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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration

Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitude for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in upper lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1985

Preliminary evaluation of the airborne imaging spectrometer for vegetation analysis in the Klamath National Forest of northeastern California

The experiences and results associated with a project entitled Preliminary Evaluation of the Airborne Imaging Spectrometer for Vegetation Analysis is documented. The primary goal of the project was to provide ground truth, manual interpretation, and computer processing of data from an experimental flight of the Airborne Infrared Spectrometer (AIS) to determine the extent to which high spectral resolution remote sensing could differentiate among plant species, and especially species of conifers, for a naturally vegetated test site. Through the course of the research, JPL acquired AIS imagery of the test areas in the Klamath National Forest, northeastern California, on two overflights of both the Dock Well and Grass Lake transects. Over the next year or so, three generations of data was also received: first overflight, second overflight, and reprocessed second overflight. Two field visits were made: one trip immediately following the first overflight to note snow conditions and temporally-related vegetation states at the time of the sensor overpass; and a second trip about six weeks later, following acquisition of prints of the images from the first AIS overpass.

Strahler, A. H.↗
DOE OSTI2023

Association between soil organic carbon and calcium in acidic grassland soils from Point Reyes National Seashore, CA

All data is collected from across 3 soil cores spanning a pH gradient at Point Reyes National Seashore, as part of a recent publication. The bulk data csv includes hygroscopic moisture content, soil pH, total C and N values, particle size distributions, cation exchange capacity and extractable cations. The STXM data package includes raw spectra data from scanning transmission X-ray microscopy C near-edge X-ray absorption fine structure spectroscopy analysis, extracted from STXM Image Reader analysis and then subsequently normalised in Athena (see details in paper). The data file also contains a bulk chemistry and mineralogy dataset, including total and trace element contents from 3 depths and mineral compositions as measured at BL 11-3 SSRL and semi-quantified in HighScore. Finally the final data file is calcium K-edge X-ray absorption near-edge structure spectroscopy (BL 4-3) and micro-XANES and micro-X-ray fluorescence (BL 14-3b) data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Mars Public Engagement Overview

This viewgraph presentation reviews the Mars public engagement goal to understand and protect our home planet, explore the Universe and search for life, and to inspire the next generation of explorers. Teacher workshops, robotics education, Mars student imaging and analysis programs, MARS Student Imaging Project (MSIP), Russian student participation, MARS museum visualization alliance, and commercialization concepts are all addressed in this project.

public outreach↗
NASA NTRS1991

Applications of satellite image processing to the analysis of Amazonian cultural ecology

This paper examines the application of satellite image processing towards identifying and comparing resource exploitation among indigenous Amazonian peoples. The use of statistical and heuristic procedures for developing land cover/land use classifications from Thematic Mapper satellite imagery will be discussed along with actual results from studies of relatively small (100 - 200 people) settlements. Preliminary research indicates that analysis of satellite imagery holds great potential for measuring agricultural intensification, comparing rates of tropical deforestation, and detecting changes in resource utilization patterns over time.

Behrens, Clifford A.↗
NASA NTRS1977

A technique to correct for sample thickness variations for use with IDAPS X-ray radiograph analysis

The Image Data Processing System (IDAPS) at the Marshall Space Flight Center is used to analyze radiographs of metal samples to qualitatively and quantitatively map compositional variations across the samples. When the X-ray radiographs are of samples having thickness variations, corrections must be made to accomplish compositional analysis. A correction technique is described for cylindrical samples and is applied to radiographs of SPAR Experiment 74-18. Uncorrected and corrected images are shown.

Schafer, C. F.↗
NASA NTRS2004

Automatic Generation of Algorithms for the Statistical Analysis of Planetary Nebulae Images

Analyzing data sets collected in experiments or by observations is a Core scientific activity. Typically, experimentd and observational data are &aught with uncertainty, and the analysis is based on a statistical model of the conjectured underlying processes, The large data volumes collected by modern instruments make computer support indispensible for this. Consequently, scientists spend significant amounts of their time with the development and refinement of the data analysis programs. AutoBayes [GF+02, FS03] is a fully automatic synthesis system for generating statistical data analysis programs. Externally, it looks like a compiler: it takes an abstract problem specification and translates it into executable code. Its input is a concise description of a data analysis problem in the form of a statistical model as shown in Figure 1; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Internally, however, it is quite different: AutoBayes derives a customized algorithm implementing the given model using a schema-based process, and then further refines and optimizes the algorithm into code. A schema is a parameterized code template with associated semantic constraints which define and restrict the template s applicability. The schema parameters are instantiated in a problem-specific way during synthesis as AutoBayes checks the constraints against the original model or, recursively, against emerging sub-problems. AutoBayes schema library contains problem decomposition operators (which are justified by theorems in a formal logic in the domain of Bayesian networks) as well as machine learning algorithms (e.g., EM, k-Means) and nu- meric optimization methods (e.g., Nelder-Mead simplex, conjugate gradient). AutoBayes augments this schema-based approach by symbolic computation to derive closed-form solutions whenever possible. This is a major advantage over other statistical data analysis systems which use numerical approximations even in cases where closed-form solutions exist. AutoBayes is implemented in Prolog and comprises approximately 75.000 lines of code. In this paper, we take one typical scientific data analysis problem-analyzing planetary nebulae images taken by the Hubble Space Telescope-and show how AutoBayes can be used to automate the implementation of the necessary anal- ysis programs. We initially follow the analysis described by Knuth and Hajian [KHO2] and use AutoBayes to derive code for the published models. We show the details of the code derivation process, including the symbolic computations and automatic integration of library procedures, and compare the results of the automatically generated and manually implemented code. We then go beyond the original analysis and use AutoBayes to derive code for a simple image segmentation procedure based on a mixture model which can be used to automate a manual preproceesing step. Finally, we combine the original approach with the simple segmentation which yields a more detailed analysis. This also demonstrates that AutoBayes makes it easy to combine different aspects of data analysis.

Fischer, Bernd↗
NASA NTRS1973

Structural geologic analysis of Nevada using ERTS-1 images: A preliminary report

Structural analysis of Nevada using ERTS-1 images showns several previously unrecognized lineaments which may be the surface manifestations of major fault or fracture zones. Principle trends are NE, NW, NNE-NNW, and ENE. Two lineament zones, the Walker Lane and Midas Trench lineament system, transect the predominantly NNE-NNW trending mountain ranges for more than 500 km. 50 circular features have been delineated. Comparison with known Tertiary volcanic centers and reference to geologic maps suggest 8 new centers. Preferred distribution of mines and Tertiary volcanic centers along some of the major lineament suggests a genetic relationship. The intersection of three previously unmapped lineaments in northwestern Nevada is the location of a highly productive metallogenic district. In the Walker Lane, ENE-trending lineament appear to be related to the occurrence of productive ore deposits.

Rowan, L. C.↗
NASA NTRS1982

Topographic slope correction for analysis of thermal infrared images

A simple topographic slope correction using a linearized thermal model and assuming slopes less than about 20 degrees is presented. The correction can be used to analyzed individual thermal images or composite products such as temperature difference or thermal inertia. Simple curves are provided for latitudes of 30 and 50 degrees. The form is easily adapted for analysis of HCMM images using the DMA digital terrain data.

Watson, K.↗
DOE OSTI2021

Ambient Single-Cell Analysis and Native Tissue Imaging Using Laser-Ablation Electrospray Ionization Mass Spectrometry with Increased Spatial Resolution

Laser ablation electrospray ionization mass spectrometry (LAESI-MS) is an emerging method that has the potential to transform the field of metabolomics. This is in part due to LAESI-MS being an ambient ionization method that requires minimal sample preparation and uses (endogenous) water for in situ analysis. This application note details the employment of the ‘LAESI microscope’ source to perform spatially resolved MS analysis of cells and MS imaging (MSI) of tissues at high spatial resolution. This source configuration utilizes a long-working distance reflective objective that permits both visualization of the sample and a smaller LAESI laser beam profile than conventional LAESI setups. In this work, we analyzed 200 single cells of Allium cepa (red onion) and imaged Fittonia argyroneura (nerve plant) in high spatial resolution using this source coupled to a Fourier transform mass spectrometer for high mass resolution and high mass accuracy metabolomics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Automatic analysis of stereoscopic satellite image pairs for determination of cloud-top height and structure

Results are presented on an automatic stereo analysis of cloud-top heights from nearly simultaneous satellite image pairs from the GOES and NOAA satellites, using a massively parallel processor computer. Comparisons of computer-derived height fields and manually analyzed fields show that the automatic analysis technique shows promise for performing routine stereo analysis in a real-time environment, providing a useful forecasting tool by augmenting observational data sets of severe thunderstorms and hurricanes. Simulations using synthetic stereo data show that it is possible to automatically resolve small-scale features such as 4000-m-diam clouds to about 1500 m in the vertical.

Hasler, A. F.↗
DOE OSTI2016

SU-F-R-17: Advancing Glioblastoma Multiforme (GBM) Recurrence Detection with MRI Image Texture Feature Extraction and Machine Learning

Purpose: To test the potential of early Glioblastoma Multiforme (GBM) recurrence detection utilizing image texture pattern analysis in serial MR images post primary treatment intervention. Methods: MR image-sets of six time points prior to the confirmed recurrence diagnosis of a GBM patient were included in this study, with each time point containing T1 pre-contrast, T1 post-contrast, T2-Flair, and T2-TSE images. Eight Gray-level co-occurrence matrix (GLCM) texture features including Contrast, Correlation, Dissimilarity, Energy, Entropy, Homogeneity, Sum-Average, and Variance were calculated from all images, resulting in a total of 32 features at each time point. A confirmed recurrent volume was contoured, along with an adjacent non-recurrent region-of-interest (ROI) and both volumes were propagated to all prior time points via deformable image registration. A support vector machine (SVM) with radial-basis-function kernels was trained on the latest time point prior to the confirmed recurrence to construct a model for recurrence classification. The SVM model was then applied to all prior time points and the volumes classified as recurrence were obtained. Results: An increase in classified volume was observed over time as expected. The size of classified recurrence maintained at a stable level of approximately 0.1 cm{sup 3} up to 272 days prior to confirmation. Noticeable volume increase to 0.44 cm{sup 3} was demonstrated at 96 days prior, followed by significant increase to 1.57 cm{sup 3} at 42 days prior. Visualization of the classified volume shows the merging of recurrence-susceptible region as the volume change became noticeable. Conclusion: Image texture pattern analysis in serial MR images appears to be sensitive to detecting the recurrent GBM a long time before the recurrence is confirmed by a radiologist. The early detection may improve the efficacy of targeted intervention including radiosurgery. More patient cases will be included to create a generalizable classification model applicable to a larger patient cohort. NIH R43CA183390 and R01CA188300.NSF Graduate Research Fellowship DGE-1144087.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Cinema image-based in situ analysis and visualization of MPAS-ocean simulations

We created an in situ exploration visualization of an MPAS-Ocean simulation.We leveraged compositing in Cinema to provide interactive exploration.We decreased the storage footprint of the analysis and visualization results. Due to power and I/O constraints associated with extreme scale scientific simulations, in situ analysis and visualization will become a critical component to scientific exploration and discovery. Current analysis and visualization options at extreme scale are presented in opposition: write files to disk for interactive, exploratory analysis, or perform in situ analysis to save data products about phenomena that a scientists knows about in advance. In this study, we demonstrate extreme scale visualization of MPAS-Ocean simulations leveraging a third option based on Cinema, which is a novel framework for highly interactive, image-based in situ analysis and visualization that promotes exploration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

TU-F-17A-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - An Automatic Toolkit for Efficient and Robust Analysis of 4D Respiratory Motion

Purpose: To provide an automatic image analysis toolkit to process thoracic 4-dimensional computed tomography (4DCT) and extract patient-specific motion information to facilitate investigational or clinical use of 4DCT. Methods: We developed an automatic toolkit in MATLAB to overcome the extra workload from the time dimension in 4DCT. This toolkit employs image/signal processing, computer vision, and machine learning methods to visualize, segment, register, and characterize lung 4DCT automatically or interactively. A fully-automated 3D lung segmentation algorithm was designed and 4D lung segmentation was achieved in batch mode. Voxel counting was used to calculate volume variations of the torso, lung and its air component, and local volume changes at the diaphragm and chest wall to characterize breathing pattern. Segmented lung volumes in 12 patients are compared with those from a treatment planning system (TPS). Voxel conversion was introduced from CT# to other physical parameters, such as gravity-induced pressure, to create a secondary 4D image. A demon algorithm was applied in deformable image registration and motion trajectories were extracted automatically. Calculated motion parameters were plotted with various templates. Machine learning algorithms, such as Naive Bayes and random forests, were implemented to study respiratory motion. This toolkit is complementary to and will be integrated with the Computational Environment for Radiotherapy Research (CERR). Results: The automatic 4D image/data processing toolkit provides a platform for analysis of 4D images and datasets. It processes 4D data automatically in batch mode and provides interactive visual verification for manual adjustments. The discrepancy in lung volume calculation between this and the TPS is <±2% and the time saving is by 1–2 orders of magnitude. Conclusion: A framework of 4D toolkit has been developed to analyze thoracic 4DCT automatically or interactively, facilitating both investigational and clinical use. More software tools are under development for automatic analysis of other 4D imaging modalities. This research is in part supported by NIH (U54CA137788/132378) and City Seed FY13(Grant No. 93348-14 01). AY would like to thank MSKCC summer medical student research program supported by National Cancer Institute and hosted by Department of Medical Physics at MSKCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Cone-Beam Computed Tomography (CBCT) Hepatic Arteriography in Chemoembolization for Hepatocellular Carcinoma: Performance Depicting Tumors and Tumor Feeders

PurposeThis study was designed to analyze retrospectively the performance of cone-beam computed tomography (CBCT) hepatic arteriography in depicting tumors and their feeders and to investigate the related determining factors in chemoembolization for hepatocellular carcinoma (HCC).MethodsEighty-six patients with 142 tumors satisfying the imaging diagnosis criteria of HCC were included in this study. The performance of CBCT hepatic arteriography for chemoembolization per tumor and per patient was evaluated using maximum intensity projection images alone (MIP analysis) or MIP combined with multiplanar reformation images (MIP + MPR analysis) regarding the following three aspects: tumor depiction, confidence of tumor feeder detection, and trackability of tumor feeders. Tumor size, tumor enhancement, tumor location, number of feeders, diaphragmatic motion, portal vein enhancement, and hepatic artery to parenchyma enhancement ratio were regarded as potential determining factors.ResultsTumors were depicted in 125 (88.0 %) and 142 tumors (100 %) on MIP and MIP + MPR analysis, respectively. Imaging performances on MIP and MIP + MPR analysis were good enough to perform subsegmental chemoembolization without additional angiographic investigation in 88 (62.0 %) and 128 tumors (90.1 %) on per-tumor basis and in 43 (50 %) and 73 (84.9 %) on per-patient basis, respectively. Significant determining factors for performance in MIP + MPR analysis on per tumor basis were tumor size (p = 0.030), tumor enhancement (0.005), tumor location (p = 0.001), and diaphragmatic motion (p < 0.001).ConclusionsCBCT hepatic arteriography provided sufficient information for subsegmental chemoembolization by depicting tumors and their feeders in the vast majority of patients. Combined analysis of MIP and MPR images was essential to enhance the performance of CBCT hepatic arteriography.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Applications Of Machine Learning to Gas Plume Analysis In Longwave Infrared Hyperspectral Images

Longwave infrared hyperspectral images can be used for gas plume analysis, as many gases exhibit distinct absorption features in this portion of the electromagnetic spectrum. In practice, accurately identifying weak gas signatures is difficult because the observed radiance is dominated by background radiance, which varies with material, temperature, and viewing conditions. Many gas plume analysis pipelines operate on single images, limiting the ability to leverage spatial and multi-view information that could enhance the analysis. The goal of this dissertation is to explore how machine learning and deep learning methods can complement classical approaches to improve gas plume identification in longwave infrared hyperspectral imagery, and to investigate the use of neural radiance fields for hyperspectral scene reconstruction.

3D Scene Reconstruction↗