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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Computed Tomography Automation Architecture Developed from a Comprehensive Literature Review

This paper presents a literature review on techniques related to the computed tomography procedure that incorporate automation elements in their research investigations or industrial applications. Computed tomography (CT) is a non-destructive testing (NDT) technique in that the imaging and inspection are performed without damaging the sample, allowing for additional or repeated analysis if necessary. The reviewed literature is organized based on the steps associated with a general NDT task in order to define an end-to-end computed tomography automation architecture. The process steps include activities prior to image collection, during the scan, and after the data are collected. It further reviews efforts related to repeating this process based on a previous scan result. By analyzing the multiple existing but disparate efforts found in the literature, we present a framework for fully automating NDT procedures and discuss the remaining technical gaps in the developed framework.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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 OSTI2021

Microbial Community Characteristics Largely Unaffected by X-Ray Computed Tomography of Sediment Cores

X-ray computed tomography (CT) scanning is used to study the physical characteristics of soil and sediment cores, allowing scientists to analyze stratigraphy without destroying core integrity. Microbiologists often work with geologists to understand the microbial properties in such cores; however, we do not know whether CT scanning alters microbial DNA such that DNA sequencing, a common method of community characterization, changes as a result of X-ray exposure. Our objective was to determine whether CT scanning affects the estimates of the composition of microbial communities that exist in cores. Sediment cores were extracted from a salt marsh and then submitted for CT scanning. We observed a minimal effect of CT scanning on microbial community composition in the sediment cores either when the cores were examined shortly after recovery from the field or after the cores had been stored for several weeks. In contrast, properties such as sediment layer and marsh location did affect microbial community structure. While we observed that CT scanning did not alter microbial community composition as a whole, we identified a few amplicon sequence variants (13 out of 7,037) that showed differential abundance patterns between scanned and unscanned samples among paired sample sets. Our overall conclusion is that the CT-scanning conditions typically used to obtain images for geological core characterization do not significantly alter microbial community structure. We stress that minimizing core exposure to X-rays is important if cores are to be studied for biological properties. Future investigations might consider variables, such as the length and energy of radiation exposure, the volume of the core, or the degree, to which microbial communities are stressed as important factors in assessing the impact of X-rays on microbes in geological cores.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Correlative single-cell hard X-ray computed tomography and X-ray fluorescence imaging

Abstract X-ray computed tomography (XCT) and X-ray fluorescence (XRF) imaging are two non-invasive imaging techniques to study cellular structures and chemical element distributions, respectively. However, correlative X-ray computed tomography and fluorescence imaging for the same cell have yet to be routinely realized due to challenges in sample preparation and X-ray radiation damage. Here we report an integrated experimental and computational workflow for achieving correlative multi-modality X-ray imaging of a single cell. The method consists of the preparation of radiation-resistant single-cell samples using live-cell imaging-assisted chemical fixation and freeze-drying procedures, targeting and labeling cells for correlative XCT and XRF measurement, and computational reconstruction of the correlative and multi-modality images. With XCT, cellular structures including the overall structure and intracellular organelles are visualized, while XRF imaging reveals the distribution of multiple chemical elements within the same cell. Our correlative method demonstrates the feasibility and broad applicability of using X-rays to understand cellular structures and the roles of chemical elements and related proteins in signaling and other biological processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fourier method for 3-dimensional data fusion of X-ray Computed Tomography and ultrasound

X-ray Computed Tomography (CT) is essential for nondestructive inspection of many manufactured components but is susceptible to various forms of artifacts and noise. In particular, multi-detector row cone-beam CT systems can suffer from so called “cone-beam” artifacts and partial volume effects, particularly on planar edges at the periphery of the field of view in the cone angle dimension. Uni-directional ultrasonic testing methods generally have extremely accurate in-plane depth resolution but poorer lateral resolution due to physical and geometric constraints. This paper presents a novel technique called Computed Tomographic Fusion (CT-F) which uses three-dimensional Fourier filtering to combine x-ray cone-beam CT reconstructions and ultrasound data in the frequency space. The result is a single image with improved accuracy and quality. CT-F mitigates artifacts while allowing for rapid, accurate characterization of large three-dimensional CT volumes. Finally, using simulations and experiments, we demonstrate artifact reduction and edge contrast improvement in volumetric reconstructions.

42 ENGINEERING↗
DOE OSTI2021

Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography

Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.

Wang, Xiao↗
DOE OSTI2024

Laboratory-Based Micro-X-ray Computed Tomography of Energy Materials at Idaho National Laboratory

Abstract The Idaho National Laboratory (INL) has implemented laboratory-based micro-X-ray computed tomography in a laboratory equipped for the examination of highly radioactive samples. This capability provides nondestructive three-dimensional volumetric information on samples to inform subsequent traditional destructive examinations as well as real-world inputs for high-fidelity scientific modeling. Samples can be imaged with spatial resolutions ranging from several hundred nm/voxel up to ~ 100 µm/voxel. The best usable spatial resolution achieved to date is 384 nm/voxel with this instrument, while the highest radiological dose rate of a sample imaged is ~ 60 R/h β/γ on contact. Advanced data analysis, including custom tomographic reconstruction and segmentation methods, have also been developed to support this capability. In addition to traditional digital X-ray radiography and tomography, this instrument is also able to visualize in situ tensile and compression testing as well as perform diffraction contrast tomography. This work describes the X-ray computed tomography post-irradiation examination capabilities at INL, as well as detailing a variety of applications this instrument has examined.

36 MATERIALS SCIENCE↗
DOE OSTI2026

High-energy synchrotron X-ray multimodal computed tomography: enabling multiscale materials characterization at NSLS-II

We report the commissioning of a multimodal computed tomography experimental setup at the 28-ID-2 (XPD) beamline of the National Synchrotron Light Source II. This high-energy (>60 keV) resource features a tunable X-ray beam size ranging from several millimetres to a few micrometres and enables comprehensive characterization of high-Z materials—an essential capability for nuclear and advanced materials research. It provides four complementary computed tomography modalities: X-ray absorption, X-ray fluorescence, X-ray diffraction, and pair distribution function tomography. A case study using a custom-made heterogeneous sample demonstrates these abilities to simultaneously capture atomic, elemental, and morphological information. This unique combination of imaging, structural, and chemical sensitive methods provides a holistic approach to study complex materials with amorphous and crystalline systems across multiple length scales.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Limited-angle computed tomography with deep image and physics priors

Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. One major challenge in some applications, such as in electron or x-ray tomography systems, is that the projections cannot be gathered over all the angles due to the sample holder setup or shape of the sample. This results in an ill-posed problem called the limited angle reconstruction problem. Typical image reconstruction in this setup leads to distortion and artifacts, thereby hindering a quantitative evaluation of the results. To address this challenge, we use a generative model to effectively constrain the solution of a physics-based approach. Our approach is self-training that can iteratively learn the nonlinear mapping from partial projections to the scanned object. Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training. We also complement our approach with total-variation regularization to handle high-frequency noise in reconstructions and implement a solver based on alternating direction method of multipliers. We present numerical results for various degrees of missing angle range and noise levels, which demonstrate the effectiveness of the proposed approach.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Computed Tomography Scans of Naturally Mineralized Fractures in Upper Wolfcamp Shale

Computed tomography scans of fractures in small samples of Wolfcamp Shale from the Permian Basin and a depth of approximately 10,400 feet that have been cemented through natural processes. These samples were CT scanned at NETL. The data sets compliment the manuscript by Hajirezaie et al entitled "Mineral characterization of a sealed fracture: A multiscale multimodal imaging study of a syntaxial vein"

Computed Tomography↗
DOE OSTI2025

Computed Tomography Scanning and Geophysical Measurements of the Clinton Formation in Ohio

Computed tomography and multi-sensor core logging of core material from twelve Ohio wells held by the Ohio Department of Natural Resources, Division of Geological Survey to make publicly available core information from the Early Silurian Clinton Formation in Eastern Ohio. Describes data available can be accessed from NETL’s Energy Data eXchange (EDX) online system, https://edx.netl.doe.gov/dataset/ct-and-geophysical-data-of-ohio-clinton-sands.

58 GEOSCIENCES↗
DOE OSTI2022

High-Frequency 3D Photoacoustic Computed Tomography Using an Optical Microring Resonator

3D photoacoustic computed tomography (3D-PACT) has made great advances in volumetric imaging of biological tissues, with high spatial-temporal resolutions and large penetration depth. The development of 3D-PACT requires high-performance acoustic sensors with a small size, large detection bandwidth, and high sensitivity. In this work, we present a new high-frequency 3D-PACT system that uses a microring resonator (MRR) as the acoustic sensor. The MRR sensor has a size of 80 μ m in diameter and was fabricated using the nanoimprint lithography technology. Using the MRR sensor, we have developed a transmission-mode 3D-PACT system that has achieved a detection bandwidth of ~23 MHz, an imaging depth of ~8 mm, a lateral resolution of 114 μ m, and an axial resolution of 57 μ m. We have demonstrated the 3D PACT’s performance on in vitro phantoms, ex vivo mouse brain, and in vivo mouse ear and tadpole. The MRR-based 3D-PACT system can be a promising tool for structural, functional, and molecular imaging of biological tissues at depths.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A full-ring variable-aperture cadmium zinc telluride system for whole-body single photon emission computed tomography: realistic simulations with phantoms

Single photon emission computed tomography (SPECT) is an imaging modality that has demonstrated its utility in a number of clinical indications. Despite this progress, a high sensitivity, high spatial resolution, multi-tracer SPECT with a large field of view suitable for whole-body imaging of a broad range of radiotracers for theranostics is not available. Purpose We have designed a cadmium zinc telluride (CZT) variable-aperture full-ring SPECT scanner instrumented with a broad-energy tungsten collimator intended to fill this technological gap. The final purpose is to provide a multi-tracer solution for brain and whole-body imaging. Our static SPECT scanner breaks the paradigm of the standard dual- and triple-head rotational SPECT systems, utilizing a larger detector area in each scan increasing the sensitivity. We provide a demonstration of the performance of our design using a realistic model of our detector with simulated body-sized 99mTc phantoms. Methods We developed a realistic model of our detector by using a combination of a Geant4 Monte Carlo simulation and a CZT detector response model based on a finite element model. Our approach models the characteristic low-energy tail effect in CZT that noticeably affects the sensitivity and the quality of the scatter correction in CZT detectors. We implement a modified dual energy window scatter correction adapted to include the CZT low-energy tail effect. A dedicated correction is also developed to eliminate the undesirable truncation observed in images given the presence of detector edges and gaps between detectors, due to the non-rotational nature of our device. Corrections for the attenuation, detector response and the presence of collimators are also included. The images are reconstructed using the maximum-likelihood expectation-maximization algorithm implemented in the reconstruction open software STIR. Detector and reconstruction performance are characterized with a Derenzo phantom and a body-sized National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom containing 99mTc. Results Our SPECT design can resolve 6.4mm rods in a Derenzo phantom and obtain a good image contrast with the IQ phantom. Explicit testing of the gap and edge correction is provided, showing an excellent performance in eliminating the image truncation artifacts. Our modified scatter correction shows no overestimation of the contrast-recovery ratio for our realistic CZT detector model, as opposed to the cases without correction and with a standard dual-energy window scatter correction. Conclusions In this paper, we further demonstrate the performance of our design for whole-body imaging purposes. This adds to our previous demonstration of improved qualitative and quantitative 99mTc imaging for brain perfusion and 123I imaging for dopamine transport with respect to state-of-the-art NaI dual-head cameras. We show that our design performs similarly to the VERITON SPECT from Spectrum Dynamics, a commercial full-ring CZT SPECT camera, with the potential advantage of the broader energy range of application given by our custom-design tungsten collimators. Here, our device combines high sensitivity and image resolution with a broad-energy imaging application for the purpose of clinical imaging and theranostics of emerging radionuclides.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

X-ray Computed Tomography of Irradiated and Unirradiated AGR-3/4 Compacts

X-ray Computed Tomography (XCT) has been utilized to image and characterize compacts from the combined third and fourth irradiation of the Advanced Gas Reactor (AGR) Program, AGR-3/4, fuel. The experiment contained tristructural isotropic (TRISO)-coated fuel particles as well as designed-to-fail (DTF) fuel particles. Two irradiated compacts, representing the lower and higher range of AGR-3/4 burnup (4.85% and 14.92% fissions per initial heavy metal atom FIMA) were examined. These represent the first known highly irradiated TRISO fuel compacts to be examined via X-ray CT. Additionally, two unirradiated compacts from the same production batch as the examined irradiation compacts were also imaged for a baseline comparison. As XCT of irradiated TRISO compacts is not a commonly implemented characterization technique, a significant portion of the report focuses on developed methodology and imaging conditions. A specialized sample shielding device was developed and fabricated specifically to limit received dose to staff during sample preparation for XCT and to minimize excess gamma radiation dose to sensitive electronic components with the utilized X-ray system. Significant penetration through the uranium oxycarbide fuel kernels by significantly hardening the X-ray beam with specialized proprietary filters acquired from Carl Zeiss NTS Ltd. The filter utilized resulted in an average X ray photon energy of ~110 keV which approaches uranium’s K-edge (~115 keV), maximizing penetration for a microfocus X-ray source. The gamma-radiation emitted from the irradiated AGR-3/4 TRISO compacts, has the same properties and mechanisms for interaction with matter as X-rays, thus the detection of gamma-radiation by the utilized X-ray detectors was initially a concern. However, although ?-rays did produce an observable signal on the X-ray detector, its contribution to the overall imaging results appeared negligible upon 3D reconstruction. The neglibile impact on the resulting 3D reconstructed volumes were likely the result of: (1) a significantly lower detection efficiency for ?-rays relative to X-rays; (2) An X-ray flux at the detector several orders of magnitude higher than that of the impinging ?-rays from the irradiated compacts. These results suggest that irradiated compacts with significantly higher radiation fields can be examined in the future if an acceptable route for sample handling and preparation can be determined. Additionally, the 3D imaging results of XCT can provide a valuable means of assessing compacts. While in many ways complimentary to traditional post irradiation examination techniques such as optical ceramography, XCT can provide additional insight into compact features traditionally difficult to discern directly from cross-sectional imaging alone. Preliminary analyses on kernel size, morphology (aspect ratio and sphericity), and kernel orientation were presented. Sphericity, a simple morphological shape descriptor, was utilized to screen for kernel extrusions within the high burnup compact. The number of kernel extrusions identified via XCT represented an approximate two-fold increase from the quantity of extruded particles observed (via optical ceramography) in adjacent compacts from the same irradiation capsule. While numerical analysis of the compact datasets was highly preliminary, initial results show promise for providing complimentary metrics to current AGR-3/4 PIE and potentially additional insight into the processes driving TRISO fuel degradation during reactor operation. Additional analyses to be performed at a later date include a more detailed examination of kernel size, kernel sphericity (and observed kernel extrusions), and sphericity. Given all particles can be observed in a single data volume possible correlation of spatial position with observed kernel features will also be made at a later date.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Exploiting voxel-sparsity for bone imaging with sparse-view cone-beam computed tomography

An optimization-based image reconstruction frame work is developed specifically for bone imaging. This framework exploits voxel-sparsity by use of ℓ 1 -norm image regularization and it enables image reconstruction from sparse-view cone-beam computed tomography (CBCT) acquisition. The effectiveness of the voxel-sparsity regularization is enhanced by using a blurred image representation. Ramp-filtering is included in the data discrepancy term and it has the effect of acting as a preconditioner, reducing the necessary number of iterations. The bone image reconstruction framework is demonstrated on CBCT data taken from an equine metacarpal condyle specimen.

Bone imaging↗
DOE OSTI2022

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗