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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Imaging the inside of thick structures using cosmic rays

The authors present here a new method to image reinforcement elements inside thick structures and the results of a demonstration measurement performed on a mock-up wall built at Los Alamos National Laboratory. The method, referred to as “multiple scattering muon radiography”, relies on the use of cosmic-ray muons as probes. The work described in this article was performed to prove the viability of the technique as a means to image the interior of the dome of Florence Cathedral Santa Maria del Fiore, one of the UNESCO World Heritage sites and among the highest profile buildings in existence. Its result shows the effectiveness of the technique as a tool to radiograph thick structures and image denser object inside them.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2011

Methods for Radiometric Cross-Calibration of Imaging Sensors With and Without Overlapping Collections

This slide presentation reviews methods of cross calibration for imaging sensors. Cross calibration methods fall under two categories: (1) Relies on knowledge of a source that is common to both sensors, (2) Typically near-coincident views. Some work has used methods that do not require simultaneous data collections. This talk discusses the International System of Units (SI) -traceable approaches that permit cross-calibration. It describes on-orbit cross-calibration methods that are typically used, with and without overlapping views. It reviews sample results. It also describes a method without overlapping views and without on-site measurements.

Thome, K. J.↗
DOE OSTI2018

SATYA: Defending Against Adversarial Attacks Using Statistical HypothesisTesting

The paper presents a new defense against adversarial attacks for deep neural networks. We demonstrate the effectiveness of our approach against the popular adversarial image generation method DeepFool. Our approach uses Wald’s Sequential Probability Ratio Test to sufficiently sample a carefully chosen neighborhood around an input image to determine the correct label of the image. On a benchmark of 50,000 randomly chosen adversarial images generated by DeepFool we demonstrate that our method SATYA is able to recover the correct labels for 95.76% of the images for CaffeNet and 97.43% of the correct label for GoogLeNet.

Raj, Sunny↗
DOE OSTI2025

A bi-channel aided stitching of atomic force microscopy images

Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscope need to be optimally stitched before accurate physical information can be extracted from post analysis. However, the existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images when performing image stitching. To address these issues, we propose a bi-channel aided feature-based image stitching method and demonstrate its use on Atomic Force Microscopy (AFM) generated Pantoea sp. YR343 biofilm and PTO thin film sample images as experimental data. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. We utilize the amplitude and phase channels of AFM data to maximize the matching features and to estimate the position of the original topographical images and show that the proposed bi-channel aided stitching method outperforms the traditional direct stitching approach in AFM topographical image stitching task. Here, we demonstrated the application on AFM, but similar approaches could be employed of optical microscopy with brightfield and fluorescence channels. We believe this proposed workflow can serve as a valuable augmentation strategy for microscopy image stitching tasks and will benefit the experimentalist to avoid erroneous analysis and discovery due to incorrect stitching.

Atomic force microscopy↗
DOE OSTI2018

System and method for controlling depth of imaging in tissues using fluorescence microscopy under ultraviolet excitation following staining with fluorescing agents

A method is disclosed for analyzing a thin tissue sample and adapted to be supported on a slide. The tissue sample may be placed on a slide and exposed to one or more different exogenous fluorophores excitable in a range of about 300 nm-200 nm, and having a useful emission band from about 350 nm-900 nm, and including one or more fluorescent dyes or fluorescently labeled molecular probes that accumulate in tissue or cellular components. The fluorophores may be excited with a first wavelength of UV light between about 200 nm-290 nm. An optical system collects emissions from the fluorophores at a second wavelength, different from the first wavelength, which are generated in response to the first wavelength of UV light, to produce an image for analysis.

47 OTHER INSTRUMENTATION↗
NASA NTRS2007

A Software Platform for Post-Processing Waveform-Based NDE

Ultrasonic, microwave, and terahertz nondestructive evaluation imaging systems generally require the acquisition of waveforms at each scan point to form an image. For such systems, signal and image processing methods are commonly needed to extract information from the waves and improve resolution of, and highlight, defects in the image. Since some similarity exists for all waveform-based NDE methods, it would seem a common software platform containing multiple signal and image processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. This presentation describes NASA Glenn Research Center's approach in developing a common software platform for processing waveform-based NDE signals and images. This platform is currently in use at NASA Glenn and at Lockheed Martin Michoud Assembly Facility for processing of pulsed terahertz and ultrasonic data. Highlights of the software operation will be given. A case study will be shown for use with terahertz data. The authors also request scientists and engineers who are interested in sharing customized signal and image processing algorithms to contribute to this effort by letting the authors code up and include these algorithms in future releases.

Roth, Donald J.↗
DOE OSTI2025

Scaling deep learning for material imaging with a pseudo 3D model for domain transfer

The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.

25 ENERGY STORAGE↗
DOE OSTI2019

A Test of ASTM Methods E1245-03 and E562-11 in Calculating the Volume Fraction of Carbides in a U-10Mo Foil

Volume fraction measurements of carbides in DU-10Mo castings and foils were performed as part of a round robin study aimed at ensuring characterization efforts by multiple laboratories were yielding consistent results. Three of the four labs employed an automatic image analysis method to determine volume fraction of carbides in the metal and reported results around 2%. The other laboratory used a grid intercept count method and reported a fraction percentage of above 5%, about 3% higher than the other labs. At LANL, the method ASTM E1245-03, Determining the Inclusion or Second-Phase Constituent Content of Metals by Automatic Image Analysis was employed with a result of 2.02 ± .26%. There was a need to resolve the large discrepancy in volume fraction measurements reported from the round robin participant that used the grid intercept method. To do this, the grid intercept method outlined in ASTM E562-11, Determining Volume Fraction by Systematic Manual Point Count was also performed on the exact set of micrographs already analyzed by image analysis software at LANL. Calculations carried out following the manual point count method reported a percentage of 2.2 ± .6% similar to the result yielded by the automated image analysis method. Explanations for discrepancies in the results of the two methods are proposed.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Platform for Post-Processing Waveform-Based NDE

Signal- and image-processing methods are commonly needed to extract information from the waves, improve resolution of, and highlight defects in an image. Since some similarity exists for all waveform-based nondestructive evaluation (NDE) methods, it would seem that a common software platform containing multiple signal- and image-processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. NDE Wave & Image Processor Version 2.0 software provides a single, integrated signal- and image-processing and analysis environment for total NDE data processing and analysis. It brings some of the most useful algorithms developed for NDE over the past 20 years into a commercial-grade product. The software can import signal/spectroscopic data, image data, and image series data. This software offers the user hundreds of basic and advanced signal- and image-processing capabilities including esoteric 1D and 2D wavelet-based de-noising, de-trending, and filtering. Batch processing is included for signal- and image-processing capability so that an optimized sequence of processing operations can be applied to entire folders of signals, spectra, and images. Additionally, an extensive interactive model-based curve-fitting facility has been included to allow fitting of spectroscopy data such as from Raman spectroscopy. An extensive joint-time frequency module is included for analysis of non-stationary or transient data such as that from acoustic emission, vibration, or earthquake data.

Roth, Don J.↗
DOE OSTI2022

Real-time image denoising of mixed Poisson–Gaussian noise in fluorescence microscopy images using ImageJ

Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame either are computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson–Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high-performance ( > <#comment/> 8 d B ) denoising in less time than other fluorescence microscopy denoising methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Partially Collapsed Gibbs Samplers for Linear Inverse Problems and Applications to X-ray Imaging

Deblurring is one of the most common linear inverse problems in imaging, and most methods for deblurring an image assume that the convolution kernel, the so-called point spread function (PSF), is known or attempt to reconstruct the PSF simultaneously with the image. For many experimental imaging systems, like high energy X-ray radiography systems, the PSF must be computed from calibration imagery. The associated inverse problem is large-scale, so, in order to compute meaningful uncertainties with the reconstruction, it is common to use hierarchical Bayesian models and Markov chain Monte Carlo methods for sampling from the posterior. Classical Gibbs samplers can suffer from long autocorrelation lengths in the Markov chain when the reconstructed quantity in the inverse problem is finely discretized, resulting in poor computational efficiency of the Markov chain. In this work we present partially collapsed Gibbs samplers for linear inverse problems that greatly decrease correlations in the Markov chain, thus increasing the computational efficiency of the algorithms, and we present the results of the enhanced samplers on the problem of reconstructing the point spread function for an X-ray imaging system at the U.S. Department of Energy's Nevada National Security Site.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-E-J-28: Gantry Speed Significantly Affects Image Quality and Imaging Dose for 4D Cone-Beam Computed Tomography On the Varian Edge Platform

Purpose: 4D-CBCT facilitates assessment of tumor motion at treatment position. We investigated the effect of gantry speed on 4D-CBCT image quality and dose using the Varian Edge On-Board Imager (OBI). Methods: A thoracic protocol was designed using a 125 kVp spectrum. Image quality parameters were obtained via 4D acquisition using a Catphan phantom with a gating system. A sinusoidal waveform was executed with a five second period and superior-inferior motion. 4D-CBCT scans were sorted into 4 and 10 phases. Image quality metrics included spatial resolution, contrast-to-noise ratio (CNR), uniformity index (UI), Hounsfield unit (HU) sensitivity, and RMS error (RMSE) of motion amplitude. Dosimetry was accomplished using Gafchromic XR-QA2 films within a CIRS Thorax phantom. This was placed on the gating phantom using the same motion waveform. Results: High contrast resolution decreased linearly from 5.93 to 4.18 lp/cm, 6.54 to 4.18 lp/cm, and 5.19 to 3.91 lp/cm for averaged, 4 phase, and 10 phase 4DCBCT volumes respectively as gantry speed increased from 1.0 to 6.0 degs/sec. CNRs decreased linearly from 4.80 to 1.82 as the gantry speed increased from 1.0 to 6.0 degs/sec, respectively. No significant variations in UIs, HU sensitivities, or RMSEs were observed with variable gantry speed. Ion chamber measurements compared to film yielded small percent differences in plastic water regions (0.1–9.6%), larger percent differences in lung equivalent regions (7.5–34.8%), and significantly larger percent differences in bone equivalent regions (119.1–137.3%). Ion chamber measurements decreased from 17.29 to 2.89 cGy with increasing gantry speed from 1.0 to 6.0 degs/sec. Conclusion: Maintaining technique factors while changing gantry speed changes the number of projections used for reconstruction. Increasing the number of projections by decreasing gantry speed decreases noise, however, dose is increased. The future of 4DCBCT’s clinical utility relies on further investigation of image optimization.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

A morphological filter for removing 'Cirrus-like' emission from far-infrared extragalactic IRAS fields

The presence of diffuse extended IR emission from the Galaxy in the form of the so called 'Galactic Cirrus' emission has hampered the exploration of the extragalactic sky at long IR wavelengths. We describe the development of a filter based on mathematical morphology which appears to be a promising approach to the problem of cirrus removal. The method of Greyscale Morphology was applied to a 100 micron IRAS image of the M81 group of galaxies. This is an extragalactic field which suffers from serious contamination from foreground Galactic 'cirrus'. Using a technique called 'sieving', it was found that the cirrus emission has a characteristic behavior which can be quantified in terms of an average spatial structure spectrum or growth function. This function was then used to attempt to remove 'cirrus' from the entire image. The result was a significant reduction of cirrus emission by an intensity factor of 15 compared with the original input image. The method appears to preserve extended emission in the spatially extended IR disks of M81 and M82 as well as distinguishing fainter galaxies within bright regions of galactic cirrus. The techniques may also be applicable to IR databases obtained with the Cosmic Background Explorer.

Appleton, P. N.↗
NASA NTRS1998

Surface Emissivity Derived From Multispectral Satellite Data

Surface emissivity is critical for remote sensing of surface skin temperature and infrared cloud properties when the observed radiance is influenced by the surface radiation. It is also necessary to correctly compute the longwave flux from a surface at a given skin temperature. Surface emissivity is difficult to determine because skin temperature is an ill-defined parameter. The surface-emitted radiation may arise from a range of surface depths depending on many factors including soil moisture, vegetation, surface porosity, and heat capacity. Emissivity can be measured in the laboratory for pure surfaces. Transfer of laboratory measurements to actual Earth surfaces, however, is fraught with uncertainties because of their complex nature. This paper describes a new empirical approach for estimating surface skin temperature from a combination of brightness temperatures measured at different infrared wavelengths with satellite imagers. The method uses data from the new Geostationary Operational Environmental Satellite (GOES) imager to determine multispectral emissivities from the skin temperatures derived over the ARM Southern Great Plains domain.

Minnis, P.↗
NASA NTRS2012

Systems, computer-implemented methods, and tangible computer-readable storage media for wide-field interferometry

Disclosed herein are systems, computer-implemented methods, and tangible computer-readable storage media for wide field imaging interferometry. The method includes for each point in a two dimensional detector array over a field of view of an image: gathering a first interferogram from a first detector and a second interferogram from a second detector, modulating a path-length for a signal from an image associated with the first interferogram in the first detector, overlaying first data from the modulated first detector and second data from the second detector, and tracking the modulating at every point in a two dimensional detector array comprising the first detector and the second detector over a field of view for the image. The method then generates a wide-field data cube based on the overlaid first data and second data for each point. The method can generate an image from the wide-field data cube.

Lyon, Richard G.↗
NASA NTRS1992

Recognition and characterization of hierarchical interstellar structure. II - Structure tree statistics

A new method of image analysis is described, in which images partitioned into 'clouds' are represented by simplified skeleton images, called structure trees, that preserve the spatial relations of the component clouds while disregarding information concerning their sizes and shapes. The method can be used to discriminate between images of projected hierarchical (multiply nested) and random three-dimensional simulated collections of clouds constructed on the basis of observed interstellar properties, and even intermediate systems formed by combining random and hierarchical simulations. For a given structure type, the method can distinguish between different subclasses of models with different parameters and reliably estimate their hierarchical parameters: average number of children per parent, scale reduction factor per level of hierarchy, density contrast, and number of resolved levels. An application to a column density image of the Taurus complex constructed from IRAS data is given. Moderately strong evidence for a hierarchical structural component is found, and parameters of the hierarchy, as well as the average volume filling factor and mass efficiency of fragmentation per level of hierarchy, are estimated. The existence of nested structure contradicts models in which large molecular clouds are supposed to fragment, in a single stage, into roughly stellar-mass cores.

Houlahan, Padraig↗
DOE OSTI2016

WE-FG-207B-02: Material Reconstruction for Spectral Computed Tomography with Detector Response Function

Purpose: Different from the conventional computed tomography (CT), spectral CT based on energy-resolved photon-counting detectors is able to provide the unprecedented material composition. However, an important missing piece for accurate spectral CT is to incorporate the detector response function (DRF), which is distorted by factors such as pulse pileup and charge-sharing. In this work, we propose material reconstruction methods for spectral CT with DRF. Methods: The polyenergetic X-ray forward model takes the DRF into account for accurate material reconstruction. Two image reconstruction methods are proposed: a direct method based on the nonlinear data fidelity from DRF-based forward model; a linear-data-fidelity based method that relies on the spectral rebinning so that the corresponding DRF matrix is invertible. Then the image reconstruction problem is regularized with the isotropic TV term and solved by alternating direction method of multipliers. Results: The simulation results suggest that the proposed methods provided more accurate material compositions than the standard method without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity. Conclusion: We have proposed material reconstruction methods for spectral CT with DRF, whichprovided more accurate material compositions than the standard methods without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity. Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).

60 APPLIED LIFE SCIENCES↗