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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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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Relevance Vector Machine Image Reconstruction Algorithm for Electrical Capacitance Tomography With Explicit Uncertainty Estimates

We present a Relevance Vector Machine (RVM) based algorithm for electrical capacitance tomography (ECT) applications that can concurrently provide image reconstruction results and uncertainty estimates about the reconstruction. To illustrate the RVM operation in ECT, we simulate typical ECT scenarios, making explicit the connection between the reconstructed pixel values and the corresponding uncertainty estimates in each case. We compare the RVM reconstruction performance with that of the Iterative Landweber Method (ILM) and the least absolute shrinkage and selection operator (LASSO) in all the considered scenarios. The results show that, in addition to the key advantage of providing uncertainty measures, RVM can achieve similar reconstruction results with either lower or similar computational complexity.

42 ENGINEERING↗
DOE OSTI2025

Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction

4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an important inverse problem in non-destructive evaluation. Conventional back-projection based reconstruction methods assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. Here, we use a forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued 4D object coordinates. Unlike previous neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fast Hyperspectral Neutron Tomography

Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.

Chowdhury, Mohammad Samin Nur [Purdue University]↗
DOE OSTI2025

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. Here, we propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.

image segmentation↗
DOE OSTI2022

Coordinated neutron and X-ray computed tomography of meteorites: Detection and distribution of hydrogen-bearing materials

The presence and distribution of hydrogen-bearing materials in meteorites are important constraints on processes in the early solar system, and the delivery of volatile constituents to growing planets. Here, in this paper, we show that coordinated neutron and X-ray computed tomography, NXCT, can reveal the presence and distributions of hydrogen-bearing materials in meteorites, and thus help constrain the presence and actions of water in the early solar system. NXCT is nearly nondestructive of meteorite samples. Neutron fluence in NXCT is approximately seven orders of magnitude less than in typical instrumental neutron activation analysis, and so produces little residual radioactivity and currently undetectable changes in isotope ratios. Heating during NXCT is minimal, but NXCT will overprint the record of cosmic ray exposure held in natural thermoluminescence. Two meteorites were examined. EET 87503 is a howardite, a regolith breccia inferred to be from the asteroid 4 Vesta, and contains fragments of eucrite basalt, diogenite pyroxenite, and H-rich carbonaceous chondrites. With NXCT, the chondrite fragments within the meteorite piece can be clearly located and characterized, in preparation for possible extraction and detailed analyses. Graves Nunataks (GRA) 06100 is a CR2 chondrite meteorite that contains abundant iron metal and H-bearing silicates from aqueous alteration. In NXCT, H-bearing altered material is clearly distinguished from metal, and its distribution in three dimensions is revealed as a constraint on the processes of alteration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Influence of Data Acquisition Algorithms on X-ray Phase Contrast Imaging Computed Tomography

X-ray phase contrast imaging (XPCI) is a non-destructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating based Talbot-Lau XPCI system to three- dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Deep Learning Super-Resolution X-Ray Computed Tomography Algorithms for Additive Manufacturing

Industrial X-ray computed tomography (XCT) is a nondestructive method for inspection and characterization of additively manufactured (AM) materials and parts. In practice, the resolution of XCT can be limited by factors such as detector binning, restricted field of view for large-scale objects, system blur, motion during scanning, and acquisition settings. These limitations can reduce the detectability of critical flaws such as pores, cracks, and lack of fusion. Super-resolution (SR) techniques offer a promising solution for improving the effective resolution and image quality of XCT reconstructions without the need for expensive hardware upgrades or laborious, time-consuming scans. In particular, deep learning-based SR methods have garnered attention in recent years as powerful tools for reconstructing high-resolution volumes from low-resolution inputs. In this work, a novel deep learning-based SR method is proposed for XCT scans of AM parts, and compared against several existing state-of-the-art (SOTA) methods. The proposed method, Simurgh-SR, is built on the pre-existing Simurgh framework and consists of a 2.5D U-Net trained to map low-quality inputs containing noise and artifacts to high-quality reconstructions characterized by higher flaw contrast, better noise texture, and reduced artifacts. The experimental results demonstrate superior performance of Simurgh-SR in performing 4× SR on real industrial XCT scans of thick 316L components, enhancing the structural similarity score and peak signal-to-noise ratio (>7dB) compared to the LR counterpart while improving the F1-score for flaw detection by more than 2.3× when compared to alternative SOTA SR methods. This improvement enables more accurate and significantly faster characterization of metal AM components. Additionally, Simurgh-SR was trained for both 2X and 4X SR and performs effectively at both levels, enabling the use of a single model for various SR factors.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
DOE OSTI2023

Coexistent quantum channel characterization using quantum process tomography with spectrally resolved detection

The coexistence of classical and quantum signals over the same optical fiber is critical for quantum networks operating within the existing communications infrastructure. Here, we characterize the quantum channel that results from distributing approximate single-photon polarization-encoded qubits simultaneously with classical light of varying intensities through a 25 km fiber-optic channel. We use spectrally resolved quantum process tomography with a newly developed Bayesian reconstruction method to estimate the quantum channel from experimental data, both with and without classical noise. Furthermore, we show that the coexistent fiber-based quantum channel has high process fidelity with an ideal depolarizing channel if the noise is dominated by Raman scattering. These results aid future development of quantum repeater designs and quantum error-correcting codes which benefit from realistic channel error models.

Chapman, Joseph↗
DOE OSTI2020

Visualizing subcellular rearrangements in intact β cells using soft x-ray tomography

Characterizing relationships between cell structures and functions requires mesoscale mapping of intact cells showing subcellular rearrangements following stimulation; however, current approaches are limited in this regard. Here, we report a unique application of soft x-ray tomography to generate three-dimensional reconstructions of whole pancreatic β cells at different time points following glucose-stimulated insulin secretion. Reconstructions following stimulation showed distinct insulin vesicle distribution patterns reflective of altered vesicle pool sizes as they travel through the secretory pathway. Our results show that glucose stimulation caused rapid changes in biochemical composition and/or density of insulin packing, increased mitochondrial volume, and closer proximity of insulin vesicles to mitochondria. Costimulation with exendin-4 (a glucagon-like peptide-1 receptor agonist) prolonged these effects and increased insulin packaging efficiency and vesicle maturation. This study provides unique perspectives on the coordinated structural reorganization and interactions of organelles that dictate cell responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Ultrastructure of Organohalide-Respiring Dehalococcoidia Revealed by Cryo-Electron Tomography

Dehalococcoides mccartyi (Dhc) and Dehalogenimonas spp. (Dhgm) are members of the class Dehalococcoidia, phylum Chloroflexi, characterized by streamlined genomes and a strict requirement for organohalogens as electron acceptors. Here, we used cryo-electron tomography to reveal morphological and ultrastructural features of Dhc strain BAV1 and “Candidatus Dehalogenimonas etheniformans” strain GP cells at unprecedented resolution. Dhc cells were irregularly shaped discs (890 ± 110 nm long, 630 ± 110 nm wide, and 130 ± 15 nm thick) with curved and straight sides that intersected at acute angles, whereas Dhgm cells appeared as slightly flattened cocci (760 ± 85 nm). The cell envelopes were composed of a cytoplasmic membrane (CM), a paracrystalline surface layer (S-layer) with hexagonal symmetry and ~22-nm spacing between repeating units, and a layer of unknown composition separating the CM and the S-layer. Cell surface appendages were only detected in Dhc cells, whereas both cell types had bundled cytoskeletal filaments. Repetitive globular structures, ~5 nm in diameter and ~9 nm apart, were observed associated with the outer leaflet of the CM. We hypothesized that those represent organohalide respiration (OHR) complexes and estimated ~30,000 copies per cell. In Dhgm cultures, extracellular lipid vesicles (20 to 110 nm in diameter) decorated with putative OHR complexes but lacking an S-layer were observed. Furthermore, the new findings expand our understanding of the unique cellular ultrastructure and biology of organohalide-respiring Dehalococcoidia.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Cryogenic electron tomography reveals novel structures in the apical complex of Plasmodium falciparum

Intracellular infectious agents, like the malaria parasite, Plasmodium falciparum, face the daunting challenge of how to invade a host cell. This problem may be even harder when the host cell in question is the enucleated red blood cell, which lacks the host machinery co-opted by many pathogens for internalization. Evolution has provided P. falciparum and related single-celled parasites within the phylum Apicomplexa with a collection of organelles at their apical end that mediate invasion. This apical complex includes at least two sets of secretory organelles, micronemes and rhoptries, and several structural features like apical rings and a putative pore through which proteins may be introduced into the host cell during invasion. We perform cryogenic electron tomography (cryo-ET) equipped with Volta Phase Plate on isolated and vitrified merozoites to visualize the apical machinery. Through tomographic reconstruction of cellular compartments, we see new details of known structures like the rhoptry tip interacting directly with a rosette resembling the recently described rhoptry secretory apparatus (RSA), or with an apical vesicle docked beneath the RSA. Subtomogram averaging reveals that the apical rings have a fixed number of repeating units, each of which is similar in overall size and shape to the units in the apical rings of tachyzoites of Toxoplasma gondii. Comparison of these polar rings in Plasmodium and Toxoplasma parasites also reveals them to have a structurally conserved assembly pattern. These results provide new insight into the essential and structurally conserved features of this remarkable machinery used by apicomplexan parasites to invade their respective host cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Surface-wave tomography of the Emeishan large igneous province (China): Magma storage system, hidden hotspot track, and its impact on the Capitanian mass extinction

Large igneous provinces (LIPs) are commonly associated with mass extinctions. However, the precise relations between LIPs and their impacts on biodiversity is enigmatic, given that they can be asynchronous. It has been proposed that the environmental impacts are primarily related to sill emplacement. Therefore, the structure of LIPs' magma storage system is critical because it dictates the occurrence and timing of mass extinction. We use surface-wave tomography to image the lithosphere under the Permian Emeishan large igneous province (ELIP) in southwestern China. We find a northeast-trending zone of high shear-wave velocity (Vs) and negative radial anisotropy (Vsv > Vsh; v and h are vertically and horizontally polarized S waves, respectively) in the crust and lithosphere. We rule out the possibilities of rifting or orogenesis to explain these seismic characteristics and interpret the seismic anomaly as a mafic-ultramafic, dike-dominated magma storage system of the ELIP. We further propose that the anomaly represents a hidden hotspot track that was emplaced before the ELIP eruption. A zone of higher velocity but less-negative radial anisotropy, on the hotspot track but to the northeast of the eruption center in the Panxi region, reflects an elevated proportion of sills emplaced at the incipient stage of the ELIP. Liberation of poisonous gases by the early sill intrusions explains why the mid-Capitanian global biota crisis preceded the peak ELIP eruption by 2–3 m.y.

58 GEOSCIENCES↗
DOE OSTI2023

Deep learning assisted jet tomography for the study of Mach cones in QGP

Abstract Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the propagation direction of the parton shower relative to the radial flow because of the distortion caused by the collective expansion of the QGP and the large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and parton-shower propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different path lengths and orientations relative to the radial flow of the QGP in heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Real-time High-resolution X-Ray Computed Tomography

Computed Tomography (CT) serves as a key imaging technology that relies on computationally intensive filtering and back-projection algorithms for 3D image reconstruction. While conventional high-resolution image reconstruction (> 2K3) solutions provide quick results, they typically treat reconstruction as an offline workload to be performed remotely on large-scale HPC systems. The growing demand for post-construction AI-driven analytics and the need for real-time adjustments call for high-resolution reconstruction solutions that are feasible on local computing resources, i.e. a multi-GPU server at most. In this paper, we propose a novel approach that utilizes Tensor Cores to optimize image reconstruction without sacrificing precision. We also introduce a framework designed to enable real-time execution of end-to-end distributed image reconstruction in a multi-GPU environment. Evaluations conducted on a single Nvidia A100 and H100 GPU show performance improvements of 1.91 × and 2.15 × compared to highly optimized production libraries. Furthermore, our framework, when deployed on 8-card Nvidia A100 GPU system, demonstrates the ability to reconstruct real-world datasets into 20483 volumes (32 GB) in slightly more than one minute and 40963 volumes (256 GB) in 7 minutes.

Wu, Du↗
DOE OSTI2025

Soft X-Ray Tomography Has Evolved into a Powerful Tool for Revealing Cell Structures

Over the past three decades, soft X-ray tomography (SXT) has rapidly evolved from a proof-of-concept microscopy method into a high-throughput quantitative imaging modality. This advancement enables researchers to address central questions in cell biology. Despite its relatively short developmental period compared to light and electron microscopy, SXT has emerged as a powerful imaging technology. It enables measuring chemical changes in cellular organelles, analyzing three-dimensional structures of whole cells and creating digital cellular models to study cell motility. We discuss the unique nature of SXT to visualize cells without fixation or labeling, enabling quantitative analyses of organelle chemical composition. We explore SXT microscopes available worldwide, SXT segmentation software, and the diverse cell types studied using this technique. We conclude with emerging directions in SXT imaging, including a brief discussion of recent discoveries that are highly influential and likely to become integral to cell biology textbooks.

Weinhardt, Venera↗
DOE OSTI2021

TomoTwin: A simple digital twin for synchrotron tomography

A digital twin for synchrotron-based tomography for generating synthetic datasets from ground-truth phantoms. The code outputs greyscale projection images / tomographic volumes with realistic artifacts such as noise and phase-contrast and emulates their dependence on the acquisition parameters defining the beam and detector configuration. Some ground-truth phantoms are included with the code.

TEKAWADE, ANIKET↗
DOE OSTI2021

TomoEncoders: 3D Autoencoders for feature extraction in X-ray tomography

Real-time steering of time-resolved or in-situ X-ray tomography requires capturing changes in morphological descriptors in a sample (e.g., porosity, particle size, and crack width) during continuous data acquisition. Image segmentation (2D or3D) followed by quantitative measurement is the conventional method for tracking changes in these descriptors with respect to a previous time-step or a 3D search in a volume. However, image segmentation is expensive. As a faster and unsupervised alternative, a feature-extraction approach using a convolutional autoencoders was developed, where the latent space of the encoder responds to relative changes in morphology with-out prior knowledge of the morphological descriptors.

TEKAWADE, ANIKET↗
DOE OSTI2022

Tomo2Mesh: Fast Reconstruction and Visualization of Tomography Data in Mesh Format

Tomo2Mesh is an open-source project targeted towards real-time reconstruction, segmentation, and visualization of computed tomography (CT) data in mesh format. The CT reconstruction scheme is based on filtered back-projection of voxel subsets. The segmentation scheme uses a 3D convolutional neural network. To allow for fast, real-time reconstruction, voxel subsets are first identified by coarse reconstruction. The detail in specific regions of interest is improved through full reconstruction of voxel subsets in that region. Data structures are implemented to store and process voxel subsets. Finally, voxel data is labeled using connected components to detect disconnected regions such as voids whose morphological attributes (e.g., Feret diameter, principal axis orientation, local number density, size, etc.) can be measured also in real-time. Finally, a fast marching cubes implementation processes labeled voxel data into a triangular face mesh (vertices and faces) in .ply format for visualization in Paraview or other mesh visualization tools. The code provides a simple programming interface for detecting, classifying, and visualizing regions of interest based on morphology. For example, detected voids can be classified as round pores or extended cracks. Highly porous neighborhoods can be identified based on local number density. The mesh texture (or color) is assigned based these morphological attributes to allow smart visualization scenarios in real-time (e.g., show only long cracks). At the time of first release (July 2022), extraction of face mesh for visualization for raw CT data from a 2 megapixel camera would take between 1-5 minutes for most scenarios.

TEKAWADE, ANIKET↗