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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.|
|Dates:||October 16-19, 2006|
|Keywords:||cross-validation,filtered back projection,fuel cells,neutron transmission tomography,penalized maximum likelihood,statistical learning.|