Enhanced local tomography
Abstract
Local tomography is enhanced to determine the location and value of a discontinuity between a first internal density of an object and a second density of a region within the object. A beam of radiation is directed in a predetermined pattern through the region of the object containing the discontinuity. Relative attenuation data of the beam is determined within the predetermined pattern having a first data component that includes attenuation data through the region. In a first method for evaluating the value of the discontinuity, the relative attenuation data is inputted to a local tomography function .function..sub..LAMBDA. to define the location S of the density discontinuity. The asymptotic behavior of .function..sub..LAMBDA. is determined in a neighborhood of S, and the value for the discontinuity is estimated from the asymptotic behavior of .function..sub..LAMBDA.. In a second method for evaluating the value of the discontinuity, a gradient value for a mollified local tomography function .gradient..function..sub..LAMBDA..epsilon. (x.sub.ij) is determined along the discontinuity; and the value of the jump of the density across the discontinuity curve (or surface) S is estimated from the gradient values.
- Inventors:
-
- Los Alamos, NM
- Manhattan, KS
- Issue Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- OSTI Identifier:
- 870586
- Patent Number(s):
- 5550892
- Assignee:
- Regents of University of California (Alameda, CA)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06T - IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- enhanced; local; tomography; determine; location; value; discontinuity; internal; density; region; beam; radiation; directed; predetermined; pattern; containing; relative; attenuation; data; determined; component; method; evaluating; inputted; function; lambda; define; asymptotic; behavior; neighborhood; estimated; gradient; mollified; epsilon; ij; jump; curve; surface; values; tomography function; local tomography; predetermined pattern; internal density; /378/
Citation Formats
Katsevich, Alexander J, and Ramm, Alexander G. Enhanced local tomography. United States: N. p., 1996.
Web.
Katsevich, Alexander J, & Ramm, Alexander G. Enhanced local tomography. United States.
Katsevich, Alexander J, and Ramm, Alexander G. Mon .
"Enhanced local tomography". United States. https://www.osti.gov/servlets/purl/870586.
@article{osti_870586,
title = {Enhanced local tomography},
author = {Katsevich, Alexander J and Ramm, Alexander G},
abstractNote = {Local tomography is enhanced to determine the location and value of a discontinuity between a first internal density of an object and a second density of a region within the object. A beam of radiation is directed in a predetermined pattern through the region of the object containing the discontinuity. Relative attenuation data of the beam is determined within the predetermined pattern having a first data component that includes attenuation data through the region. In a first method for evaluating the value of the discontinuity, the relative attenuation data is inputted to a local tomography function .function..sub..LAMBDA. to define the location S of the density discontinuity. The asymptotic behavior of .function..sub..LAMBDA. is determined in a neighborhood of S, and the value for the discontinuity is estimated from the asymptotic behavior of .function..sub..LAMBDA.. In a second method for evaluating the value of the discontinuity, a gradient value for a mollified local tomography function .gradient..function..sub..LAMBDA..epsilon. (x.sub.ij) is determined along the discontinuity; and the value of the jump of the density across the discontinuity curve (or surface) S is estimated from the gradient values.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 1996},
month = {Mon Jan 01 00:00:00 EST 1996}
}
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