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Results for “3D image processing”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images

Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Automatic Detection, Segmentation and Classification of Retinal Horizontal Neurons in Large-scale 3D Confocal Imagery

Automatic analysis of neuronal structure from wide-field-of-view 3D image stacks of retinal neurons is essential for statistically characterizing neuronal abnormalities that may be causally related to neural malfunctions or may be early indicators for a variety of neuropathies. In this paper, we study classification of neuron fields in large-scale 3D confocal image stacks, a challenging neurobiological problem because of the low spatial resolution imagery and presence of intertwined dendrites from different neurons. We present a fully automated, four-step processing approach for neuron classification with respect to the morphological structure of their dendrites. In our approach, we first localize each individual soma in the image by using morphological operators and active contours. By using each soma position as a seed point, we automatically determine an appropriate threshold to segment dendrites of each neuron. We then use skeletonization and network analysis to generate the morphological structures of segmented dendrites, and shape-based features are extracted from network representations of each neuron to characterize the neuron. Based on qualitative results and quantitative comparisons, we show that we are able to automatically compute relevant features that clearly distinguish between normal and abnormal cases for postnatal day 6 (P6) horizontal neurons.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

System and method for generating micro-seismic events and characterizing properties of a medium with non-linear acoustic interactions

A method and system includes generating a first coded acoustic signal including pulses each having a modulated signal at a central frequency; and a second coded acoustic signal each pulse of which includes a modulated signal a central frequency of which is a fraction d of the central frequency of the modulated signal for the corresponding pulse in the first plurality of pulses. A receiver detects a third signal generated by a non-linear mixing process in the mixing zone and the signal is processed to extract the third signal to obtain an emulated micro-seismic event signal occurring at the mixing zone; and to characterize properties of the medium or creating a 3D image of the properties of the medium, or both, based on the emulated micro-seismic event signal.

58 GEOSCIENCES↗
DOE OSTI2025

Investigation of the temperature-dependent failure processes in PVD Cr-coated ZIRLO nuclear fuel cladding using in situ X-ray micro-tomography imaging

In this work, an accident tolerant fuel cladding system with Cr coating deposited using physical vapour deposition (PVD) method onto commercial Optimized ZIRLO™ was studied. The cladding tubes were machined into C-rings and loaded to failure under compression with real-time synchrotron micro-X-ray computed tomography (XCT) imaging at room temperature (RT), 345 °C, 650 °C and 950 °C in argon (Ar) atmosphere. The mechanical behaviour and failure processes were found strongly temperature-dependent where the Cr coating showed brittle fracture at RT and 345 °C, ductile fracture at 650 °C and a reversion to brittle fracture at 950 °C. Nanoindentation measurements and scanning electron microscopy (SEM) imaging were conducted on the materials after high temperature testing. It was found that recrystallisation of the Cr coating occurred at elevated temperatures, which significantly affected its local properties hence the failure behaviour at different temperatures. This work represents the first in situ 3D XCT observation of progressive failure processes in PVD Cr-coated ZIRLO claddings up to 950 °C providing critical insights into its brittle-to-ductile transition (BDTT) behaviour and subsequent ductile-to-brittle reversion with the increase in temperature. Moreover, results are compared with other PVD Cr-coated Zircaloy materials from open literature; the influences of the coating microstructure and local properties on the failure stress/strain and fracture processes are discussed.

Yuan, Guanjie↗
DOE OSTI2026

Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials

Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Calculation of grain boundary normals directly from 3D microstructure images

The determination of grain boundary normals is an integral part of the characterization of grain boundaries in polycrystalline materials. These normal vectors are difficult to quantify due to the discretized nature of available microstructure characterization techniques. The most common method to determine grain boundary normals is by generating a surface mesh from an image of the microstructure, but this process can be slow, and is subject to smoothing issues. A new technique is proposed, utilizing first order Cartesian moments of binary indicator functions, to determine grain boundary normals directly from a voxelized microstructure image. In order to validate the accuracy of this technique, the surface normals obtained by the proposed method are compared to those generated by a surface meshing algorithm. Specifically, the local divergence between the surface normals obtained by different variants of the proposed technique and those generated from a surface mesh of a synthetic microstructure constructed using a marching cubes algorithm followed by Laplacian smoothing is quantified. Next, surface normals obtained with the proposed method from a measured 3D microstructure image of a Ni polycrystal are used to generate grain boundary character distributions (GBCD) for Σ3 and Σ9 boundaries, and compared to the GBCD generated using a surface mesh obtained from the same image. Finally, the results show that the proposed technique is an efficient and accurate method to determine voxelized fields of grain boundary normals.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Using resonant soft x-ray scattering to image patterns on undeveloped resists

Copyright © 2018 SPIE. Extreme ultraviolet lithography is one of the most promising printing techniques for high volume semiconductor manufacturing at the 14 nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials such as the exposure-dose sensitivity or the line-width roughness continue to impede the full adoption into industrial nanofab facilities. New metrology tools are required to address these challenges by helping to determine the impact of the EUV materials' properties and processing conditions on the roughness through the different step of the process. Here, we apply the resonant soft x-ray scattering (RSOXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon absorption edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with sub-nm precision. Critical-dimension grazing incidence small-angle X-ray scattering (CDGISAXS) was then performed at energies where the contrast was maximized, enabling the reconstruction of the 3D shape of the latent image. This demonstrates the potential of RSOXS to provide a high-resolution heightsensitive profile of patterned EUV resists, which will help to quantify the evolution of critical features, such as the line edge roughness, at each step of the nanofabrication process.

Pandolfi, Ronald J↗
DOE OSTI2024

Temporal and spatial resolution of magnetosome degradation at the subcellular level in a 3D lung carcinoma model

Magnetic nanoparticles offer many exciting possibilities in biomedicine, from cell imaging to cancer treatment. One of the currently researched nanoparticles are magnetosomes, magnetite nanoparticles of high chemical purity synthesized by magnetotactic bacteria. Despite their therapeutic potential, very little is known about their degradation in human cells, and even less so of their degradation within tumours. In an effort to explore the potential of magnetosomes for cancer treatment, we have explored their degradation process in a 3D human lung carcinoma model at the subcellular level and with nanometre scale resolution. We have used state of the art hard X-ray probes (nano-XANES and nano-XRF), which allow for identification of distinct iron phases in each region of the cell. Our results reveal the progression of magnetite oxidation to maghemite within magnetosomes, and the biosynthesis of magnetite and ferrihydrite by ferritin.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enabling dynamic 3D coherent diffraction imaging via adaptive latent space tuning of generative autoencoders

Abstract Coherent diffraction imaging (CDI) is an advanced non-destructive 3D X-ray imaging technique for measuring a sample’s electron density. The main challenge of CDI is loss of phase information in diffraction intensity measurements, resulting in lengthy iterative reconstruction processes that can return non-unique solutions, which pose challenges for experiments attempting to track dynamic sample evolution through multiple states. As the increased brightness of fourth-generation light sources enables faster sample measurements and drives operando experiments with Bragg CDI, there is a growing need for faster reconstruction techniques that can keep pace. We have developed an adaptive generative autoencoder approach for uniquely tracking a sample’s electron density as it dynamically evolves. Our approach adaptively tunes the low-dimensional latent embedding of a generative autoencoder, enabling a computationally efficient manner to account for time-varying shifting distributions in real-time. Analytic proof of convergence is provided as well as numerical demonstration of sample tracking with noisy measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

MO-C-17A-03: A GPU-Based Method for Validating Deformable Image Registration in Head and Neck Radiotherapy Using Biomechanical Modeling

Purpose: Deformable image registration (DIR) plays a pivotal role in head and neck adaptive radiotherapy but a systematic validation of DIR algorithms has been limited by a lack of quantitative high-resolution groundtruth. We address this limitation by developing a GPU-based framework that provides a systematic DIR validation by generating (a) model-guided synthetic CTs representing posture and physiological changes, and (b) model-guided landmark-based validation. Method: The GPU-based framework was developed to generate massive mass-spring biomechanical models from patient simulation CTs and contoured structures. The biomechanical model represented soft tissue deformations for known rigid skeletal motion. Posture changes were simulated by articulating skeletal anatomy, which subsequently applied elastic corrective forces upon the soft tissue. Physiological changes such as tumor regression and weight loss were simulated in a biomechanically precise manner. Synthetic CT data was then generated from the deformed anatomy. The initial and final positions for one hundred randomly-chosen mass elements inside each of the internal contoured structures were recorded as ground truth data. The process was automated to create 45 synthetic CT datasets for a given patient CT. For instance, the head rotation was varied between +/− 4 degrees along each axis, and tumor volumes were systematically reduced up to 30%. Finally, the original CT and deformed synthetic CT were registered using an optical flow based DIR. Results: Each synthetic data creation took approximately 28 seconds of computation time. The number of landmarks per data set varied between two and three thousand. The validation method is able to perform sub-voxel analysis of the DIR, and report the results by structure, giving a much more in depth investigation of the error. Conclusions: We presented a GPU based high-resolution biomechanical head and neck model to validate DIR algorithms by generating CT equivalent 3D volumes with simulated posture changes and physiological regression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Controlled activation of ZnTe:Cu contacted CdTe solar cells using rapid thermal processing

Back contacts can significantly limit CdTe solar cell performance, reducing both open circuit voltage (Voc) and fill factor (FF). Copper is an essential component of effective back contacts, but its presence in the CdTe absorber creates detrimental recombination centers. Rapid thermal processing (RTP) is demonstrated as a highly effective approach for reducing back contact barriers in CdTe solar cells contacted with ZnTe:Cu buffer layers, substantially improving both FF (473%) and V oc (4850 mV). Current density and quantum efficiency remain essentially unchanged, but a five-fold increase in minority carrier lifetime is observed which is attributed to passivation of recombination sites in the back contact region. Quantitative analysis of secondary ion mass spectrometry shows that the majority of Cu segregates to the Au metallization layer and that the ZnTe buffer appears to inhibit the Cu diffusion into CdTe. 3D imaging of the back contact region using atom probe tomography shows that optimized devices are characterized by preferential segregation of copper to both the Au|ZnTe and CdTe|ZnTe interfaces, perhaps in the form of Cu x Te. Finally, with its low thermal budget the RTP process has been successfully applied to multiple device architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-F-T-61: Treatment Planning Observations for the CivaSheet Directional Brachytherapy Device Using VariSeed 9.0

Purpose: The VariSeed 9.0 brachytherapy TPS is recently available and has new features such as ability to rotate a brachytherapy source away from normal to the imaging plane. Consequently, a dosimetric analysis was performed for a directional brachytherapy source (CivaSheet) with tests of this functionality and experiences from clinical treatment planning were documented. These observations contribute to safe, practical, and accurate use of such new software features. Methods: Several tests were established to evaluate the new rotational feature, specific to the CivaSheet for the first patients treated using this new brachytherapy device. These included suitability of imaging slice-thickness and in-plane resolution, window/level adjustments for brachytherapy source visualization, commissioning the source physical length for performing rotations, and using different planar and 3D window views to identify source orientation. Additional CivaSheet-specific tests were performed to determine the dosimetric influence on target coverage: changing the source tilt angle, source positioning in the treatment plan based on the CivaSheet rectangular array of CivaDots, and influence of prescription depth on the necessary treatment margin for adequate target coverage. Results: Higher imaging-resolution produced better accuracy for source orientation and positioning, with sub-millimeter CT slice-thickness and in-plane resolution preferred. Source rotation was possible only in sagittal or coronal views. The process for validating source orientation required iteratively altering rotations then checking them in the 3D view, which was cumbersome given the absence of quantitative plan documentation to indicate orientation. Given the small Pd-103 source size, influence of source tilt within 30° was negligible for <1.0 cm. Influence of source position was important when the source was positioned in/out of the adjacent source plane, causing changes of 15%, 7%, and 3% at depths of 0.5, 0.7, and 1.0 cm. Conclusion: The new TPS rotational feature worked well, but several issues were identified to improve the treatment planning process. Research supported in part by CivaTech Oncology, Inc. for Dr. Rivard.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Morphological and Electrochemical Characterization of Nanostructured Li 4 Ti 5 O 12 Electrodes Using Multiple Imaging Mode Synchrotron X-ray Computed Tomography

In this study, synchrotron X-ray computed tomography has been utilized using two different imaging modes, absorption and Zernike phase contrast, to reconstruct the real three-dimensional (3D) morphology of nanostructured Li 4 Ti 5 O 12 (LTO) electrodes. The morphology of the high atomic number active material has been obtained using the absorption contrast mode, whereas the percolated solid network composed of active material and carbon-doped polymer binder domain (CBD) has been obtained using the Zernike phase contrast mode. The 3D absorption contrast image revealed that some LTO nano-particles tend to agglomerate and form secondary micro-sized particles with varying degrees of sphericity. The tortuosity of electrode’s pore and solid phases were found to have directional dependence, different from Bruggeman’s tortuosity commonly used in macro-homogeneous models. The electrode’s heterogeneous structure was investigated by developing a numerical model to simulate galvanostatic discharge process using the Zernike phase contrast mode. The inclusion of CBD in the Zernike phase contrast results in an integrated percolated network of active material and CBD that is highly suited for continuum modeling. As a result, the simulation results highlight the importance of using the real 3D geometry since the spatial distribution of physical and electrochemical properties have a strong non-uniformity due to microstructural heterogeneities.

25 ENERGY STORAGE↗
DOE OSTI2023

Facile hermetic TEM grid preparation for molecular imaging of hydrated biological samples at room temperature

Abstract Although structures of vitrified supramolecular complexes have been determined at near-atomic resolution, elucidating in situ molecular structure in living cells remains a challenge. Here, we report a straightforward liquid cell technique, originally developed for real-time visualization of dynamics at a liquid-gas interface using transmission electron microscopy, to image wet biological samples. Due to the scattering effects from the liquid phase, the micrographs display an amplitude contrast comparable to that observed in negatively stained samples. We succeed in resolving subunits within the protein complex GroEL imaged in a buffer solution at room temperature. Additionally, we capture various stages of virus cell entry, a process for which only sparse structural data exists due to their transient nature. To scrutinize the morphological details further, we used individual particle electron tomography for 3D reconstruction of each virus. These findings showcase this approach potential as an efficient, cost-effective complement to other microscopy technique in addressing biological questions at the molecular level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Synthetic fossilization of soft biological tissues and their shape-preserving transformation into silica or electron-conductive replicas

Structural preservation of complex biological systems from the subcellular to whole organism level in robust forms, enabling dissection and imaging while preserving 3D context, represents an enduring grand challenge in biology. Here we report a simple immersion method for structurally preserving intact organisms via conformal stabilization within silica. This self-limiting process, which we refer to as silica bioreplication, occurs by condensation of water-soluble silicic acid proximally to biomolecular interfaces throughout the organism. Conformal nanoscopic silicification of all biomolecular features imparts structural rigidity enabling the preservation of shape and nano-to-macroscale dimensional features upon drying to form a biocomposite and further high temperature oxidative calcination to form silica replicas or reductive pyrolysis to form electrically conductive carbon replicas of complete organisms. Ultimately, the simplicity and generalizability of this approach should facilitate efforts in biological preservation and analysis and could enable the development of new classes of biomimetic composite materials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

42 ENGINEERING↗