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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Grid artifact reduction for direct digital radiography detectors based on rotated stationary grids with homomorphic filtering

Purpose: Grid artifacts are caused when using the antiscatter grid in obtaining digital x-ray images. In this paper, research on grid artifact reduction techniques is conducted especially for the direct detectors, which are based on amorphous selenium. Methods: In order to analyze and reduce the grid artifacts, the authors consider a multiplicative grid image model and propose a homomorphic filtering technique. For minimal damage due to filters, which are used to suppress the grid artifacts, rotated grids with respect to the sampling direction are employed, and min-max optimization problems for searching optimal grid frequencies and angles for given sampling frequencies are established. The authors then propose algorithms for the grid artifact reduction based on the band-stop filters as well as low-pass filters. Results: The proposed algorithms are experimentally tested for digital x-ray images, which are obtained from direct detectors with the rotated grids, and are compared with other algorithms. It is shown that the proposed algorithms can successfully reduce the grid artifacts for direct detectors. Conclusions: By employing the homomorphic filtering technique, the authors can considerably suppress the strong grid artifacts with relatively narrow-bandwidth filters compared to the normal filtering case. Using rotated grids also significantly reduces the ringing artifact. Furthermore, for specific grid frequencies and angles, the authors can use simple homomorphic low-pass filters in the spatial domain, and thus alleviate the grid artifacts with very low implementation complexity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1997

An Accurate and Dynamic Computer Graphics Muscle Model

A computer based musculo-skeletal model was developed at the University in the departments of Mechanical and Biomedical Engineering. This model accurately represents human shoulder kinematics. The result of this model is the graphical display of bones moving through an appropriate range of motion based on inputs of EMGs and external forces. The need existed to incorporate a geometric muscle model in the larger musculo-skeletal model. Previous muscle models did not accurately represent muscle geometries, nor did they account for the kinematics of tendons. This thesis covers the creation of a new muscle model for use in the above musculo-skeletal model. This muscle model was based on anatomical data from the Visible Human Project (VHP) cadaver study. Two-dimensional digital images from the VHP were analyzed and reconstructed to recreate the three-dimensional muscle geometries. The recreated geometries were smoothed, reduced, and sliced to form data files defining the surfaces of each muscle. The muscle modeling function opened these files during run-time and recreated the muscle surface. The modeling function applied constant volume limitations to the muscle and constant geometry limitations to the tendons.

Levine, David Asher↗
NASA NTRS2018

The Impact of MISR-Derived Injection Height Initialization on Wildfire and Volcanic Plume Dispersion in the HYSPLIT Model

The dispersion of particles from wildfires, volcanic eruptions, dust storms, and other aerosol sources can affect air quality and other environmental factors downwind. Aerosol injection height is one source attribute that mediates downwind dispersion, as wind speed and direction can vary dramatically with elevation. Using plume heights derived from space-based, multi-angle imaging, we examine the impact of initializing plumes with satellite-measured vs. nominal (model-calculated or VAAC observations) injection height on the simulated dispersion of six large aerosol plumes. When there are significant differences in nominal vs. satellite-derived particle injection heights, or if one injection height is within the planetary boundary layer (PBL) and the other is above the PBL, differences in simulation results can arise. In the cases studied with significant nominal vs. satellite-derived injection height differences, the NOAA Air Resources Laboratory's Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model tends to represent plume evolution better if the injection height in the model is constrained by hyper-stereo satellite retrievals.

Plume heights↗
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Characterization of 316L Stainless Steel after Solid Phase Processing using Ultrasonic NDE Method

The Pacific Northwest National Laboratory undertook the Materials Characterization, Prediction, and Control Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). A motivation of the Materials Characterization, Prediction, and Control Project was to demonstrate ultrasonic testing as a nondestructive evaluation method to complement traditional destructive methods for characterizing material microstructure with emphasis on grain size determination using a method that may have future applications for real-time inline process monitoring. The objective of the work described in this report is to establish the process and an analysis method for measuring grain sizes of polycrystalline metals with ultrafine grains using ultrasonic shear wave backscattering, building on prior studies on coarser-grained material. The work involves five tasks: Measured ultrasonic backscattering experimentally for a series of 316L stainless steel specimens with various grain sizes made by friction stir processing. Calculated ultrasonic backscattering coefficients from experimental data based on a physical measurement model. Measured ground truth grain sizes of the specimens from electron backscatter diffraction grain boundary images using a generalization of the ASTM E112 (ASTM 2021) intercept method. Built a curve of ultrasonic backscattering coefficients versus the ground truth intercept-based grain sizes to determine the correlation between mean grain sizes and ultrasonic measurements. Demonstrated the ability of using the correlation curve to deduce grain sizes with measured ultrasonic backscattering coefficients for a few 316L stainless steel specimens whose grain sizes were unknown beforehand but were targeted to be an extrapolation to larger grain sizes than used to formulate the correlation curves. Experimental procedures and computational algorithms are developed and validated for these tasks. This work establishes an ultrasonic technique for characterizing material microstructure with ultrafine grains that are often resulted by solid-phase processing. The technique is nondestructive, and it has the potential to be used for real time inline process monitoring. This work successfully demonstrates the viability of an ultrasonic nondestructive evaluation method for microstructural characterization of material having ultrafine grain structure (as small as 1?mm) and produced by an advanced manufacturing method. This includes a demonstration of the method to extrapolate to other conditions. While not demonstrated here, the method is expected to be viable for in-line, or near-inline, process monitoring in advanced manufacturing applications with suitable consideration for access of instrumentation to the material being manufactured.

316 L Stainless Steel↗
NASA NTRS1994

Towards an Intelligent Planning Knowledge Base Development Environment

ract describes work in developing knowledge base editing and debugging tools for the Multimission VICAR Planner (MVP) system. MVP uses artificial intelligence planning techniques to automatically construct executable complex image processing procedures (using models of the smaller constituent image processing requests made to the JPL Multimission Image Processing Laboratory.

image processing knowledge bases artificial intell↗
DOE OSTI2014

Wellbore cement fracture evolution at the cement–basalt caprock interface during geologic carbon sequestration

Composite Portland cement-basalt caprock cores with fractures, as well as neat Portland cement columns, were prepared to understand the geochemical and geomechanical effects on the integrity of wellbores with defects during geologic carbon sequestration. The samples were reacted with CO2-saturated groundwater at 50 ºC and 10 MPa for 3 months under static conditions, while one cement-basalt core was subjected to mechanical stress at 2.7 MPa before the CO2 reaction. Micro-XRD and SEM-EDS data collected along the cement-basalt interface after 3-month reaction with CO2-saturated groundwater indicate that carbonation of cement matrix was extensive with the precipitation of calcite, aragonite, and vaterite, whereas the alteration of basalt caprock was minor. X-ray microtomography (XMT) provided three-dimensional (3-D) visualization of the opening and interconnection of cement fractures due to mechanical stress. Computational fluid dynamics (CFD) modeling further revealed that this stress led to the increase in fluid flow and hence permeability. After the CO2-reaction, XMT images displayed that calcium carbonate precipitation occurred extensively within the fractures in the cement matrix, but only partially along the fracture located at the cement-basalt interface. The 3-D visualization and CFD modeling also showed that the precipitation of calcium carbonate within the cement fractures after the CO2-reaction resulted in the disconnection of cement fractures and permeability decrease. The permeability calculated based on CFD modeling was in agreement with the experimentally determined permeability. This study demonstrates that XMT imaging coupled with CFD modeling represent a powerful tool to visualize and quantify fracture evolution and permeability change in geologic materials and to predict their behavior during geologic carbon sequestration or hydraulic fracturing for shale gas production and enhanced geothermal systems.

basalt↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Background based Gaussian mixture model lesion segmentation in PET

Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was demonstrated. The inclusion of the spatial prior improved segmentation accuracy only for lesions surrounded by heterogeneous background: in the relevant simulation subset, the median VE significantly decreased from 13% to 7%. Results on clinical data were found in accordance with simulations, with absolute VE <7%, Dice >0.85, CE <0.30, and HD <0.81. Conclusions: The sole introduction of constraints based on background modeling outperformed standard GMM and the other tested algorithms. Insertion of a spatial prior improved the accuracy for realistic cases of objects in heterogeneous backgrounds. Moreover, robustness against initialization supports the applicability in a clinical setting. In conclusion, application-driven constraints can generally improve the capabilities of GMM and statistical clustering algorithms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207B-05: Correlation of Normal Lung Density Changes with Dose After Stereotactic Body Radiotherapy (SBRT) for Early Stage Lung Cancer

Purpose: To investigate correlation of normal lung CT density changes with dose accuracy and outcome after SBRT for patients with early stage lung cancer. Methods: Dose distributions for patients originally planned and treated using a 1-D pencil beam-based (PB-1D) dose algorithm were retrospectively recomputed using algorithms: 3-D pencil beam (PB-3D), and model-based Methods: AAA, Acuros XB (AXB), and Monte Carlo (MC). Prescription dose was 12 Gy × 4 fractions. Planning CT images were rigidly registered to the followup CT datasets at 6–9 months after treatment. Corresponding dose distributions were mapped from the planning to followup CT images. Following the method of Palma et al .(1–2), Hounsfield Unit (HU) changes in lung density in individual, 5 Gy, dose bins from 5–45 Gy were assessed in the peri-tumor region, defined as a uniform, 3 cm expansion around the ITV(1). Results: There is a 10–15% displacement of the high dose region (40–45 Gy) with the model-based algorithms, relative to the PB method, due to the electron scattering of dose away from the tumor into normal lung tissue (Fig.1). Consequently, the high-dose lung region falls within the 40–45 Gy dose range, causing an increase in HU change in this region, as predicted by model-based algorithms (Fig.2). The patient with the highest HU change (∼110) had mild radiation pneumonitis, and the patient with HU change of ∼80–90 had shortness of breath. No evidence of pneumonitis was observed for the 3 patients with smaller CT density changes (<50 HU). Changes in CT densities, and dose-response correlation, as computed with model-based algorithms, are in excellent agreement with the findings of Palma et al. (1–2). Conclusion: Dose computed with PB (1D or 3D) algorithms was poorly correlated with clinically relevant CT density changes, as opposed to model-based algorithms. A larger cohort of patients is needed to confirm these results. This work was supported in part by a grant from Varian Medical Systems, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Protein identification from electron cryomicroscopy maps by automated model building and side-chain matching

Using single-particle electron cryo-microscopy (cryo-EM), it is possible to obtain multiple reconstructions showing the 3D structures of proteins imaged as a mixture. Here, it is shown that automatic map interpretation based on such reconstructions can be used to create atomic models of proteins as well as to match the proteins to the correct sequences and thereby to identify them. This procedure was tested using two proteins previously identified from a mixture at resolutions of 3.2 Å, as well as using 91 deposited maps with resolutions between 2 and 4.5 Å. The approach is found to be highly effective for maps obtained at resolutions of 3.5 Å and better, and to have some utility at resolutions as low as 4 Å.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3–0.5 mm for patients 1–3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1–1.5 min per phase. Conclusions: High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Towards local progression estimation of pulmonary emphysema using CT

Purpose: Whole lung densitometry on chest CT images is an accepted method for measuring tissue destruction in patients with pulmonary emphysema in clinical trials. Progression measurement is required for evaluation of change in health condition and the effect of drug treatment. Information about the location of emphysema progression within the lung may be important for the correct interpretation of drug efficacy, or for determining a treatment plan. The purpose of this study is therefore to develop and validate methods that enable the local measurement of lung density changes, which requires proper modeling of the effect of respiration on density. Methods: Four methods, all based on registration of baseline and follow-up chest CT scans, are compared. The first naïve method subtracts registered images. The second employs the so-called dry sponge model, where volume correction is performed using the determinant of the Jacobian of the transformation. The third and the fourth introduce a novel adaptation of the dry sponge model that circumvents its constant-mass assumption, which is shown to be invalid. The latter two methods require a third CT scan at a different inspiration level to estimate the patient-specific density-volume slope, where one method employs a global and the other a local slope. The methods were validated on CT scans of a phantom mimicking the lung, where mass and volume could be controlled. In addition, validation was performed on data of 21 patients with pulmonary emphysema. Results: The image registration method was optimized leaving a registration error below half the slice increment (median 1.0 mm). The phantom study showed that the locally adapted slope model most accurately measured local progression. The systematic error in estimating progression, as measured on the phantom data, was below 2 gr/l for a 70 ml (6%) volume difference, and 5 gr/l for a 210 ml (19%) difference, if volume correction was applied. On the patient data an underlying linearity assumption relating lung volume change with density change was shown to hold (fitR{sup 2} = 0.94), and globalized versions of the local models are consistent with global results (R{sup 2} of 0.865 and 0.882 for the two adapted slope models, respectively). Conclusions: In conclusion, image matching and subsequent analysis of differences according to the proposed lung models (i) has good local registration accuracy on patient data, (ii) effectively eliminates a dependency on inspiration level at acquisition time, (iii) accurately predicts progression in phantom data, and (iv) is reasonably consistent with global results in patient data. It is therefore a potential future tool for assessing local emphysema progression in drug evaluation trials and in clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-E-I-71: Quality Assessment of Surrogate Metrics in Multi-Atlas-Based Image Segmentation

Purpose: With the ever-growing data of heterogeneous quality, relevance assessment of atlases becomes increasingly critical for multi-atlas-based image segmentation. However, there is no universally recognized best relevance metric and even a standard to compare amongst candidates remains elusive. This study, for the first time, designs a quantification to assess relevance metrics’ quality, based on a novel perspective of the metric as surrogate for inferring the inaccessible oracle geometric agreement. Methods: We first develop an inference model to relate surrogate metrics in image space to the underlying oracle relevance metric in segmentation label space, with a monotonically non-decreasing function subject to random perturbations. Subsequently, we investigate model parameters to reveal key contributing factors to surrogates’ ability in prognosticating the oracle relevance value, for the specific task of atlas selection. Finally, we design an effective contract-to-noise ratio (eCNR) to quantify surrogates’ quality based on insights from these analyses and empirical observations. Results: The inference model was specialized to a linear function with normally distributed perturbations, with surrogate metric exemplified by several widely-used image similarity metrics, i.e., MSD/NCC/(N)MI. Surrogates’ behaviors in selecting the most relevant atlases were assessed under varying eCNR, showing that surrogates with high eCNR dominated those with low eCNR in retaining the most relevant atlases. In an end-to-end validation, NCC/(N)MI with eCNR of 0.12 compared to MSD with eCNR of 0.10 resulted in statistically better segmentation with mean DSC of about 0.85 and the first and third quartiles of (0.83, 0.89), compared to MSD with mean DSC of 0.84 and the first and third quartiles of (0.81, 0.89). Conclusion: The designed eCNR is capable of characterizing surrogate metrics’ quality in prognosticating the oracle relevance value. It has been demonstrated to be correlated with the performance of relevant atlas selection and ultimate label fusion.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1987

L-band radar backscatter modeling of forest stands

An L-band HH radar backscatter model of a coniferous forest stand is described and compared with SIR-B L-band image data of the Mount Shasta region of northern California. Being based upon an identification and implementation of the expected major components of forest backscattering, the model is simple in form and thus fast computationally, making possible extensive simulations of forest stands. A particularly important component in the model relates to representing the specular reflections expected from tree trunks to the ground and then back to the sensor. These are strong returns and are seen to be necessary to explain both the forest measurements made by the authors and the observations of others. Although the experimental data is limited in quantity and quality, agreement between available experimental and simulated values of forest backscatter is better than the residual uncertainty and relative calibration error of the experimental data, provided the model and experiment are matched initially at one set of parameter values.

Richards, John A.↗
NASA NTRS1989

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

Barrett, Eamon B.↗
DOE OSTI2014

Far-field imaging with a multi-frequency metalens

A metalens, i.e., a dense array of identical resonators, allows to image an object pattern at subwavelength scale from far field radiation field. Here, we show that the efficiency can be improved when the resonant frequencies of the cell are distributed over a given frequency range. Because in such systems each eigen mode is localized, the subwavelength image is built from a spectral analysis of the radiated field. A simple model based on coupled resonant dipoles is used to find the best frequency distribution. This multifrequency metalens approach is validated using a flat array of split ring resonators. We experimentally demonstrate the subwavelength resolution of such a device at microwave range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Billion-pixel x-ray camera (BiPC-X)

The continuing improvement in quantum efficiency (above 90% for single visible photons), reduction in noise (below 1 electron per pixel), and shrink in pixel pitch (less than 1 μm) enable billion-pixel x-ray cameras (BiPC-X) based on commercial complementary metal–oxide–semiconductor (CMOS) imaging sensors. We describe BiPC-X designs and prototype construction based on flexible tiling of commercial CMOS imaging sensors with millions of pixels. Device models are given for direct detection of low energy x rays (<10 keV) and indirect detection of higher energies using scintillators. Modified Birks’s law is proposed for light yield non-proportionality in scintillators as a function of x-ray energy. Single x-ray sensitivity and spatial resolution have been validated experimentally using a laboratory x-ray source and the Argonne Advanced Photon Source. Possible applications include wide field-of-view or large x-ray aperture measurements in high-temperature plasmas, the state-of-the-art synchrotron, x-ray free electron laser, and pulsed power facilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗