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Statistical Learning Methods for Neutron Transmission Tomography of Fuel Cells



Kevin Coakley, Dominic F. Vecchia, Daniel S. Hussey


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 Title
Proceedings of 8th World Conference on Neutron Radiography.
Conference Dates
October 16-19, 2006
Conference Location
Gaithersburg, MD, USA


cross-validation, filtered back projection, fuel cells, neutron transmission tomography, penalized maximum likelihood, statistical learning.


Coakley, K. , Vecchia, D. and Hussey, D. (2008), Statistical Learning Methods for Neutron Transmission Tomography of Fuel Cells, Proceedings of 8th World Conference on Neutron Radiography., Gaithersburg, MD, USA (Accessed April 14, 2024)
Created July 7, 2008, Updated October 12, 2021