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Publication Citation: Statistical Learning Methods for Neutron Transmission Tomography of Fuel Cells

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Author(s): Kevin J. Coakley; Dominic F. Vecchia; Daniel S. Hussey;
Title: Statistical Learning Methods for Neutron Transmission Tomography of Fuel Cells
Published: July 08, 2008
Abstract: In a fuel cell, water is formed as a by-product of the reaction between hydrogen and oxygen. As a neutron beam passes through a fuel cell, it can undergo s-wave scattering. For the dry and wet states of a fuel cell, there are spatially varying neutron attenuation images. Ideally, the difference of these attenuation images is proportional to the water density in the fuel cell. We estimate a nonnegative residual attenuation image from joint analysis of the wet and dry state projection data using a penalized likelihood (PL) method with a Huber penalty function that has two adjustable regularization parameters. We also reconstruct using a Filtered Back Projection (FBP) method where we apodize the ramp filter by a window corresponding to a Gaussian kernel with an adjustable standard deviation. We determine the adjustable regularization parameters by a statistical learning method called two-fold cross-validation. We simulate projection data based on Hussey phantom images and neglect beam hardening effects, backgrounds, and model the number of detected photoelectrons as a compound Poisson process. For simulated data, the PL approach yields sharper reconstructions than does the FBP approach.
Proceedings: Proceedings of 8th World Conference on Neutron Radiography.
Pages: 10 pp.
Location: Gaithersburg, MD
Dates: October 16-19, 2006
Keywords: cross-validation;filtered back projection;fuel cells;neutron transmission tomography;penalized maximum likelihood;statistical learning.
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