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Statistical Reconstruction for Cosmic Ray Muon Tomography
- Source :
- IEEE Transactions on Image Processing. 16:1985-1993
- Publication Year :
- 2007
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2007.
-
Abstract
- Highly penetrating cosmic ray muons constantly shower the earth at a rate of about 1 muon per cm2 per minute. We have developed a technique which exploits the multiple Coulomb scattering of these particles to perform nondestructive inspection without the use of artificial radiation. In prior work [1]-[3], we have described heuristic methods for processing muon data to create reconstructed images. In this paper, we present a maximum likelihood/expectation maximization tomographic reconstruction algorithm designed for the technique. This algorithm borrows much from techniques used in medical imaging, particularly emission tomography, but the statistics of muon scattering dictates differences. We describe the statistical model for multiple scattering, derive the reconstruction algorithm, and present simulated examples. We also propose methods to improve the robustness of the algorithm to experimental errors and events departing from the statistical model.
- Subjects :
- Iterative method
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Cosmic ray
Image processing
Iterative reconstruction
Radiation
Sensitivity and Specificity
Nondestructive testing
Image Interpretation, Computer-Assisted
Expectation–maximization algorithm
Medical imaging
Computer Simulation
Tomography
Models, Statistical
Muon tomography
Tomographic reconstruction
Muon
Scattering
Cosmic ray muons
business.industry
Reproducibility of Results
Statistical model
Reconstruction algorithm
Image Enhancement
Computer Graphics and Computer-Aided Design
Data Interpretation, Statistical
business
Algorithm
Algorithms
Cosmic Radiation
Software
Subjects
Details
- ISSN :
- 19410042 and 10577149
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Image Processing
- Accession number :
- edsair.doi.dedup.....9a90972d3cd917cd0e267459ce633bc4