Quantitative Object Reconstruction using Abel Transform Tomography and Mixed Variable Optimization
Abstract
Researchers at the Los Alamos National Laboratory (LANL) are interested in quantitatively reconstructing an object using Abel transform x-ray tomography. Specifically, they obtain a radiograph by x-raying an object and attempt to quantitatively determine the number and types of materials and the thickness of each material layer. Their current methodologies either fail to provide a quantitative description of the object or are generally too slow to be useful in practice. As an alternative, the problem is model here as a mixed variable programming (MVP) problem, in which some variables are nonnumeric and for which no derivative information is available. The generalized pattern search (GPS) algorithm for linearly constrained MVP problems is applied to the x-ray tomography problem, by means of the NOMADm MATLAB software package. Numerical results are provided for several test configurations of cylindrically symmetrical objects and show that, while there are difficulties to be overcome by researchers at LANL, this method is promising for solving x-ray tomography object reconstruction problems in practice.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2006
- Accession Number
- ADA446159
Entities
People
- Kevin R. O'reilly
Organizations
- Air Force Institute of Technology