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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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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-J-01: 3D Fluoroscopic Image Estimation From Patient-Specific 4DCBCT-Based Motion Models

Purpose: 3D motion modeling derived from 4DCT images, taken days or weeks before treatment, cannot reliably represent patient anatomy on the day of treatment. We develop a method to generate motion models based on 4DCBCT acquired at the time of treatment, and apply the model to estimate 3D time-varying images (referred to as 3D fluoroscopic images). Methods: Motion models are derived through deformable registration between each 4DCBCT phase, and principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated based on cone-beam projections simulating kV treatment imaging. PCA coefficients are optimized iteratively through comparison of these cone-beam projections and projections estimated based on the motion model. Digital phantoms reproducing ten patient motion trajectories, and a physical phantom with regular and irregular motion derived from measured patient trajectories, are used to evaluate the method in terms of tumor localization, and the global voxel intensity difference compared to ground truth. Results: Experiments included: 1) assuming no anatomic or positioning changes between 4DCT and treatment time; and 2) simulating positioning and tumor baseline shifts at the time of treatment compared to 4DCT acquisition. 4DCBCT were reconstructed from the anatomy as seen at treatment time. In case 1) the tumor localization error and the intensity differences in ten patient were smaller using 4DCT-based motion model, possible due to superior image quality. In case 2) the tumor localization error and intensity differences were 2.85 and 0.15 respectively, using 4DCT-based motion models, and 1.17 and 0.10 using 4DCBCT-based models. 4DCBCT performed better due to its ability to reproduce daily anatomical changes. Conclusion: The study showed an advantage of 4DCBCT-based motion models in the context of 3D fluoroscopic images estimation. Positioning and tumor baseline shift uncertainties were mitigated by the 4DCBCT-based motion models, while they caused errors when using 4DCT-based motion models. Partially funded by Varian research grant.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1990

Synthetic laser range imagery using Z-buffering

A technique for simulating laser range imagers using Z-buffering and readily available 3D graphics hardware is described. This approach utilizes the graphics hardware features available for hidden surface removal to achieve faster than real-time speed. How Z-buffering works in graphics workstations, how it can be adapted to provide a simulation of laser range images, and the performance characteristics of this simulation on a computer are shown.

Norsworthy, Robert↗
DOE OSTI2014

Ab initio multiple cloning simulations of pyrrole photodissociation: TKER spectra and velocity map imaging

In this study, we report a detailed computational simulation of the photodissociation of pyrrole using the ab initio Multiple Cloning (AIMC) method implemented within MOLPRO. The efficiency of the AIMC implementation, employing train basis sets, linear approximation for matrix elements, and Ehrenfest configuration cloning, allows us to accumulate significant statistics. We calculate and analyze the total kinetic energy release (TKER) spectrum and Velocity Map Imaging (VMI) of pyrrole and compare the results directly with experimental measurements. Both the TKER spectrum and the structure of the velocity map image (VMI) are well reproduced. Previously, it has been assumed that the isotropic component of the VMI arises from long time statistical dissociation. Instead, our simulations suggest that ultrafast dynamics contributes significantly to both low and high energy portions of the TKER spectrum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

SU-G-IeP3-08: Image Reconstruction for Scanning Imaging System Based On Shape-Modulated Point Spreading Function

Purpose: Deconvolution is a widely used tool in the field of image reconstruction algorithm when the linear imaging system has been blurred by the imperfect system transfer function. However, due to the nature of Gaussian-liked distribution for point spread function (PSF), the components with coherent high frequency in the image are hard to restored in most of the previous scanning imaging system, even the relatively accurate PSF is acquired. We propose a novel method for deconvolution of images which are obtained by using shape-modulated PSF. Methods: We use two different types of PSF - Gaussian shape and donut shape - to convolute the original image in order to simulate the process of scanning imaging. By employing deconvolution of the two images with corresponding given priors, the image quality of the deblurred images are compared. Then we find the critical size of the donut shape compared with the Gaussian shape which has similar deconvolution results. Through calculation of tightened focusing process using radially polarized beam, such size of donut is achievable under same conditions. Results: The effects of different relative size of donut and Gaussian shapes are investigated. When the full width at half maximum (FWHM) ratio of donut and Gaussian shape is set about 1.83, similar resolution results are obtained through our deconvolution method. Decreasing the size of donut will favor the deconvolution method. A mask with both amplitude and phase modulation is used to create a donut-shaped PSF compared with the non-modulated Gaussian PSF. Donut with size smaller than our critical value is obtained. Conclusion: The utility of donutshaped PSF are proved useful and achievable in the imaging and deconvolution processing, which is expected to have potential practical applications in high resolution imaging for biological samples.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

deeplenstronomy: A dataset simulation package for strong gravitational lensing

Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

CO tip functionalization in subatomic resolution atomic force microscopy

Noncontact atomic force microscopy (nc-AFM) employing a CO-functionalized tip displays dramatically enhanced resolution wherein covalent bonds of polycyclic aromatic hydrocarbon can be imaged. Employing real-space pseudopotential first-principles calculations, we examine the role of CO in functionalizing the nc-AFM tip. Our calculations allow us to simulate full AFM images and ascertain the enhancement mechanism of the CO molecule. We consider two approaches: one with an explicit inclusion of the CO molecule and one without. By comparing our simulations to existing experimental images, we ascribe the enhanced resolution of the CO functionalized tip to the special orbital characteristics of the CO molecule.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Robust primary modulation-based scatter estimation for cone-beam CT

Purpose: Scattered radiation is one of the major problems facing image quality in flat detector cone-beam computed tomography (CBCT). Previously, a new scatter estimation and correction method using primary beam modulation has been proposed. The original image processing technique used a frequency-domain-based analysis, which proved to be sensitive to the accuracy of the modulator pattern both spatially and in amplitude as well as to the frequency of the modulation pattern. In addition, it cannot account for penumbra effects that occur, for example, due to the finite focal spot size and the scatter estimate can be degraded by high-frequency components of the primary image. Methods: In this paper, the authors present a new way to estimate the scatter using primary modulation. It is less sensitive to modulator nonidealities and most importantly can handle arbitrary modulator shapes and changes in modulator attenuation. The main idea is that the scatter estimation can be expressed as an optimization problem, which yields a separation of the scatter and the primary image. The method is evaluated using simulated and experimental CBCT data. The scattering properties of the modulator itself are analyzed using a Monte Carlo simulation. Results: All reconstructions show strong improvements of image quality. To quantify the results, all images are compared to reference images (ideal simulations and collimated scans). Conclusions: The proposed modulator-based scatter reduction algorithm may open the field of flat detector-based imaging to become a quantitative modality. This may have significant impact on C-arm imaging and on image-guided radiation therapy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

SU-E-J-183: Quantifying the Image Quality and Dose Reduction of Respiratory Triggered 4D Cone-Beam Computed Tomography with Patient- Measured Breathing

Purpose: Respiratory triggered four dimensional cone-beam computed tomography (RT 4D CBCT) is a novel technique that uses a patient's respiratory signal to drive the image acquisition with the goal of imaging dose reduction without degrading image quality. This work investigates image quality and dose using patient-measured respiratory signals for RT 4D CBCT simulations instead of synthetic sinusoidal signals used in previous work. Methods: Studies were performed that simulate a 4D CBCT image acquisition using both the novel RT 4D CBCT technique and a conventional 4D CBCT technique from a database of oversampled Rando phantom CBCT projections. A database containing 111 free breathing lung cancer patient respiratory signal files was used to create 111 RT 4D CBCT and 111 conventional 4D CBCT image datasets from realistic simulations of a 4D RT CBCT system. Each of these image datasets were compared to a ground truth dataset from which a root mean square error (RMSE) metric was calculated to quantify the degradation of image quality. The number of projections used in each simulation is counted and was assumed as a surrogate for imaging dose. Results: Based on 111 breathing traces, when comparing RT 4D CBCT with conventional 4D CBCT the average image quality was reduced by 7.6%. However, the average imaging dose reduction was 53% based on needing fewer projections (617 on average) than conventional 4D CBCT (1320 projections). Conclusion: The simulation studies using a wide range of patient breathing traces have demonstrated that the RT 4D CBCT method can potentially offer a substantial saving of imaging dose of 53% on average compared to conventional 4D CBCT in simulation studies with a minimal impact on image quality. A patent application (PCT/US2012/048693) has been filed which is related to this work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Water Film Structure during Rupture as Revealed by MDS Image Analysis

The structure of thin water films during the rupture process was investigated by a new approach, which combines molecular dynamics simulation (MDS) with image processing analysis. The analysis procedure was developed to convert MDS trajectories to readable 3D images. The water films were studied at different thicknesses by MDS to determine the critical thickness at which the film ruptures. The potential energy of each specific film thickness during the simulation time was analyzed, and the results showed that the potential energy of stable films remained unchanged while the potential energy kept decreasing for films which ruptured during the simulation time. By applying the new procedure, the molecular porosity, which is defined as the void fraction between the volume of molecular pores in the water film and the total volume of the water film, was calculated. The results of molecular porosity for different film thicknesses during the simulation time suggested a critical molecular porosity as 49%. In other words, stable films have a molecular porosity of less than 49%. If a water film has a molecular porosity greater than 49%, rupture occurs during the simulation. This research was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences.

molecular dynamics simulation, film stability, ima↗
DOE OSTI2015

SU-E-J-217: Multiparametric MR Imaging of Cranial Tumors On a Dedicated 1.0T MR Simulator Prior to Stereotactic Radiosurgery

Purpose: Quantitative magnetic resonance imaging (MRI) of cranial lesions prior to stereotactic radiosurgery (SRS) may improve treatment planning and provide potential prognostic value. The practicality and logistics of acquiring advanced multiparametric MRI sequences to measure vascular and cellular properties of cerebral tumors are explored on a 1.0 Tesla MR Simulator. Methods: MR simulation was performed immediately following routine CT simulation on a 1T MR Simulator. MR sequences used were in the order they were performed: T2-Weighted Turbo Spin Echo (T2W-TSE), T2 FLAIR, Diffusion-weighted (DWI, b = 0, 800 to generate an apparent diffusion coefficient (ADC) map), 3D T1-Weighted Fast Field Echo (T1W-FFE), Dynamic Contrast Enhanced (DCE) and Post Gadolinium Contrast Enhanced 3D T1W-FFE images. T1 pre-contrast values was generated by acquiring six different flip angles. The arterial input function was derived from arterial pixels in the perfusion images selected manually. The extended Tofts model was used to generate the permeability maps. Routine MRI scans took about 30 minutes to complete; the additional scans added 12 minutes. Results: To date, seven patients with cerebral tumors have been imaged and tumor physiology characterized. For example, on a glioblastoma patient, the volume contoured on T1 Gd images, ADC map and the pharmacokinetic map (Ktrans) were 1.9, 1.4, and 1.5 cc respectively with strong spatial correlation. The mean ADC value of the entire volume was 1141 μm2/s while the value in the white matter was 811 μm2/s. The mean value of Ktrans was 0.02 min-1 in the tumor volume and 0.00 in the normal white matter. Conclusion: Our initial results suggest that multiparametric MRI sequences may provide a more quantitative evaluation of vascular and tumor properties. Implementing functional imaging during MR-SIM may be particularly beneficial in assessing tumor extent, differentiating radiation necrosis from tumor recurrence, and establishing reliable bio-markers for treatment response evaluation. The Department of Radiation Oncology at Henry Ford Health System has research agreement with Varian Medical System and Philips Health Care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Investigation of energy weighting using an energy discriminating photon counting detector for breast CT

Purpose: Breast CT is an emerging imaging technique that can portray the breast in 3D and improve visualization of important diagnostic features. Early clinical studies have suggested that breast CT has sufficient spatial and contrast resolution for accurate detection of masses and microcalcifications in the breast, reducing structural overlap that is often a limiting factor in reading mammographic images. For a number of reasons, image quality in breast CT may be improved by use of an energy resolving photon counting detector. In this study, the authors investigate the improvements in image quality obtained when using energy weighting with an energy resolving photon counting detector as compared to that with a conventional energy integrating detector.Methods: Using computer simulation, realistic CT images of multiple breast phantoms were generated. The simulation modeled a prototype breast CT system using an amorphous silicon (a-Si), CsI based energy integrating detector with different x-ray spectra, and a hypothetical, ideal CZT based photon counting detector with capability of energy discrimination. Three biological signals of interest were modeled as spherical lesions and inserted into breast phantoms; hydroxyapatite (HA) to represent microcalcification, infiltrating ductal carcinoma (IDC), and iodine enhanced infiltrating ductal carcinoma (IIDC). Signal-to-noise ratio (SNR) of these three lesions was measured from the CT reconstructions. In addition, a psychophysical study was conducted to evaluate observer performance in detecting microcalcifications embedded into a realistic anthropomorphic breast phantom.Results: In the energy range tested, improvements in SNR with a photon counting detector using energy weighting was higher (than the energy integrating detector method) by 30%–63% and 4%–34%, for HA and IDC lesions and 12%–30% (with Al filtration) and 32%–38% (with Ce filtration) for the IIDC lesion, respectively. The average area under the receiver operating characteristic curve (AUC) for detection of microcalcifications was higher by greater than 19% (for the different energy weighting methods tested) as compared to the AUC obtained with an energy integrating detector.Conclusions: This study showed that breast CT with a CZT photon counting detector using energy weighting can provide improvements in pixel SNR, and detectability of microcalcifications as compared to that with a conventional energy integrating detector. Since a number of degrading physical factors were not modeled into the photon counting detector, this improvement should be considered as an upper bound on achievable performance.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning

We present here a method that enables wide-field ground-based telescopes to scan the sky for subsecond stellar variability. The method has operational and image processing components. The operational component takes star trail images. Each trail serves as a light curve for its corresponding source and facilitates subexposure photometry. We train a deep neural network to identify stellar variability in wide-field star trail images. We use the Large Synoptic Survey Telescope Photon Simulator to generate simulated star trail images and include transient bursts as a proxy for variability. The network identifies transient bursts on timescales down to 10 ms. We argue that there are multiple fields of astrophysics that can be advanced by the unique combination of time resolution and observing throughput that our method offers.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2014

Low-Mass Planar Photonic Imaging Sensor

Continuing on the successful progress of NIAC Phase I, this report summarizes the technical progress achieved under NIAC Phase II during the performance period September 19, 2014-June 18, 2017. During this period, the research team has made the following accomplishments: designed and layout a silica photonic integrated circuit (PIC) as a two baseline interferometric imager; constructed an experiment to utilize the two baselines for complex visibility measurement on a point source and a variable width slit; analyzed and studied the testbed results. (in collaboration with Lockheed Martin); designed and layout Si3N4 PICs for the low-resolution and high-resolution SPIDER telescope; fabricated the multi-layer Si3N4 PIC for low and high resolution SPIDER telescope; characterize the optical throughput and heater response for Si3N4 PIC for low and high resolution SPIDER telescopes; carried out imaging experiments using the Si3N4 PIC low-resolution version (in collaboration with Lockheed Martin); investigated signal-to-noise (SNR) ratio of SPIDER imager compared to the conventional panchromatic imager (in collaboration with Lockheed Martin); fulfilled the SNR simulation upon SPIDER imager (in collaboration with Lockheed Martin).

Yoo, S. J. Ben↗
NASA NTRS2017

Low-Mass Planar Photonic Imaging Sensor

Continuing on the successful progress of NIAC (NASA Innovative Advanced Concepts) Phase I, this report summarizes the technical progress achieved under NIAC Phase II during the performance period September 19, 2014 to June 18, 2017. During this period, the research team has made the following accomplishments: designed and layout a silica photonic integrated circuit (PIC) as a two baselineinterferometric imager; constructed an experiment to utilize the two baselines for complex visibility measurementon a point source and a variable width slit; analyzed and studied the testbed results (in collaboration with Lockheed Martin); designed and layout Si3N4 PICs for the low-resolution and high-resolution SPIDER (Segmented Planar Imaging Detector for Electro-Optical Reconnaissance) telescope; fabricated the multi-layer Si3N4 PIC for low and high resolution SPIDER telescope; characterized the optical throughput and heater response for Si3N4 PIC for low and highresolution SPIDER telescopes; carried out imaging experiments using the Si3N4 PIC low-resolution version (in collaboration with Lockheed Martin); investigated signal-to-noise (SNR) ratio of SPIDER imager compared to the conventional panchromatic imager (in collaboration with Lockheed Martin); fulfilled the SNR simulation upon SPIDER imager (in collaboration with Lockheed Martin).

Yoo, S. J. Ben↗
DOE OSTI2020

3D micromechanical simulation of PBX composites

Previous research studied the constitutive response and interface strength of a bonded bicrystal system containing two HMX explosive grains bound with a HTPB polymer binder by performing FE simulations on real geometry imaged with micro Computed Tomography (µCT). The parameters generated from this past study were successfully applied to 2D mesoscale simulations of µCT imaged HMX-HTPB polycrystal samples. Presently, the mechanical response of PBXs containing a Nitro-Plasticized Estane (NPE) binder with similar microstructural geometry to the HMX-HTPB polycrystal samples were studied. Imaging of a HMX-NPE bicrystal sample allowed for simulation of the new material pairing. However, imaging using µCT techniques on the new polycrystal samples was difficult due to the decrease in contrast between the HMX grains and the NPE binder. Mesoscale polycrystal simulations utilizing a single µCT imaged microstructure were performed on a 3D representative volume element (RVE) comparing the delamination behavior and mechanical response of both HMX-HTPB and HMX-NPE PBXs. Furthermore, this technique shows the ability to virtually explore the mechanical response of a range of hypothetical materials that share common microstructural geometric characteristics in addition to studying numerically altered geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Validation of full-wave simulations for mode conversion of waves in the ion cyclotron range of frequencies with phase contrast imaging in Alcator C-Mod

Mode conversion of fast waves in the ion cyclotron range of frequencies (ICRF) is known to result in current drive and flow drive under optimised conditions, which may be utilized to control plasma profiles and improve fusion plasma performance. To describe these processes accurately in a realistic toroidal geometry, numerical simulations are essential. Quantitative comparison of these simulations and the actual experimental measurements is important to validate their predictions and to evaluate their limitations. The phase contrast imaging (PCI) diagnostic has been used to directly detect the ICRF waves in the Alcator C-Mod tokamak. The measurements have been compared with full-wave simulations through a synthetic diagnostic technique. Recently, the frequency response of the PCI detector array on Alcator C-Mod was recalibrated, which greatly improved the comparison between the measurements and the simulations. In this study, mode converted waves for D-{sup 3}He and D-H plasmas with various ion species compositions were re-analyzed with the new calibration. For the minority heating cases, self-consistent electric fields and a minority ion distribution function were simulated by iterating a full-wave code and a Fokker-Planck code. The simulated mode converted wave intensity was in quite reasonable agreement with the measurements close to the antenna, but discrepancies remain for comparison at larger distances.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗