skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Blind Detection of Ultra-faint Streaks with a Maximum Likelihood Method

Authors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1406419
Report Number(s):
LLNL-CONF-703048
DOE Contract Number:
AC52-07NA27344
Resource Type:
Conference
Resource Relation:
Conference: Presented at: Advanced Maui Optical and Space Surveillance Technologies, na, CA, United States, Sep 15 - Sep 15, 2016
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE

Citation Formats

Dawson, W A, and Schneider, M D. Blind Detection of Ultra-faint Streaks with a Maximum Likelihood Method. United States: N. p., 2016. Web.
Dawson, W A, & Schneider, M D. Blind Detection of Ultra-faint Streaks with a Maximum Likelihood Method. United States.
Dawson, W A, and Schneider, M D. Wed . "Blind Detection of Ultra-faint Streaks with a Maximum Likelihood Method". United States. doi:. https://www.osti.gov/servlets/purl/1406419.
@article{osti_1406419,
title = {Blind Detection of Ultra-faint Streaks with a Maximum Likelihood Method},
author = {Dawson, W A and Schneider, M D},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Sep 07 00:00:00 EDT 2016},
month = {Wed Sep 07 00:00:00 EDT 2016}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • A maximum-likelihood algorithm (ML) has been previously proposed for the reconstruction of positron emission tomography (PET) images. Herein, the authors compare the relative performance of this new algorithm to the filtered back projectin (FBP) technique for the PETT VI system with measured and simulated phantom data. Using point source data from PETT VI, in low resolution mode, the ML reconstructed image had a resolution which was approximately half of the full-width half-maximum (FWHM) detector resolution. Using PETT VI brain images (o-15 water bolus) ML yielded images with visibly better resolution than FBP. Simulations of data collections on PETT VI weremore » used to quantitate the relationship between image resolution and noise: 1) A pie-phantom containing six uniform wedges and one million events was reconstructed with ML and FBP. The image resolution and standard deviation (SD) of pixel values within the regions of uniform activity were measured. 2) A brain-phantom containing detailed structure, and one million events was simulated for 20 realizations, all of which were reconstructed using ML and FBP. Image resolution and SD of lcm/sup 2/ region values, across the 20 realizations, were measured. In both simulations ML had little to no advantage over FBP for reconstruction of low resolution and low regional SD images. However, if images with higher SD were accepted, the difference in resolution attained between ML and FBP became substantial: FWHM(ML)/FWHM(FBP)approx. =0.70. These results indicate that ML may be preferred over FBP for high resolution reconstruction, approaching, or even less than the detector resolution.« less
  • A new method for calculation of ROI values has been developed from the EM maximum likelihood reconstruction algorithm. The current problem is that ROI values extracted from filtered backprojection (FBP) images are biased due to the partial volume effect and the reconstruction filter. The new algorithm incorporates a physical-statistical model of the projection data and requires knowledge of the location of all ROIs to define its ''pixels''. Unbiased estimates of ROI concentrations are directly estimated, i.e. the bias due to the partial volume effect has been removed. In addition, a dramatic reduction in computation time compared to the EM reconstructionmore » is achieved due to the reduced number of pixels and the improved convergence characteristics. The new algorithm was tested with simulated tomographic projection data generated at a range of count rates with 1 cm resolution. ROI values were estimated directly by the extended EM algorithm. FBP reconstructions were performed with high and low resolution filters and ROI values were extracted with a range of concentric regions within the true ROI. With no noise, the FBP values were biased from 3 to 40% (less bias for small concentric regions in large ROIs) while the EM values (50 iterations) were less than 0.5% biased. For the noisy simulations, the EM estimates had 2 to 4 times smaller standard deviation than small concentric FBP regions and comparable noise to large FBP regions which are significantly biased. This demonstrates that use of a physical statistical, and anatomical information with the approplate algorithm can significantly improve the accuracy of tomographic ROI measurements.« less
  • A number of advanced reactor concepts incorporate intrinsic design features that act to safely limit reactor response during upsets. In the integral fast reactor (IFR) concept, for example, metallic fuel is used to provide sufficient negative reactivity feedback to achieve a safe response for a number of unprotected upsets. In reactors such as the IFR that rely on passive features for part of their safety, the licensing of these systems will probably require that they be periodically tested to verify proper operation. Commercial light water plants have similar requirements for active safety systems. The approach to testing considered in thismore » paper involves determining during normal operation the values of key reactor parameters that govern the unprotected reactor response and then using these values to predict upset response. The values are determined using the maximum likelihood method. If the predicted reactor response is within safe limits, then one concludes that the intrinsic safety features are operating correctly.« less
  • In this paper we discussed the nonparametric density estimation problem. We are most interested in an estimation which is {open_quotes}optimal{close_quotes} in some sense and can incorporate the prior information one knows about the density function. By applying the maximum likelihood method to the nonparametric density estimation problem and incorporating some type of prior information, say support of the density function, shape of the density function, etc, as constraints, we established an infinite dimensional constrained optimization model. We use the newly developed epi-analysis theory to the problem, we proved the consistency of constrained maximum likelihood estimators and in the case thatmore » the constraint set have the {open_quotes}uniformly approximation{close_quotes} property we proved the epi-distance convergence of the empirical problem to the limiting problem and hence establish the upper bound of some constrained maximum likelihood estimator by the epi-distance between the empirical problem and the limiting problem. Furthermore, numerical simulation examples have been implemented.« less