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Feasibility of Coded Source Neutron Transmission Tomography

Published

Author(s)

Kevin Coakley, Daniel S. Hussey

Abstract

In a simulation experiment, we study the feasibility of coded source neutron transmission tomography for imaging water density in fuel cells at the NIST neutron imaging facility. In standard two-dimensional transmission tomography, one reconstructs a spatially varying attenuation image based on many projections or views of an object. Here, we consider the limiting case where only one view is available. Rather than parallel beam sources, the projection data is produced by multiple pinhole sources. For a high count case where the object is near the sources and object magnification is approximately 200, and attenuation varies very smoothly in the object, we demonstrate that a penalized maximum likelihood method yields a reconstruction of attenuation that has a fractional root-mean-square prediction error of 5.8 percent. We determine the regularization parameter in the penalized likelihood method using a statistical learning method called two-fold cross-validation. As the object-to-source distance increases and object magnification in the detector plane decreases, the quality of the reconstruction deteriorates. At the NIST neutron imaging facility, object magnification in a coded source neutron imaging experiment would only be about 4. Due to this low magnification, even for the favorable case considered where attenuation varies very smoothly, we conclude that single-view coded source neutron transmission tomography is not a promising method for quantifying the spatial distribution of water density in a fuel cell.
Citation
Measurement Science & Technology
Volume
18

Keywords

coded source tomography, cross-validation, fuel cell, hydrogen economy, neutron imaging, penalized maximum likelihood, statistical learning, water density

Citation

Coakley, K. and Hussey, D. (2007), Feasibility of Coded Source Neutron Transmission Tomography, Measurement Science & Technology (Accessed June 13, 2024)

Issues

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Created December 31, 2006, Updated October 12, 2021