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DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗

DOE OSTI2021

Bragg Coherent Diffraction Imaging for In Situ Studies in Electrocatalysis

Electrocatalysis is at the heart of a broad range of physicochemical applications that play an important role in the present and future of a sustainable economy. Among the myriad of different electrocatalysts used in this field, nanomaterials are of ubiquitous importance. An increased surface area/volume ratio compared to bulk makes nanoscale catalysts the preferred choice to perform electrocatalytic reactions. Bragg coherent diffraction imaging (BCDI) was introduced in 2006 and since has been applied to obtain 3D images of crystalline nanomaterials. BCDI provides information about the displacement field, which is directly related to strain. Lattice strain in the catalysts impacts their electronic configuration and, consequently, their binding energy with reaction intermediates. Even though there have been significant improvements since its birth, the fact that the experiments can only be performed at synchrotron facilities and its relatively low resolution to date (~10 nm spatial resolution) have prevented the popularization of this technique. Herein, we will briefly describe the fundamentals of the technique, including the electrocatalysis relevant information that we can extract from it. Subsequently, we review some of the computational experiments that complement the BCDI data for enhanced information extraction and improved understanding of the underlying nanoscale electrocatalytic processes. We next highlight success stories of BCDI applied to different electrochemical systems and in heterogeneous catalysis to show how the technique can contribute to future studies in electrocatalysis. Finally, we outline current challenges in spatiotemporal resolution limits of BCDI and provide our perspectives on recent developments in synchrotron facilities as well as the role of machine learning and artificial intelligence in addressing them.

bragg coherent diffraction imaging↗

DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗

DOE OSTI2022

Handheld multi-sensor biometric imaging device and processing pipeline

A handheld biometric imaging device having an array of cameras configured to simultaneously capture face, iris and fingerprint biometrics of a subject. The device includes a plurality of visible-light cameras and a plurality of infrared-light cameras capable of being triggered simultaneously to obtain a plurality of images from which a 3D image of the light field can be constructed. The device includes a plurality of visible-light illuminators and a plurality of infrared-light illuminators that allow images of a subject to be captured under different lighting profiles. The device may include an onboard control system that is capable of reconstructing a face region, an iris region and a fingerprint region from the 3D light-field image, and then extract a corresponding face template, an iris template, and a fingerprint template from the respective reconstructed regions. The extracted face, iris, and fingerprint templates may be used for enrollment and/or comparison against a biometric-template database associated with one or more watchlists.

Bolme, David S.↗

DOE OSTI2023

Ultrasparse View X-ray Computed Tomography for 4D Imaging

X-ray computed tomography (CT) is a noninvasive, nondestructive approach to imaging materials, material systems, and engineered components in two and three dimensions. Acquisition of three-dimensional (3D) images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such 3D data acquisition strategies commonly involve suboptimal temporal sampling for in situ and operando studies (4D imaging). Herein, we introduce a sparse-view imaging approach, Tomo-NeRF, which is capable of reconstructing high-fidelity 3D images from <10 twodimensional radiographic images. Experimental 2D and 3D X-ray images were used to test the reconstruction capability in two-view, four-view, and six-view scenarios. Tomo-NeRF is capable of reconstructing 3D images with a structural similarity of 0.9971–0.9975 and a voxel-wise accuracy of 81.83–89.59% from 2D experimentally obtained images. Furthermore, the reconstruction accuracy for the experimentally obtained images is less than the synthetic structures. Experimentally obtained images demonstrate a similarity of 0.9973–0.9984 and a voxelwise accuracy of 84.31–95.77%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Image Reconstruction from Sparse-view Data Acquired with Portable X-ray Devices

• Portable X-ray systems enable on-site 3D imaging for non-invasive inspection of suspicious packages and explosives. • Existing reconstruction algorithms (e.g., FDK or Feldkamp, Davis and Kress) require hundreds of projections over 360 degrees. • Sparse-view scan reduces scanning time and setup effort, making it ideal for field use in timecritical scenarios. • Existing reconstruction algorithms introduce severe artifacts when applied to sparse-view data. • We developed a total variation (TV)-based optimization algorithm for yielding 3D images from sparse-view data collected with our portable X-ray imaging system.

Xia, Dan [University of Chicago, Chicago, IL]↗

DOE OSTI2024

T staging esophageal tumors with x rays

With histopathology results typically taking several days, the ability to stage tumors during interventions could provide a step change in various cancer interventions. X-ray technology has advanced significantly in recent years with the introduction of phase-based imaging methods. These have been adapted for use in standard labs rather than specialized facilities such as synchrotrons, and approaches that enable fast 3D scans with conventional x-ray sources have been developed. This opens the possibility to produce 3D images with enhanced soft tissue contrast at a level of detail comparable to histopathology, in times sufficiently short to be compatible with use during surgical interventions. In this paper we discuss the application of one such approach to human esophagi obtained from esophagectomy interventions. We demonstrate that the image quality is sufficiently high to enable tumor T staging based on the x-ray datasets alone. Alongside detection of involved margins with potentially life-saving implications, staging tumors intra-operatively has the potential to change patient pathways, facilitating optimization of therapeutic interventions during the procedure itself. Besides a prospective intra-operative use, the availability of high-quality 3D images of entire esophageal tumors can support histopathological characterization, from enabling “right slice first time” approaches to understanding the histopathology in the full 3D context of the surrounding tumor environment.

Optics↗

DOE OSTI2020

Volumetric analysis and mesh generation of real and artificial microstructural geometries

Producing a viable finite element mesh of realistic microstructural structural geometry is a critical step in analyzing the thermo-mechanical behavior of complex multi-material composites. Advancements in imaging technology such as micro computed tomography have allowed modelers to access high resolution mesoscale geometries for direct numerical simulation. However, converting from voxel based 3D images to usable finite element meshes has been challenging. A robust method including algorithms and software scripts for generating finite element meshes from 3D imaged microstructures is presented in this paper. It includes a routine for inserting cohesive elements around material interfaces to enable modeling of interface properties including delamination and damage. The algorithms and procedures presented in this method leverage currently available software packages for processing surface based geometry into volume based meshes. In addition to converting real geometry from physical imaging systems, algorithms for producing numerically generated and statistically equivalent microstructural geometry are also included. These artificial microstructures can be a valuable resource for modelers when physical specimens do not exist or are limited in quantity: Method establishes a workflow from voxel data to viable finite element mesh including interface information, and Includes a method for synthetic geometry generation based on metrics from real microstructures.

36 MATERIALS SCIENCE↗

DOE OSTI2020

High Speed In-situ X-ray Imaging of 3D Freeze Printing of Aerogels

3D freeze printing (3DFP) combines drop-on-demand (DOD) inkjet printing with freeze casting to fabricate lightweight and multifunctional aerogels with customized geometries. Freeze casting is an efficient and easily implemented method capable of fabricating porous, sponge-like structures for many different applications. This process enables tailoring the microstructure of the final product (i.e., pore morphology, alignment, average size distribution, etc.) by controlling the fabrication conditions and freezing kinetics. Furthermore, its combination with DOD printing provides the capability of engineering the macrostructure without relying on a mold as reported for 3D freeze-printed aerogels made from graphene, silver nanowires, and other nanocomposites. In this paper, we performed in-situ X-ray imaging to understand the inside process dynamics in 3DFP using a commercially available colloidal silica ink. We investigated the 3DFP process with the following hierarchy: first, single droplets; then, uniform lines obtained from coalescence of droplets; and finally, three consecutive lines deposited layer by layer. With the help of X-ray imaging, the importance of the balance between material deposition and freezing rates was shown in-situ by the observation inside of the freeze front following the tip of the printed line. The effects of the substrate temperature on the elimination of undesired interfacial boundaries were also shown by the observed ice crystals penetrating from lower to upper layer.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A holey cavity for single-transducer 3D ultrasound imaging with physical optimization

Within the compressive sensing (CS) framework, one effective way to increase the likelihood of successful signal reconstruction is to employ random processes in the construction of the sensing matrix. This work presents a 3D holey cavity, with diverse frequency modes, to spectrally code, that is, randomize, the ultrasound wave fields. The simulated results show that the use of such a cavity enables imaging simple or complex targets, such as spheres or the letter E, by only a single transceiver—something that is not possible without the use of a coding structure like the cavity. The effect of noise on imaging results and the size of the targets on the first-order Born approximation (BA) are also investigated. Moreover, this study attempts to optimize the cavity, based on a single numerical metric, such as the sum of singular values (SSV) or mutual coherence (MC). Yet, it will be shown that neither of these metrics can consistently compare the norm-one imaging performance between two cavities of different materials or hole sizes. This leaves finding a quantitative metric for these kinds of optimizations an open problem.

42 ENGINEERING↗

DOE OSTI2023

Concurrent multi-peak Bragg coherent x-ray diffraction imaging of 3D nanocrystal lattice displacement via global optimization

Abstract In this paper we demonstrated a method to reconstruct vector-valued lattice distortion fields within nanoscale crystals by optimization of a forward model of multi-reflection Bragg coherent diffraction imaging (MR-BCDI) data. The method flexibly accounts for geometric factors that arise when making BCDI measurements, is amenable to efficient inversion with modern optimization toolkits, and allows for globally constraining a single image reconstruction to multiple Bragg peak measurements. This is enabled by a forward model that emulates the multiple Bragg peaks of a MR-BCDI experiment from a single estimate of the 3D crystal sample. We present this forward model, we implement it within the stochastic gradient descent optimization framework, and we demonstrate it with simulated and experimental data of nanocrystals with inhomogeneous internal lattice displacement. We find that utilizing a global optimization approach to MR-BCDI affords a reliable path to convergence of data which is otherwise challenging to reconstruct.

36 MATERIALS SCIENCE↗

DOE OSTI2022

High-Energy X-ray Diffraction Microscopy for Nuclear Forensics FY2022 Project Report

Morphological information on nuclear material has been identified using visible light and scanning electron microscopy. These identify qualitative differences in particle morphology. Three-dimensional imaging of materials through alternating scanning electron microscopy imaging and focused ion beam milling has also been used. Unfortunately, these techniques are time- and labor-intensive, with significant sample preparation required and lengthy analysis times. Further, the resulting 3D images are qualitative, require manual identification, and do not capture statistically-representative populations. High energy X-ray 3D imaging using a direct-beam or diffracted-beam (High-Energy Diffraction Microscopy) have been developed at the Advanced Photon Source and can produce quantitative information on grains (phase, location, etc.) and pores (size distribution, sphericity) in a material. These techniques require only minutes to characterize a sample volume and are non-destructive, thus suitable for a wide range of existing samples and for confirmatory analyses to be carried out using conventional microscopy techniques. In this first year of the project, all uranium oxide samples were synthesized and characterized using conventional analyses by the analytical chemistry laboratory. Conventional analysis methods included powder x-ray diffraction, scanning electron microscopy, impurity analysis via inductively coupled plasma mass spectrometry, and infrared spectroscopy. Impurity analysis shows a drop in boron content from UO 3 to the lowest U 3 O 8 calcination temperature, but otherwise no appreciable difference in any sample. Analysis of diffraction data shows a flip of peaks from UO 3 dominated for the 600 °C calcined sample to U 3 O 8 dominated at 700 °C and 800 °C. Analysis of scanning electron microscopy images shows that with increased calcination temperature the size distribution of particles seems to increase and broaden. Both of these last findings are in line with previously published data, though this work used significantly fewer particles to simply show similar trends instead of getting truly quantitative particle analysis. Infrared analysis similarly shows ingrowth of U 3 O 8 as calcination temperature is increased, along with depression of peaks associated with UO 3 and water. Samples were prepared for analysis at the Advanced Photon Source at beamline 1-ID. It is anticipated that analysis will occur in November of 2022. AI/ML techniques to de-noise data coming out of 1-ID during the analyses was also developed during this time using previously gathered data. Preliminary results using a self-supervision technique called Noise2Selfshow good de-noising of data. Once the uranium oxide samples are analyzed, real data will be used to test the de-noising and other AI/ML techniques that may be developed in the second year of the project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

High-Energy X-ray Diffraction Microscopy for Nuclear Forensics (FY23 Project Report)

36 MATERIALS SCIENCE↗

DOE OSTI2022

Understanding FLiNaK Salt Intrusion Behavior on Nuclear-Grade Graphite via Neutron Tomography

Graphite is an essential material as a neutron moderator in molten salt reactors (MSRs). To understand the impact of salt on the graphite structure to develop structural materials for MSRs, molten salt intrusion behavior on nuclear-grade graphite was studied. The graphite samples, IG-110 and PCEA, were tested for infiltration with LiF-NaF-KF (FLiNaK) at 750°C, 5 bar for 12 h, and, after the intrusion experiment, the graphite was analyzed by neutron imaging. The graphite and Li from FLiNaK showed great contrast in the neutron attenuation coefficient. Thus, the salt behavior in the graphite structure has been visualized for the first time without damaging the sample. The 3D image of the graphite was reconstructed after a neutron computed tomography scan, and the average salt coverage distribution of the XY surface in different depths was obtained from the reconstructed 3D images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

LABQ3: Bayesian method for quantification of mineral compositions and nano-scale elemental mapping of 3D synchrotron XCT data

Quantitative analysis of mineral compositions is essential in understanding geochemical, mineralogical and environmental processes. Fine-resolution 3D imaging is widely done using synchrotron X-ray computed tomography (XCT), but existing analyses are limited to visualization and segmentation. This paper presents a new method, Linear Attenuation Bayesian Quantitative 3D-mapper (LABQ3), based on the linearity of X-ray attenuation with respect to elemental concentrations. To address the random variability in attenuation measurements, LABQ3 employs Bayesian decision theory to minimize classification error, using reference attenuation distributions from scans of pure mineral standards. To demonstrate LABQ3 and test its performance, we studied precipitated carbonate samples. XCT scans were done at multiple energies using the transmission X-ray microscope (TXM) at beamline 32-ID-C of the Advanced Photon Source at Argonne National Laboratory. The reconstructed 3D images have a voxel size of 20 nm. Analyses revealed rich nano-scale compositional heterogeneity within individual particles. A mixture of calcium and cadmium produced an overall stoichiometric composition of (Ca 0.78 ,Cd 0.22 )CO 3 , with some voxels containing nearly pure CdCO 3 . The addition of zinc led to an overall stoichiometric composition of 33% Ca, 28% Cd, 39% Zn, with a nearly pure CaCO 3 core and compositional zonation through the rim. These compositional gradients are related to temporal sequences of carbonate mineral formation where Cd precipitated at the beginning in (Ca,Cd)CO 3 , while Cd and Zn precipitated at the end in (Ca, Cd,Zn)CO 3 . Results differ from bulk analyses using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), showing that LABQ3 provides particle-specific insights. LABQ3 distinguishes itself by quantifying chemical compositions along a continuum, making it different from XCT analyses based on segmentation. LABQ3 allows simultaneous acquisition of morphology and chemical composition in 3D, facilitating the interpretation of chemical gradients of trace elements, quantification of solid solution compositions, inferences about temporal sequences of mineral precipitation, and addressing other concerns about solid-phase chemistry.

58 GEOSCIENCES↗

DOE OSTI2026

3D seismic imaging of a fracture damage zone controlling reservoir compartmentalization at the Raft River EGS using multi-azimuth walkaway VSP

Accurate imaging of steeply dipping fracture zones in crystalline enhanced geothermal systems (EGS) is critical for constraining permeability architecture and guiding stimulation design. However, such structures remain poorly resolved by conventional surface seismic methods. We present a fully three-dimensional (3D) elastic-waveform inversion-migration workflow applied to multi-azimuth walkaway vertical seismic profiling (VSP) data acquired in a deviated borehole at the Raft River EGS. The workflow integrates first-arrival traveltime tomography, multi-scale elastic waveform inversion (EWI), and elastic least-squares reverse-time migration (ELSRTM) to recover high-resolution compressional-and shear-wave velocity models and to image structural discontinuities in the crystalline basement. The results reveal a laterally continuous low-velocity anomaly, with shear-wave velocity reductions of 25-30%, consistent with fractureinduced mechanical weakening. Two steeply dipping discontinuities bound a 50-80 m wide fracture damage zone. Independent constraints from microseismic clustering and geochemical compartmentalization corroborate the geometry and structural significance of this feature. Synthetic modeling further confirms that structures of this scale are resolvable (~30 m). These findings indicate that the Narrows structure is a distributed fracture damage zone rather than a discrete fault plane. By resolving fracture-zone geometry at the tens-of-meters scale using a single borehole, this workflow provides a practical and transferable approach for improving structural characterization, reducing uncertainty in permeability architecture, and supporting reservoir modeling and stimulation design in fractured crystalline EGS reservoirs.

58 GEOSCIENCES↗

DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗

DOE OSTI2020

3D diffractive imaging of nanoparticle ensembles using an x-ray laser

Single particle imaging at x-ray free electron lasers (XFELs) has the potential to determine the structure and dynamics of single biomolecules at room temperature. Two major hurdles have prevented this potential from being reached, namely, the collection of sufficient high-quality diffraction patterns and robust computational purification to overcome structural heterogeneity. We report the breaking of both of these barriers using gold nanoparticle test samples, recording around 10 million diffraction patterns at the European XFEL and structurally and orientationally sorting the patterns to obtain better than 3-nm-resolution 3D reconstructions for each of four samples. With these new developments, integrating advancements in x-ray sources, fast-framing detectors, efficient sample delivery, and data analysis algorithms, we illuminate the path towards sub-nanometer biomolecular imaging. The methods developed here can also be extended to characterize ensembles that are inherently diverse to obtain their full structural landscape.

47 OTHER INSTRUMENTATION↗