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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Specific absorbed fractions for a revised series of the UF/NCI pediatric reference phantoms: internal photon sources

Assessment of radiation absorbed dose to internal organs of the body from the intake of radionuclides, or in the medical setting through the injection of radiopharmaceuticals, is generally performed based upon reference biokinetic models or patient imaging data, respectively. Biokinetic models estimate the time course of activity localized to source organs. The time-integration of these organ activity profiles are then scaled by the radionuclide S-value, which defines the absorbed dose to a target tissue per nuclear transformation in various source tissues. S-values are computed using established nuclear decay information (particle energies and yields), and a parameter termed the specific absorbed fraction (SAF). The SAF is the ratio of the absorbed fraction—fraction of particle energy emitted in the source tissue that is deposited in the target tissue—and the target organ mass. While values of the SAF may be computed using patient-specific or individual-specific anatomic models, they have been more widely available through the use of computational reference phantoms. In this study, we report on an extensive series of photon SAFs computed in a revised series of the University of Florida and the National Cancer Institute pediatric reference phantoms which have been modified to conform to the specifications embodied in the ICRP reference adult phantoms of Publication 110 (e.g. organs modeled, organ ID numbers, blood contribution to elemental compositions). Following phantom anatomical revisions, photon radiation transport simulations were performed using MCNPX v2.7 in each of the ten phantoms of the series—male and female newborn, 1 year old, 5 year old, 10 year old, and 15 year old—for 60 different tissues serving as source and/or target regions. A total of 25 photon energies were considered from 10 keV to 10 MeV along a logarithm energy grid. Detailed analyses were conducted of the relative statistical errors in the Monte Carlo target tissue energy deposition tallies at low photon energies and over all energies for source–target combinations at large intra-organ separation distances. Based on these analyses, various data smoothing algorithms were employed, including multi-point weighted data smoothing, and log–log interpolation at low energies (1 keV and 5 keV) using limiting SAF values based upon target organ mass to bound the interpolation interval. The final dataset is provided in a series of ten electronic supplemental files in MS Excel format. Finally, the results of this study were further used as the basis for assessing the radiative component of internal electron source SAFs as described in our companion paper (Schwarz et al 2021) for this same pediatric phantom series.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Specific absorbed fractions for a revised series of the UF/NCI pediatric reference phantoms: internal electron sources

Assessment of radiation absorbed dose to internal organs of the body from the intake of radionuclides, or in the medical setting through the injection of radiopharmaceuticals, is generally performed based upon reference biokinetic models or patient imaging data, respectively. Biokinetic models estimate the time course of activity localized to source organs. The time-integration of these organ activity profiles are then scaled by the radionuclide S-value, which defines the absorbed dose to a target tissue per nuclear transformation in various source tissues. S-values are computed using established nuclear decay information (particle energies and yields), and a parameter termed the specific absorbed fraction (SAF). The SAF is the ratio of the absorbed fraction—fraction of particle energy emitted in the source tissue that is deposited in the target tissue—and the target organ mass. While values of the SAF may be computed using patient-specific or individual-specific anatomic models, they have been more widely available through the use of computational reference phantoms. In this study, we report on an extensive series of photon SAFs computed in a revised series of the University of Florida and the National Cancer Institute pediatric reference phantoms which have been modified to conform to the specifications embodied in the ICRP reference adult phantoms of Publication 110 (e.g. organs modeled, organ ID numbers, blood contribution to elemental compositions). Following phantom anatomical revisions, photon radiation transport simulations were performed using MCNPX v2.7 in each of the ten phantoms of the series—male and female newborn, 1 year old, 5 year old, 10 year old, and 15 year old—for 60 different tissues serving as source and/or target regions. A total of 25 photon energies were considered from 10 keV to 10 MeV along a logarithm energy grid. Detailed analyses were conducted of the relative statistical errors in the Monte Carlo target tissue energy deposition tallies at low photon energies and over all energies for source–target combinations at large intra-organ separation distances. Based on these analyses, various data smoothing algorithms were employed, including multi-point weighted data smoothing, and log–log interpolation at low energies (1 keV and 5 keV) using limiting SAF values based upon target organ mass to bound the interpolation interval. The final dataset is provided in a series of ten electronic supplemental files in MS Excel format. Finally, the results of this study were further used as the basis for assessing the radiative component of internal electron source SAFs as described in our companion paper (Schwarz et al 2021) for this same pediatric phantom series.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy

In this work, we develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single-crystal aluminum. We conclude with a discussion of our methods’ impact on future dislocation studies and possible future research avenues.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Feature Selection and Inferential Procedures for Video Data [Slides]

With the rise of electronic and high-dimensional data, new and innovative feature detection and statistical methods are required to perform accurate and meaningful statistical analysis of these datasets that provide unique statistical challenges. In the area of feature detection, much of the recent feature detection research in the computer vision community has focused on deep learning methods, which require large amounts of labeled training data. However, in many application areas, training data is very limited and often difficult to obtain. We develop methods for fast, unsupervised, precise feature detection for video data based on optical flows, edge detection, and clustering methods. We also use pretrained neural networks and interpretable linear models to extract features using very limited training data. In the area of statistics, while high-dimensional data analysis has been a main focus of recent statistical methodological research, much focus has been on populations of high-dimensional vectors, rather than populations of high-dimensional tensors, which are three-dimensional arrays that can be used to model dependent images, such as images taken of the same person or ripped video frames. Our feature detection method is a non-model-based method that fusses information from dense optical flow, raw image pixels, and frame differences to generate detections. Our hypothesis testing methods are based on the assumption that dependent images are concatenated into a tensor that follows a tensor normal distribution, and from this assumption, we derive likelihood-ratio, score, and regression-based tests for one- and multiple-sample testing problems. Our methods will be illustrated on simulated and real datasets. We conclude this report with comments on the relationship between feature detection and hypothesis testing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A region-appearance-based adaptive variational model for 3D liver segmentation

Purpose: Liver segmentation from computed tomography images is a challenging task owing to pixel intensity overlapping, ambiguous edges, and complex backgrounds. The authors address this problem with a novel active surface scheme, which minimizes an energy functional combining both edge- and region-based information. Methods: In this semiautomatic method, the evolving surface is principally attracted to strong edges but is facilitated by the region-based information where edge information is missing. As avoiding oversegmentation is the primary challenge, the authors take into account multiple features and appearance context information. Discriminative cues, such as multilayer consecutiveness and local organ deformation are also implicitly incorporated. Case-specific intensity and appearance constraints are included to cope with the typically large appearance variations over multiple images. Spatially adaptive balancing weights are employed to handle the nonuniformity of image features. Results: Comparisons and validations on difficult cases showed that the authors’ model can effectively discriminate the liver from adhering background tissues. Boundaries weak in gradient or with no local evidence (e.g., small edge gaps or parts with similar intensity to the background) were delineated without additional user constraint. With an average surface distance of 0.9 mm and an average volume overlap of 93.9% on the MICCAI data set, the authors’ model outperformed most state-of-the-art methods. Validations on eight volumes with different initial conditions had segmentation score variances mostly less than unity. Conclusions: The proposed model can efficiently delineate ambiguous liver edges from complex tissue backgrounds with reproducibility. Quantitative validations and comparative results demonstrate the accuracy and efficacy of the model.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By pooling the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.

Tourassi, Georgia↗
DOE OSTI2017

Toward Personalized Dosimetry with {sup 32}P Microparticle Therapy for Advanced Pancreatic Cancer

Purpose: To develop a Monte Carlo model for patient-specific dosimetry of {sup 32}P microparticle localized internal radionuclide therapy for advanced pancreatic cancer. Methods and Materials: Spherical tumor geometries and a pancreatic phantom were modeled, as well as different 3-dimensional non-uniform clinical pancreatic geometries based on patient-specific ultrasound images. The dosimetry simulations modeled the dose distribution due to the energy spectrum of emitted beta particles. Results: The average dose for small (3-cm diameter) and large (6-cm diameter) spherical tumors was 111 Gy (for 7.6 MBq administered activity) and 128 Gy (for 58 MBq), respectively. For the clinical 3-dimensional geometries, on the basis of patient data, the mean doses delivered to the tumor were calculated to be in the range 102 to 113 Gy, with negligible dose to the pancreas for the smallest tumor volumes. The calculated dose distributions are highly non-uniform. For the largest tumor studied, the pancreas received approximately 6% of the tumor dose (5.7 Gy). Importantly, we found that because the smallest tumor studied exhibited the most dynamic changes in volume in response to the treatment, the dose to tumor and pancreas is significantly underestimated if a static tumor volume is assumed. Conclusions: These results demonstrate the dosimetry of {sup 32}P microparticle localized internal radionuclide therapy for pancreatic cancer and the possibility of developing personalized treatment strategies. The results also highlight the importance of considering the effects of non-uniform dose distributions and dynamic change of tumor mass during treatment on the dosimetry of the tumor and critical organs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Microscopic insights into long-range 1D ordering in a dense semi-disordered molecular overlayer

In this work, the formation of a two-phase surface molecular overlayer that transitions from isolated propene molecules to a highly ordered 1D chain structure on Cu(111) is elucidated through combined high-resolution STM imaging and DFT-based calculations. These models reveal how disordered molecules present in-between the 1D chains stabilizes the system as a whole.

09 BIOMASS FUELS↗
DOE OSTI2014

First results of electron temperature measurements by the use of multi-pass Thomson scattering system in GAMMA 10

A multi-pass Thomson scattering (TS) has the advantage of enhancing scattered signals. We constructed a multi-pass TS system for a polarisation-based system and an image relaying system modelled on the GAMMA 10 TS system. We undertook Raman scattering experiments both for the multi-pass setting and for checking the optical components. Moreover, we applied the system to the electron temperature measurements in the GAMMA 10 plasma for the first time. The integrated scattering signal was magnified by approximately three times by using the multi-pass TS system with four passes. The electron temperature measurement accuracy is improved by using this multi-pass system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Three Dimensional Speckle Imaging Employing a Frequency-Locked Tunable Diode Laser

We describe a high accuracy frequency stepping method for a tunable diode laser to improve a three dimensional (3D) imaging approach based upon interferometric speckle imaging. The approach, modeled after Takeda, exploits tuning an illumination laser in frequency as speckle interferograms of the object (specklegrams) are acquired at each frequency in a Michelson interferometer. The resulting 3D hypercube of specklegrams encode spatial information in the x-y plane of each image with laser tuning arrayed along its z-axis. We present laboratory data of before and after results showing enhanced 3D imaging resulting from precise laser frequency control.

hree dimensional imaging↗
DOE OSTI2021

Confined hyperbolic metasurface modes for structured illumination microscopy

Plasmonic hyperbolic metasurfaces have emerged as an effective platform for manipulating the propagation of light. Here, confined modes on arrays of silver nanoridges that exhibit hyperbolic dispersion are used to demonstrate and model a super-resolution imaging technique based on structured illumination microscopy. A spatial resolution of ∼75 nm at 458 nm is demonstrated, which is 3.1 times better than an equivalent diffraction limited image. This work emphasizes the ability to engineer the properties of confined optical modes and to leverage those characteristics for applications in imaging. The results of this work could lead to improved approaches for super-resolution imaging using designed sub-wavelength structures.

42 ENGINEERING↗
DOE OSTI2023

Data Validation Experiments with a Computer-Generated Imagery Dataset for International Nuclear Safeguards

Computer vision models have great potential as tools for international nuclear safeguards verification activities, but off-the-shelf models require fine-tuning through transfer learning to detect relevant objects. Because open-source examples of safeguards-relevant objects are rare, and to evaluate the potential of synthetic training data for computer vision, we present the Limbo dataset. Limbo includes both real and computer-generated images of uranium hexafluoride containers for training computer vision models. Here, we generated these images iteratively based on results from data validation experiments that are detailed here. The findings from these experiments are applicable both for the safeguards community and the broader community of computer vision research using synthetic data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

Delay spectrum with phase-tracking arrays: extracting the H i power spectrum from the epoch of reionization

The detection of redshifted 21 cm emission from the epoch of reionization (EoR) is a challenging task owing to strong foregrounds that dominate the signal. In this paper, we propose a general method, based on the delay spectrum approach, to extract H i power spectra that are applicable to tracking observations using an imaging radio interferometer ('Delay Spectrum with Imaging Arrays'). Our method is based on modeling the H i signal taking into account the impact of wide field effects such as the w-term, which are then used as appropriate weights in cross-correlating the measured visibilities. Our method is applicable to any radio interferometer that tracks a phase center and could be utilized for arrays such as the Murchison Widefield Array (MWA), Low Frequency Array (LOFAR), Giant Meterwave Radio Telescope (GMRT), Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER), and HERA. In the literature the delay spectrum approach has been implemented for near-redundant baselines using drift scan observations. In this paper we explore the scheme for non-redundant tracking arrays. This is the first application of delay spectrum methodology to such data to extract the H i signal. We analyze 3 hr of MWA tracking data on the EoR1 field. We present both two-dimensional (k{sub ∥},k{sub ⊥}) and one-dimensional (k) power spectra from the analysis. Our results are in agreement with the findings of other pipelines developed to analyze the MWA EoR data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data

Remote sensing data collected from Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. This dataset includes *.pdf, *.txt, *.tif, *.dat, and *.csv with most zipped in *.tar.gz files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1971

The improvement of star positions by photographic methods

A brief description is given of the new astrometric camera and of the program on which it is being employed. This is the photography of the southern sky so that each star appears on at least four overlapping plates. Sets of star coordinates measured on each plate are combined with a limited set of provisional meridian places to give an accurate set of relative star positions which are independent of the meridian system together with a set of residuals for each plate-star. The technique is based on a simple geometrical model of image formation on photographic plates. Current investigations are concerned with the elimination of sources of systematic deviation from this model.

Clube, S. V. M.↗
NASA NTRS1994

Image Discrimination Models Predict Object Detection in Natural Backgrounds

Object detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.

Ahumada, Albert J., Jr.↗
NASA NTRS1993

Near-Infrared Continuum and 3.3um PAH Imaging of the Starburst Ring in the Type I Seyfert Galaxy NGC 7469

High resolution near-infrared images of the type 1 Seyfert Galaxy NGC 7469 have been obtained to probe its dusty nuclear environment. Direct images are relatively featureless, but residual images created by subtacting a smooth model based on best-fitting elliptical isoophotes reveal a tight inner spiral whose high surface-brightness portions correspond to a previously detected 3.

ring↗