relatively extensive body of work in the literature on SEE, and members of this consortium have been involved in theoretical and numerical development of SEE modeling as well as laboratory experiments. Nevertheless, to our knowledge few, if any, documented efforts have been specifically targeting the use of SEE for geothermal subsurface imaging and monitoring. The strength and originality of our proposal rely on an integrated approach leveraging numerical, laboratory and field experiments, to properly document the practical use of SEE. Through this process, SEE in-hand technology for the geothermal industry will be able to progress from a TRL 1 to TRL 3.

58 GEOSCIENCES↗
DOE OSTI2021

Scalable balanced training of conditional generative adversarial neural networks on image data

Here, we propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score, Fréchet inception distance, and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1000 processes and 2000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

Obata, Yoshihiro (ORCID:0000000303659129)↗
DOE OSTI2021

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence

We use a stable parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs). The parallel training reduces the risk of mode collapse and enhances scalability by using multiple generators that are concurrently trained, each one of them focusing on a single data label. The use of the Wasserstein metric reduces the risk of cycling by stabilizing the training of each generator. We apply the approach on the CIFAR10 and the CIFAR100 datasets, two standard benchmark datasets with images of the same resolution, but different number of classes. Performance is assessed using the inception score, the Fréchet inception distance, and image quality. An improvement in inception score and Fréchet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks (DC-CGANs). Weak scaling is attained on both datasets using up to 100 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

Lupo Pasini, Massimiliano↗
DOE OSTI2024

Towards Full Field-of-View Fourier Ptychography for Extreme Ultraviolet Microscope

We evaluate various Fourier ptychographic microscopy (FPM) reconstruction algorithms using both simulated and experimental data acquired from an Extreme Ultraviolet (EUV, 13.5 nm wavelength) microscope. We specifically focus on the algorithms' ability to robustly address field-dependent aberrations, which enables increased spatial resolution and quantitative phase imaging across an expanded field of view. We systematically compare the algorithms' performance under aberrations for a single zoneplate imaging system, utilizing Fourier Ring Correlation (FRC) as a systematic metric for assessing reconstruction quality. Furthermore, we explore the impact of systematic errors on the reconstruction of experimental data, aiming to increase the effective field of view by 25-fold, from the nominal 5x5 um2 diffraction-limited area. Additionally, our evaluation incorporates innovative FPM-adjacent methodologies, including the Angular Ptychographic Imaging with Closed-form method (APIC), for reconstructing EUV images.

Gu, Chaoying↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
DOE OSTI2023

Nuctech-SOP-Image_Reconstruction (includes signatures)

This Standard Operating Procedure (SOP) describes the steps to reconstruct x-ray projection images from Nuctech LS1516BS data captured at facilities operating in cooperation with the Department of Homeland Security (DHS) Explosives Threat Assessment Program (ETA). This document also provides the steps for evaluating and approving the quality of the reconstructed images.

42 ENGINEERING↗
DOE OSTI2026

RTN-117: Image Calibration and Instrument Signal Removal for the First Year of the LSST

The NSF-DOE Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) requires calibration products that provide uniform, stable, and accurate photometric and astrometric performance across the 3.2 gigapixel focal plane and throughout the 10-year survey. This paper details the algorithms and workflows used to produce instrument calibrations and remove instrumental artifacts for Data Preview 2 (DP2)---the first end-to-end processing demonstration using on-sky data with the LSST Camera (LSSTCam). We describe the verification, acceptance, and certification framework used to assess calibration quality and quantify residual systematics. We show baseline metrics on calibrated science images to evaluate the robustness of the calibration and instrument signature removal (ISR) data processing pipelines for DP2. Finally, we summarize the known limitations observed in DP2 production and outline expected algorithmic improvements for the first public LSST data release (Data Release~1, DR1).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Structural Evolution of the Hogback Monocline and Its Tectonic Significance in the San Juan Basin

The San Juan Basin is recognized as a Laramide foreland basin. It is located within the Colorado Plateau, a broad tectonic province characterized by a thick sedimentary sequence that was segmented into smaller sub basins during the Late Cretaceous to Paleogene Laramide orogeny. The Hogback Monocline lies along the northwestern margin of the San Juan Basin and is considered a Laramide-age structure formed in response to compressional stress. In this study, we interpret surface and subsurface datasets to construct a structural geological model and evaluate its tectonic significance. Through seismic data, we identify key fault and fold geometries at depth. The seismic dataset used in this study was reprocessed in depth and constrained with well log velocity data to enhance seismic imaging quality. Additionally, we performed well log correlations to identify formation tops and assess variations in basin infill and thickness geometry. A series of structural cross-sections, constructed using seismic data and a high density of boreholes, are presented to evaluate geometric variations along the structure and its evolution during basin development. Furthermore, kinematic restoration and forward modeling analyses were conducted to validate our structural interpretation. This work suggests that the Hogback Monocline formed through fault-propagation folding and flexural slip affecting the pre-Laramide sedimentary sequence under compressional stresses associated with the Laramide orogeny. This structure is interpreted as a high-angle reverse fault that influenced the geometry of the late basin infill. Additionally, monocline bending along the structure may have been controlled by fault relay systems and, in some cases, influenced by strike-slip faulting.

Reyes, Martin [New Mexico Bureau o fGeology and Mi↗
DOE OSTI2023

Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors

This software offers methods and functions for training calibrated out-of-distribution detectors for medical image classification tasks. It includes functionalities for training, synthesizing data augmentations, calibration, and out-of-distribution detection. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, VivekSivaraman↗
DOE OSTI2024

DECIDER

This software offers methods and functions for building failure detectors for deep image classification models with the aid of vision-language models and LLMs. It includes functionalities for training baseline image classifiers, debiasing classifiers using vision-language models and LLMs, evaluating failure between models along with baselines. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, Vivek Sivaraman↗
DOE OSTI2022

Fluorescence strobo-stereoscopy for specular reflection-suppressed full field of view imaging

This report introduces fluorescence strobo-stereoscopy (FSS) to suppress strong specular reflection and enable the full field of view (FFOV) 3D surface imaging while the part is rotating. Specular reflection off the target surface significantly degrades the image quality and becomes critical for highly reflective surface measurements. In FSS, the fluorescent dye-doped fluid applied on the machined surface is excited upon incident ultra-violet light and becomes a new light source by Stokes' Law. Thus, specular reflection off of smooth surface can be suppressed by separating the fluorescent light from the excitation light. The developed FSS comprises a pair of imaging cameras, spatial filters, and an excitation light source. As a result, FSS effectively rejected the specular reflection and improved the FFOV 3D surface image quality of the machined part by enhancing contrast in the rotating target surface. Such enhancements in 3D imaging allowed to identify manufacturing tolerance of the part and to detect the surface features. The axial and lateral accuracy errors of FSS were 2.3% and 1.4% with the target size of 4.07 mm and 0.215 mm, respectively. A whole view reconstruction of the cylindrical target sample was performed, and the corresponding cylindricity and diameter deviation were assessed. The fluid media effect and the target surface quality effect were discussed.

3D surface imaging↗
DOE OSTI2021

A Framework for Patient-Centered Pathways of Care for Radiopharmaceutical Therapy: An ASTRO Consensus Document

Radiopharmaceutical therapy (RPT) is an area of projected growth and importance with several agents in clinical use, new agents in late-phase clinical trials, and many others under testing and development. This article proposes a framework for developing pathways of care that can be broadly applied to all RPTs, representing the current status of RPT. It suggests foundational elements for many pathways of care for patients with cancer and concludes with areas in active development and the future horizon for RPT treatment centers. Developing a framework for patient-centered pathways of care is a critical step in establishing RPT as standard therapy for patients with a diverse spectrum of cancers. This expected increase in RPT treatment options will affect a much larger population of patients with complex cancer. It will also require enhanced coordination and collaboration among appropriately qualified personnel with diverse expertise in image acquisition, image interpretation, quantitative imaging, dosimetry calculation, radiation quality assurance and safety as well as oncology care and RPT-induced sequelae and response assessment. The essential role of this evolving RPT care team within multidisciplinary oncology care is a cornerstone of this framework for a patient-centered pathway of care for RPT. Given the status of current RPT practice and the horizon for future applications, this patient-centered pathway of care guidance is timely and should help inform future clinical RPT practice paradigms. A task force was recruited from the Theranostic Working Group of the American Society for Radiation Oncology (ASTRO) in May 2019 with equal representation from the nuclear medicine community. The task force expanded on a framework that was originally conceived by the Working Group for patient-centered care. This framework was developed to incorporate the strengths of both radiation oncologists and nuclear medicine physicians. The manuscript was then developed by the task force and posted on the ASTRO website for a 6-week public comment period ending in July 2020. Comments were adjudicated, and the draft was sent to external organizations for potential endorsement. This document was sent to the ASTRO Board of Directors in October 2020 for approval.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Dimorphic Mechanisms of Fragility in Diabetes Mellitus: the Role of Reduced Collagen Fibril Deformation

ABSTRACT Diabetes mellitus (DM) is an emerging metabolic disease, and the management of diabetic bone disease poses a serious challenge worldwide. Understanding the underlying mechanisms leading to high fracture risk in DM is hence of particular interest and urgently needed to allow for diagnosis and treatment optimization. In a case–control postmortem study, the whole 12th thoracic vertebra and cortical bone from the mid-diaphysis of the femur from male individuals with type 1 diabetes mellitus (T1DM) (n = 6; 61.3 ± 14.6 years), type 2 diabetes mellitus (T2DM) (n = 11; 74.3 ± 7.9 years), and nondiabetic controls (n = 18; 69.3 ± 11.5) were analyzed with clinical and ex situ imaging techniques to explore various bone quality indices. Cortical collagen fibril deformation was measured in a synchrotron setup to assess changes at the nanoscale during tensile testing until failure. In addition, matrix composition was analyzed including determination of cross-linking and non-crosslinking advanced glycation end-products like pentosidine and carboxymethyl-lysine. In T1DM, lower fibril deformation was accompanied by lower mineralization and more mature crystalline apatite. In T2DM, lower fibril deformation concurred with a lower elastic modulus and tendency to higher accumulation of non-crosslinking advanced glycation end-products. The observed lower collagen fibril deformation in diabetic bone may be linked to altered patterns mineral characteristics in T1DM and higher advanced glycation end-product accumulation in T2DM. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

59 BASIC BIOLOGICAL SCIENCES↗