Search NASA⌕ Search

SEARCH · Search NASA

Results for “image based modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

WE-G-BRF-04: Robust Real-Time Volumetric Imaging Based On One Single Projection

Purpose: Real-time volumetric imaging is highly desirable to provide instantaneous image guidance for lung radiation therapy. This study proposes a scheme to achieve this goal using one single projection by utilizing sparse learning and a principal component analysis (PCA) based lung motion model. Methods: A patient-specific PCA-based lung motion model is first constructed by analyzing deformable vector fields (DVFs) between a reference image and 4DCT images at each phase. At the training stage, we “learn” the relationship between the DVFs and the projection using sparse learning. Specifically, we first partition the projections into patches, and then apply sparse learning to automatically identify patches that best correlate with the principal components of the DVFs. Once the relationship is established, at the application stage, we first employ a patchbased intensity correction method to overcome the problem of different intensity scale between the calculated projection in the training stage and the measured projection in the application stage. The corrected projection image is then fed to the trained model to derive a DVF, which is applied to the reference image, yielding a volumetric image corresponding to the projection. We have validated our method through a NCAT phantom simulation case and one experiment case. Results: Sparse learning can automatically select those patches containing motion information, such as those around diaphragm. For the simulation case, over 98% of the lung region pass the generalized gamma test (10HU/1mm), indicating combined accuracy in both intensity and spatial domain. For the experimental case, the average tumor localization errors projected to the imager are 0.68 mm and 0.4 mm on the axial and tangential direction, respectively. Conclusion: The proposed method is capable of accurately generating a volumetric image using one single projection. It will potentially offer real-time volumetric image guidance to facilitate lung radiotherapy. This study is supported in part by NIH (1R01CA154747-01), The Core Technology Research in Strategic Emerging Industry, Guangdong, China(2011A081402003)

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Photometric Modeling of a Cometary Nucleus: Taking Hapke Modeling to the Limit

In the past two decades, photometric models developed by Bruce Hapke have been fit to a wide range of bodies in the Solar System: The Moon, Mercury, several asteroids, and many icy and rocky satellites. These models have enabled comparative descriptions of the physical attributes of planetary surfaces, including macroscopic roughness, particle size and size-distribution, the single scattering albedo, and the compaction state of the optically active portion of the regolith. One challenging type of body to observe and model, a cometary nucleus, awaited the first space based mission to obtain images unobscured by coma. The NASA-JPL Deep Space 1 Mission (DS1) encountered the short-period Jupiter-family comet 19/P Borrelly on September 22, 2001, about 8 days after perihelion. Prior to its closest approach of 2171 km, the remote-sensing package on the spacecraft obtained 25 CCD images of the comet, representing the first closeup, unobscured view of a comet's nucleus. At closest approach, corresponding to a resolution of 47 meters per pixel, the intensity of the coma was less than 1% of that of the nucleus. An unprecedented range of high solar phase angles (52-89 degrees), viewing geometries that are in general attainable only when a comet is active, enabled the first quantitative and disk-resolved modeling of surface photometric physical parameters.

Buratti, B. J.↗
DOE OSTI2022

Accelerating error correction in tomographic reconstruction

Abstract Spurred by recent advances in detector technology and X-ray optics, upgrades to scanning-probe-based tomographic imaging have led to an exponential growth in the amount and complexity of experimental data and have created a clear opportunity for tomographic imaging to approach single-atom sensitivity. The improved spatial resolution, however, is highly susceptible to systematic and random experimental errors, such as center of rotation drifts, which may lead to imaging artifacts and prevent reliable data extraction. Here, we present a model-based approach that simultaneously optimizes the reconstructed specimen and sinogram alignment as a single optimization problem for tomographic reconstruction with center of rotation error correction. Our algorithm utilizes an adaptive regularizer that is dynamically adjusted at each alternating iteration step. Furthermore, we describe its implementation in a software package targeting high-throughput workflows for execution on distributed-memory clusters. We demonstrate the performance of our solver on large-scale synthetic problems and show that it is robust to a wide range of noise and experimental drifts with near-ideal throughput.

Ali, Sajid (ORCID:0000000321864636)↗
DOE OSTI2025

DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes

By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.

Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗
NASA NTRS1995

Is chemical heating a major cause of the mesosphere inversion layer?

A region of thermal enhancement of the mesosphere has been detected on numerous occasions by in situ measurements, remote sensing from space, and lidar techniques. The source of these 'temperature inversion layers' has been attributed in the literature to the dissipation relating to dynamical forcing by gravity wave or tidal activity. However, evidence that gravity wave breaking can produce the inversion layer with amplitude as large as that observed in lidar measurements has been limited to results of numerical modeling. An alternative source for the production of the thermal inversion layer in the mesosphere is the direct deposition of heat by exothermic chemical reactions. Two-dimensional modeling combining a comprehensive model of the mesosphere photochemistry with the dynamical transport of long-lived species shows that the region from 80 to 95 km may be heated as much as 3 to 10 K/d during the night and half this rate during the day. Given the uncertainties in our understanding of the dynamics and chemistry for the mesopause region, separating the two sources by passive observations of the mesosphere thermal structure looks to be difficult. Therefore we have considered an active means for producing a mesopause thermal layer, namely the release of ozone into the upper mesosphere from a rocket payload. The induced effects would include artificial enhancements of the OH and Na airglow intensities as well as the mesopause thermal structure. The advantages of the rocket release of ozone is that detection of these effects by ground-based imaging, radar, and lidar systems and comparison of these effects with model predictions would help quantify the partition of the artificial inversion layer production into sources of dynamical and chemical forcing.

Meriwether, John W.↗
DOE OSTI2014

SU-D-9A-07: Imaging Dose and Cancer Risk in Image-Guided Radiotherapy of Cancers

Purpose: To systematically evaluate the imaging doses and cancer risks associated with various imaging procedures involving ionizing radiation during image-guided radiotherapy of an increasingly large number of cancer patients. Methods: 141 patients (52 brain cases, 47 thoracic cases, 42 abdominal cases, aged 3 to 91 years old) treated between October 2009 and March 2010 were included in this IRB-approved retrospective study. During the whole radiotherapy course, each patient underwent at least one type of imaging procedures, i.e., kV portal, MV portal and kVCBCT, besides CT simulations. Based on Monte Carlo modeling and particle transport in human anatomy of various dimensions, the correlations between the radiation doses to the various organs-at-risk (OARs) at the head, the thoracic and the abdominal regions and one's weight, circumference, scan mAs and kVp have been obtained and used to estimate the radiation dose from a specific imaging procedure. The radiation-induced excess relative risk (ERR) was then estimated with BEIR VII formulism based on one's gender, age and radiation dose. 1+ ERR was reported in this study as relative cancer risk. Results: For the whole cohort of 141 patients, the mean imaging doses from various imaging procedures were 8.3 cGy to the brain, 10.5 cGy to the lungs and 19.2 cGy to the red bone marrow, respectively. Accordingly, the cancer risks were 1.140, 1.369 and 2.671, respectively. In comparison, MV portal deposited largest doses to the lungs while kVCBCT delivered the highest doses to the red bone marrow. Conclusion: The compiled imaging doses to a patient during his/her treatment course were patient-specific and site-dependent, varying from 1.2 to 263.5 cGy on average, which were clinically significant and should be included in the treatment planning and overall decision-making. Our results indicated the necessity of personalized imaging to maximize its clinical benefits while reducing the associated cancer risks. Sichuan University Scholarship.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-207B-04: Noise Suppression for Energy-Resolved CT Via Variance Weighted Non-Local Filtration

Purpose: The photon starvation problem is exacerbated in energy-resolved CT, since the detected photons are shared by multiple energy channels. Using pixel similarity-based non-local filtration, we aim to produce accurate and high-resolution energy-resolved CT images with significantly reduced noise. Methods: Averaging CT images reconstructed from different energy channels reduces noise at the price of losing spectral information, while conventional denoising techniques inevitably degrade image resolution. Inspired by the fact that CT images of the same object at different energies share the same structures, we aim to reduce noise of energy-resolved CT by averaging only pixels of similar materials - a non-local filtration technique. For each CT image, an empirical exponential model is used to calculate the material similarity between two pixels based on their CT values and the similarity values are organized in a matrix form. A final similarity matrix is generated by averaging these similarity matrices, with weights inversely proportional to the estimated total noise variance in the sinogram of different energy channels. Noise suppression is achieved for each energy channel via multiplying the image vector by the similarity matrix. Results: Multiple scans on a tabletop CT system are used to simulate 6-channel energy-resolved CT, with energies ranging from 75 to 125 kVp. On a low-dose acquisition at 15 mA of the Catphan©600 phantom, our method achieves the same image spatial resolution as a high-dose scan at 80 mA with a noise standard deviation (STD) lower by a factor of >2. Compared with another non-local noise suppression algorithm (ndiNLM), the proposed algorithms obtains images with substantially improved resolution at the same level of noise reduction. Conclusion: We propose a noise-suppression method for energy-resolved CT. Our method takes full advantage of the additional structural information provided by energy-resolved CT and preserves image values at each energy level. Research reported in this publication was supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R21EB019597. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Template-based automatic breast segmentation on MRI by excluding the chest region

Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. Conclusions: The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Geothermal Play-Fairway Analysis of Washington State Prospects: Final Report

The Washington State Geothermal Play-Fairway Analysis overcomes the exploration challenges posed by dense vegetation, glacial deposits, and extreme precipitation. The geothermal play-fairways we target are locations where heat, permeability, and saturated porosity are present in sufficient volume to provide adequate heat exchange at depths accessible by modern drilling technology. The three study areas lie along the Cascade Range magmatic arc and are near Mount Baker, Mount St. Helens, and the Wind River Valley. The seven-year project is divided into three phases. In Phase 1 we build on a previous statewide assessment of geothermal resources and develop an initial modeling approach. The results are a series of favorability, uncertainty, and risk maps for three targeted study areas. Based on these initial results, we collect new geologic and geophysical data to further refine our modeling and reduce exploration uncertainty in Phase 2. We improve the modeling method to handle the new data and update the favorability, uncertainty, and risk maps. We also update the conceptual geothermal resource models. In Phase 3 we validate our modeling approach by drilling two temperature-gradient holes and collecting and analyzing core, image logs, and new geochemistry. Our modeling approach improves on an earlier statewide method through a more-rigorous and detailed assessment of heat and permeability. Permeability potential is assessed through geomechanical modeling of the deformation that can generate and maintain reservoir porosity and permeability. Metrics to inform heat potential include temperature-gradient wells, which are sparse in Washington; proximity of Quaternary volcanic vents and young intrusive rock; spring temperature; and reservoir temperature inferred from geothermometry. We weight the individual components using an expert-guided approach known as the Analytical Hierarchy Process. During Phase 2 we also develop a fluid-filled fracture model, and an infrastructure model that helps to delineate areas which are more favorable for geothermal development based on proximity to transmission lines, elevation, land ownership and use restrictions, and availability of process water. New geologic and geophysical data is collected during Phase 2 in each of our three main study areas. At Mount Baker and north of Mount St. Helens we conduct 1:24,000-scale geologic mapping and lidar analysis to better constrain the location and character of surface faults; detailed mapping in the Wind River Valley was completed just prior to the start of this project. Ages of intrusive rocks are determined with 40 Ar/ 39 Ar geochronology, though all of our samples are Miocene or older. We collect ground based gravity observations (a total of 1,580 new stations) in all of our study areas and ground-based magnetic lines (a total of 93 km) at Mount Baker. These data are combined with existing gravity and aeromagnetic data and used to constrain fault locations and geometry. Two to three cross sections are constructed at each study area using the mapped surface geology and forward-modeling of the gravity and magnetic data; these cross sections form the basis for our updated conceptual models. We collect magnetotelluric surveys at Mount Baker and Mount St. Helens and these data are inverted to form a resistivity model from the surface to about 10 km depth; each model shows conductive zones that can be interpreted as upwelling geothermal fluids. At Mount St. Helens we deploy a passive seismic array and use the newly detected events to refine the location of the Saint Helens seismic zone. We also employ ambient-noise tomography to develop a detailed seismic-velocity model for the study area and use this model to help constrain our cross sections and conceptual model. Based on the new data collected during Phase 2—and our updated models—we develop a campaign of temperature-gradient holes and core analysis to validate our modeling in Phase 3. Drill hole MB76-31 is located near Little Park Creek, 11 km west-southwest of the summit of Mount Baker, and is 1,471 ft deep. About 410 ft of core from the lower portion of the hole—and image logs from ~175 ft below ground surface to the bottom—are collected and analyzed. Water samples are collected and processed for geothermometry. Drill hole MSH17-24 is located along upper Schultz Creek, 16 km north-northeast of Mount St. Helens and has core from 470 ft to the bottom at 1,053 ft. We did not collect image logs due to borehole stability concerns, but water samples are collected and analyzed for geothermometry. Repeat temperature-gradient measurements are made at both sites and thermal conductivity is measured from core samples. At MB76-31, the equilibrated temperature gradient of 64°C/km and calculated heat flow of 141–159 mW/m 2 is more than twice the regional average. Detailed mapping and analysis of the core, coupled with correlation to the image logs, indicates a history of permeability generation consistent with our predictions of high permeability. Because the site has high favorability in the Phase 2 model, we consider the results a positive validation of the modeling. At site MSH17-24, the equilibrated temperature gradient of ~15°C/km and calculated heat flow of 41–43 mW/m 2 are similar to regional. Geochemical analysis of the water samples indicates a meteoric source without any geothermal component. Detailed outcrop-based mapping of fault exposures near the drill site and analysis of image logs from nearby boreholes indicates a history of permeability generation consistent with our predictions. Because the site has low favorability in the Phase 2 model, we consider the results a positive validation of the modeling. Together, the two sites provide a reasonably positive validation of the Phase 2 modeling and should encourage future use of this modeling approach.

15 GEOTHERMAL ENERGY↗
NASA NTRS2000

Internal Wave Study in the South China Sea Using SAR

Recently, the internal wave distribution maps in the China Seas have been compiled from hundreds of ERS-1/2, RADARSAT, and Space Shuttle SAR (Synthetic Aperture Radar) images from 1993 to 1999. Based on internal wave distribution map, most of internal waves in the northeast part of South China Sea were propagating westward. The wave crest can be as long as 200 km with amplitude of 100 m due to strong current from the Kuroshio branching out into the South China Sea. Based on the observations from drilling rigs near DongSha Island by Amoco Production Co., the solitons may be generated in a 4 km wide channel between Batan and Sabtang islands in Luzon Strait. The proposed generation mechanism is similar to the lee wave formation from a shallow topography. Both depression and elevation internal waves have been observed in the same RADARSAT ScanSAR image on May 4, 1998 near DongSha Island. Furthermore, depression and elevation internal waves have also been observed by SAR at the same location on the shelf in April and June, 1993 (in different seasons) respectively. Numerical models have been used to interpret their generation mechanism and evolution processes. Based on the SAR images, near DongSha Island, the westward propagating huge internal solitons are often encountered and diffracted/broken by the coral reefs on the shelf. After passing the island, the diffracted waves will re-merge or interact with each other. It has been observed that after the nonlinear wave-wave interaction, the phase of wave packet is shifted and wavelength is also changed. Examples of mesoscale features observed in SAR images, such as fronts, raincells, bathymetry, ship wakes, and oil spills will be presented. Recent mooring measurements in April 1999 near Dongsha Island, future field test ASIAEX (Asian Seas International Acoustics Experiment) planned for April 2001, and some pretest survey data will be discussed in this paper.

Liu, Antony K.↗
DOE OSTI2015

Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Modeling transport properties of inhomogeneous superconductor-metal composites

We propose a model for a superconductor-metal composite that allows to derive intrinsic transport properties of the superconducting phase based on 2D images of its cross section, and a minimal set of parameters. The method is tested experimentally by using, as model composite, a “transversal bridge” made on a Bi{sub 2}Sr{sub 2}Ca{sub 2}Cu{sub 3}O{sub 10+x} (BSCCO)-Ag multi-filamentary tape. It is shown that the approach allows to predict the measured I−〈E〉 curves of the filaments. In addition, one can determine the critical current anisotropy between the longitudinal and transverse directions of the Ag-BSCCO tape, and also of its superconducting filaments separately, which emphasizes the role of the morphology of the composite in the transport properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A Comparison between Physics-based and Polytropic MHD Models for Stellar Coronae and Stellar Winds of Solar Analogs

The development of the Zeeman–Doppler Imaging (ZDI) technique has provided synoptic observations of surface magnetic fields of low-mass stars. This led the stellar astrophysics community to adopt modeling techniques that have been used in solar physics using solar magnetograms. However, many of these techniques have been neglected by the solar community due to their failure to reproduce solar observations. Nevertheless, some of these techniques are still used to simulate the coronae and winds of solar analogs. Here we present a comparative study between two MHD models for the solar corona and solar wind. The first type of model is a polytropic wind model, and the second is the physics-based AWSOM model. We show that while the AWSOM model consistently reproduces many solar observations, the polytropic model fails to reproduce many of them, and in the cases where it does, its solutions are unphysical. Our recommendation is that polytropic models, which are used to estimate mass-loss rates and other parameters of solar analogs, must first be calibrated with solar observations. Alternatively, these models can be calibrated with models that capture more detailed physics of the solar corona (such as the AWSOM model) and that can reproduce solar observations in a consistent manner. Without such a calibration, the results of the polytropic models cannot be validated, but they can be wrongly used by others.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

A statistical model for evaluating GOPEX uplink performance

This article describes a statistical model to analyze the signal intensity received at the solid-state imaging (SSI) camera of the Galileo optical communications system from an Earth-based transmitter (GOPEX) demonstration. The analytical model assumes that the optical beam possesses a Gaussian profile and the communication channel has a log-normal scattering characteristic. The atmospheric-induced jitter is modelled as two independent zero mean Gaussian random variables. By modelling the system parameters as a set of independent and identically distributed (iid) random variables, the combined impact of uncertainties due to system parameters and the turbulent atmosphere can be approximated by a log-normal distributed signal intensity at the spacecraft. A Monte-Carlo software simulation package was also developed to compute the confidence interval probabilities for general optical beam profiles. Numerical results show that the approximation is valid for a wide range of operation scenarios.

Kiesaleh, K.↗
NASA NTRS2002

A Study of Imaging Interferometer Simulators

Several new space science mission concepts under development at NASA-GSFC for astronomy are intended to carry out synthetic imaging using Michelson interferometers or direct (Fizeau) imaging with sparse apertures. Examples of these mission concepts include the Stellar Imager (SI), the Space Infrared Interferometric Telescope (SPIRIT), the Submillimeter Probe of the Evolution of Cosmic Structure (SPECS), and the Fourier-Kelvin Stellar Interferometer (FKSI). We have been developing computer-based simulators for these missions. These simulators are aimed at providing a quantitative evaluation of the imaging capabilities of the mission by modelling the performance on different realistic targets in terms of sensitivity, angular resolution, and dynamic range. Both Fizeau and Michelson modes of operation can be considered. Our work is based on adapting a computer simulator called imSIM, which was initially written for the Space Interferometer Mission in order to simulate the imaging mode of new missions such as those listed. In a recent GSFC-funded study we have successfully written a preliminary version of a simulator SISIM for the Stellar Imager and carried out some preliminary studies with it. In a separately funded study we have also been applying these methods to SPECS/SPIRIT.

Allen, Ronald J.↗
NASA NTRS2002

Study of the Imaging Capabilities of SPIRIT/SPECS Concept Interferometers

Several new space science mission concepts under development at NASA-GSFC for astronomy are intended to carry out synthetic imaging using Michelson interferometers or direct (Fizeau) imaging with sparse apertures. Examples of these mission concepts include the Stellar Imager (SI), the Space Infrared Interferometric Telescope (SPIRIT), the Submillimeter Probe of the Evolution of Cosmic Structure (SPECS), and the Fourier-Kelvin Stellar Interferometer (FKSI). We have been developing computer-based simulators for these missions. These simulators are aimed at providing a quantitative evaluation of the imaging capabilities of the mission by modeling the performance on different realistic targets in terms of sensitivity, angular resolution, and dynamic range. Both Fizeau and Michelson modes of operation can be considered. Our work is based on adapting a computer simulator called imSIM which was initially written for the Space Interferometer Mission in order to simulate the imaging mode of new missions such as those listed. This report covers the activities we have undertaken to provide a preliminary version of a simulator for the SPIRIT mission concept.

Allen, Ronald J.↗