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DOE OSTI2021

TP099-CT-800DR Comparison Between SIRZ-2 and Surface Fit Method ($ρ_{e},Ζ_{e}$) Estimation

The primary objective of this test plan is to acquire dual-energy computed tomography (CT) data of 33 well-known specimens using the Leidos Reveal CT-80DR+ baggage scanner housed at LLNL (hereafter referred to as CT-80DR). These data sets will allow us to compare, for each specimen, the accuracy and precision of electron density ($ρ_{e}$) and effective atomic number ($Z_{e}$) estimates obtained from two different algorithmic methods— from TSL’s surface fits and from LLNL’s SIRZ-2 decomposition. A secondary objective is to determine how often it is necessary to acquire CT-80DR spectral response information.

42 ENGINEERING↗

DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗

NASA NTRS2010

X-Ray Computed Tomography Inspection of the Stardust Heat Shield

The "Stardust" heat shield, composed of a PICA (Phenolic Impregnated Carbon Ablator) Thermal Protection System (TPS), bonded to a composite aeroshell, contains important features which chronicle its time in space as well as re-entry. To guide the further study of the Stardust heat shield, NASA reviewed a number of techniques for inspection of the article. The goals of the inspection were: 1) to establish the material characteristics of the shield and shield components, 2) record the dimensions of shield components and assembly as compared with the pre-flight condition, 3) provide flight infonnation for validation and verification of the FIAT ablation code and PICA material property model and 4) through the evaluation of the shield material provide input to future missions which employ similar materials. Industrial X-Ray Computed Tomography (CT) is a 3D inspection technology which can provide infonnation on material integrity, material properties (density) and dimensional measurements of the heat shield components. Computed tomographic volumetric inspections can generate a dimensionally correct, quantitatively accurate volume of the shield assembly. Because of the capabilities offered by X-ray CT, NASA chose to use this method to evaluate the Stardust heat shield. Personnel at NASA Johnson Space Center (JSC) and Lawrence Livermore National Labs (LLNL) recently performed a full scan of the Stardust heat shield using a newly installed X-ray CT system at JSC. This paper briefly discusses the technology used and then presents the following results: 1. CT scans derived dimensions and their comparisons with as-built dimensions anchored with data obtained from samples cut from the heat shield; 2. Measured density variation, char layer thickness, recession and bond line (the adhesive layer between the PICA and the aeroshell) integrity; 3. FIAT predicted recession, density and char layer profiles as well as bondline temperatures Finally suggestions are made as to future uses of this technology as a tool for non-destructively inspecting and verifying both pre and post flight heat shields.

McNamara, Karen M.↗

NASA NTRSDate not supplied

A Procedure to Quantify Interlaminar Damage in Refractory Composites by In-Situ Micro-X-Ray Computed Tomography

Refractory composites are relevant for hot-structure applications on re-entry vehicles. To develop optimized high-performance hot structures, accurate material property data is required. Material properties that often limit design in two-dimensional (2-D) composites are interlaminar strengths. A better understanding of the failure mechanisms for interlaminar shear (ILS), interlaminar tension (ILT), and curved-beam interlaminar tension (CB-ILT) tests can provide insight on improving the design and fabrication of refractory composites. In-situ tests were performed to capture the progressive failure in American Society for Testing and Materials (ASTM) sized ILS (ASTM D3846), ILT (ASTM D7291), and CB-ILT (ASTM D6415) specimens. Before measuring internal damage of refractory composites specimens, the methodology of the test setup warrants a complete and thorough discussion. Detail on the test procedure, test stand design, micro-x-ray computed tomography (CT) configuration, data processing, and CT system uncertainty is discussed. Through keeping the many parameters of the CT system consistent, the uncertainty of the CT system was minimized, optimizing the quality of measurements. The overall quality of a CT scan can be quantified by three metrics: voxel resolution, minimal voxel resolution feature detection, and directional variability. A better understanding of the progressive failure mechanisms of refractory composites is provided by the results of both the present discussion and the one found in the accompanying paper titled “Analysis of Interlaminar Damage in Refractory Composites by In-Situ Micro-X-Ray Computed Tomography”.

In-situ micro-x-ray CT↗

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 OSTI2021

Systematic Screening of COVID-19 Disease Based on Chest CT and RT-PCR for Cancer Patients Undergoing Radiation Therapy in a Coronavirus French Hotspot

Patients with cancer are presumed to be more vulnerable to COVID-19. We evaluated a screening strategy combining chest computed tomography (CT) and reverse-transcription polymerase chain reaction (RT-PCR) for patients treated with radiation therapy at our cancer center located in a COVID-19 French hotspot during the first wave of the pandemic.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

Automated Segmentation of Porous Thermal Spray Material CT Scans with Geometric Uncertainty Estimation

Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗

NASA NTRS1997

NASA Robotic Neurosurgery Testbed

The detection of tissue interface (e.g., normal tissue, cancer, tumor) has been limited clinically to tactile feedback, temperature monitoring, and the use of a miniature ultrasound probe for tissue differentiation during surgical operations. In neurosurgery, the needle used in the standard stereotactic CT (Computational Tomography) or MRI (Magnetic Resonance Imaging) guided brain biopsy provides no information about the tissue being sampled. The tissue sampled depends entirely upon the accuracy with which the localization provided by the preoperative CT or MRI scan is translated to the intracranial biopsy site. In addition, no information about the tissue being traversed by the needle (e.g., a blood vessel) is provided. Hemorrhage due to the biopsy needle tearing a blood vessel within the brain is the most devastating complication of stereotactic CT/MRI guided brain biopsy. A robotic neurosurgery testbed has been developed at NASA Ames Research Center as a spin-off of technologies from space, aeronautics and medical programs. The invention entitled 'Robotic Neurosurgery Leading to Multimodality Devices for Tissue Identification' is nearing a state ready for commercialization. The devices will: 1) improve diagnostic accuracy and precision of general surgery, with near term emphasis on stereotactic brain biopsy, 2) automate tissue identification, with near term emphasis on stereotactic brain biopsy, to permit remote control of the procedure, and 3) reduce morbidity for stereotactic brain biopsy. The commercial impact from this work is the potential development of a whole new generation of smart surgical tools to increase the safety, accuracy and efficiency of surgical procedures. Other potential markets include smart surgical tools for tumor ablation in neurosurgery, general exploratory surgery, prostate cancer surgery, and breast cancer surgery.

Mah, Robert↗

DOE OSTI2024

Quantifying Trapped Powder in Electron Beam Powder Bed Fusion

Abstract Electron beam powder bed fusion (PBF-EB) shows great potential for manufacturing complex parts including those with internal cavities for heat exchanger, manifold systems, or energy absorption purposes. PBF-EB allows for the manufacture of channel geometries without the need for support structures. Due to the nature of the powder spreading process, powder feedstock is often trapped in intentionally manufactured cavities. This trapped powder can often be difficult to remove and can disturb the intended flow of fluid through the cavity or damage downstream components in its use case. These trapped powder particles present a risk of contamination and component failure if not completely evacuated. Ti6Al4V is a choice material for aerospace applications due to its high strength to weight ratio and its composition as a nonferrous metal; however, in weight sensitive applications excess entrapped powders or powders loosely attached to the surface could cause undesirable weight increases. The inherent spreading process of PBF-EB is different than laser powder bed fusion (PBF-LB) in its operational temperature, sintering. In addition, PBF-EB is less commonly studied in literature compared to its PBF-LB counterpart, and as a result the complexity of the semi-sintered powder and its spreading behavior are not well understood. Prior work has investigated the difficulty in removing trapped powder from PBF-EB, but these studies do not address how to quantify the amount of trapped powder in the cavity. Thus, an accurate method to measure the amount of trapped powder in the cavity must be investigated. In this work, Ti6Al4V coupons were manufactured with horizontal and vertical cavities of three different sizes. Archimedes testing allows for the determination of density differences caused by porosity and trapped powders by measuring mass and volumetric dispersion. Computed tomography (CT) is well suited for segmenting the internal structure and features of a part and has been studied for applications including voids, porosity, and dross. Thus, CT was explored as a method for evaluating trapped powder content in this work. The volumetric representation of the segmentation of the reconstructed CT volume can vary greatly depending on the input filter and thresholding methods. In this study, four different types of segmentation approaches were evaluated to determine the best approach for segmenting the volume as compared to an operator labeled ground truth. The percentage density results from the Archimedes testing were compared to the volumetric percent density from the computed tomography approach. Differences in packing density between two different internal channel features were investigated. Overall, this work sought to validate the use of computed tomography for the detection of trapped powders and present a framework for volumetric segmentation.

Johnstone, Brian↗

NASA NTRSDate not supplied

Characterization and Modeling of Cell Wall Imperfections in Aluminum Honeycomb Cores using X-ray CT Imaging

Honeycomb cell imperfections may significantly affect the failure of sandwich composites in core crushing and shear modes. Identification and quantification of the different types of imperfections and evaluation of their effects on the mechanical response of honeycomb cores in flatwise compression is presented. X-Ray computed tomography (CT) scans of honeycomb cores in co-cured composite sandwich panels were performed. Image analysis methods are applied to characterize and quantify core cell geometry and cell wall waviness. An approach is developed for extracting cell wall mid surface planes and waviness for constructing finite element models using shell elements. Analysis results from models with the as-manufactured imperfections are compared to results from models with idealized geometries to evaluate the effect of the as-manufactured imperfections on the initiation of cell wall buckling and subsequent core crushing.

aluminum honeycomb↗

NASA NTRS2004

Noninvasive Investigation of Bone Adaptation in Humans Cumulative Daily Mechanical Loading

Our Research Objective is to measure the spatial distribution of bone mass throughout the entire internal volume of a bone such as the human calcaneus or heel bone. Currently, x-ray computed tomography (CT) is the only practical three-dimensional imaging technology capable of measuring bone density within small contiguous volumetric regions of a bone. spectrum of energies in the x-ray beam producing the image. We have developed a computational method of correcting these errors, provided the x-ray spectrum is known or measured (Yan et al., 2000). Monitoring serial changes in volumetric bone density with age, disuse, exercise or A CT image contains measurement errors in bone density caused by the broad drug therapy also requires accurate image registration.

Whalen, Robert T.↗

NASA NTRSDate not supplied

In-Space Inspection Needs: Opportunities for advanced NDE tools such as x-ray CT for additively manufactured parts, in-situ resource utilization, geological applications, and more

It is now 50 years since the first human presence on the surface of the Moon and as we strive to return with women and men in the next few years, we embrace new technical challenges, goals, and innovative solutions to address 21st century objectives. These new ambitions carry fresh challenges and risks, with the field of NDE playing an increasingly more relevant role towards meeting these essential goals. In recent years, more advanced NDE tools have triggered a rapid expansion of applications for the space industry. In particular, x-ray Computed Tomography (CT) has proven to be a trusted and powerful asset for spaceflight hardware inspection, as well as applied geotechnical analysis for natural materials (e.g., rocks, soils) for NASA and across industry. However, such methods have yet to be extended to “deep space” applications such as those that are now part of the US National Space Policy Directive (SPD-1) and the accelerated push to return humans to the Moon (i.e., Artemis). For this reason, advancing these powerful Earth-based laboratory methods via new technologies, integrated computational solutions, and creative engineering approaches is directly aligned with national space policies, as well as with multiple NASA Strategic Plan priorities. The use of x-ray CT at scales as fine as a few microns or smaller can identify spacecraft part failure modes relevant to quality assurance for flight hardware and AM parts such as those recently developed for ISS. This technology could also identify valuable metallic phases within geological materials (i.e., rocks or drill cores), enabling resource-relevant triage of samples for In-Situ Resource Utilization (ISRU) and high science value sample return to Earth laboratories. There is also significant application for 3D imaging tools for medical use such as inspecting protective gear as well as bone density degradation studies which are critical in establishing a sustained presence in space. Timing for development of these tools for space use is advantageous as we prepare for new opportunities in the next few years and recognize recent commercial technology advancements which make it feasible. Moreover, as NASA strives to take full advantage of developments in AM technologies, including In-Space Manufacturing (ISM), it is widely recognized that NDE tools such as CT will play an essential role in acceptance of these parts for widespread use. New in-space 3D inspection tools with complimentary technology such as AI-based automated feature recognition (accelerated by machine learning), rapid compositional analysis, and advanced sample manipulation, would be a game-changing step toward a new class of crew-based laboratory sensors once human outposts on the Moon are established.

In-Space Non-Destructive Evaluation↗

NASA NTRSDate not supplied

Assessment of Segmentation-Induced Deviations of Porosity Metrics in Powder Bed Fusion Additively Manufactured Components

Post processing X-ray computational tomography (CT) inspection data for additively manufactured (AM) components can induce deviations in defect quantification, affecting subsequent fatigue and failure predictions. To assess the influence and potential impact of segmentation-induced measurement deviations, this paper applies several segmentation techniques to X-ray CT data for powder bed fusion Ti-6Al-4V specimens exhibiting porosity conditions. X-ray CT reconstructions were segmented with varying techniques including Otsu’s thresholding, random forest, k-nearest neighbors, and the multilayer perceptron. Metrics such as pore size and global porosity were compared for internal validity. Then, top-down X-ray CT measurements of surface-breaking porosity were compared to optical profilometry for cross-validation.

Data fusion↗

DOE OSTI2026

IMPACT 2025-2026 Internship Poster

At Sandia National Laboratories, I developed a C++ program that converts printed circuit boards(PCB) and integrated circuits(IC) design files to be compatible for computed tomography (CT) simulations through Monte Carlo methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Closed-Form Approximation of the Total Variation Proximal Operator

Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constant. TV regularized optimization problems are typically solved using proximal methods, but the way in which they are applied is constrained by the absence of a closed-form expression for the proximal operator of the TV function. A closed-form approximation of the TV proximal operator has previously been proposed, but its accuracy was not theoretically explored in detail. Here, we address this gap by making several new theoretical contributions, proving that the approximation leads to a proximal operator of some convex function, it is equivalent to a gradient descent step on a smoothed version of TV, and that its error can be fully characterized and controlled with its scaling parameter. We experimentally validate our theoretical results on image denoising and sparse-view computed tomography (CT) image reconstruction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Livermore tomography tools: Accurate, fast, and flexible software for tomographic science

Livermore Tomography Tools (LTT) is a customizable scientific software package that enables a broad range of research and development efforts into computed tomography (CT). Here, it was developed to process x-ray and neutron CT data accurately and rapidly from raw detector counts to reconstructed volumes with the flexibility to handle many special cases. LTT fulfills long-term CT software goals to provide quantitatively accurate results reported in physical units (e.g., mm -1 or cm -1 ) while exploiting all available computational advantages to maximize speed. Written in C/C++ with support for multiple CPUs and GPUs, LTT runs on many computing platforms (Linux/Unix, Windows, and Mac; laptops to supercomputers). As a result, LTT can:process data acquired from various custom-built and commercially available CT scanners, model and simulate x-ray and neutron interactions to encourage algorithm prototyping, and allow for rapid insertion of the latest algorithms.We describe LTT’s software architecture, user interfaces, and its 88 algorithms (as of this writing) for pre-processing, reconstruction, post-processing, and simulation that support many scanner geometries (parallel-, fan-, cone-beam, and custom). Several applications are presented that illustrate LTT’s accuracy, speed, and flexibility relative to other solutions.

36 MATERIALS SCIENCE↗

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↗