Conference: Publication Date: 29 October 2012Testing Method:

An improvement to the inversion algorithm based on the maximum entropy method (MEM) proposed by Wang et al. [1] is presented. The original algorithm aimed to remove unwanted effects in fast neutron imaging which result from an uncollimated source interacting with a finitely thick scintillator. The algorithm takes as an input the image from the thick scintillator (TS) and outputs a restored image which appears as if taken with an infinitesimally thin scintillator (ITS). However, the inversion process is heavily dependent on a linear model relating the ITS image to the TS image and in the inversion sequence, any error or noise in this model is carried through to the reconstructed image. Improving on the work of Wang et al. [1], we present a new hybrid semi-analytical approach to generate a more accurate and less noisy linear model. The new hybrid model increases accuracy by incorporating intra-scintillator scatter while lowering computation requirements by splitting the generation of the linear model into two phases.

References

1. Wang, H., V. Tang, J. McCarrick and S. Moran, “Reconstruction Algorithm for Point Source Neutron Imaging through
Finite Thickness Scintillator,” Nuclear Instruments and Methods A, DOI:10.1016/j.nima.2012.07.018.
2. Hall, J., B. Rusnak and P. Fitsos, “High-energy Neutron Imaging Development at LLNL,” in 8th World Conference on
Neutron Radiography, No. 8th, Gaithersburg, MD, 2006.
3. Chang, C. and C. Lin, LIBSVM: A Library for Support Vector Machines, 2001.
4. Cortes, C. and V. Vapnik, Support-vector Networks, Machine Learning 20, 1995.
5. Smola, A.J. and B. Scholkopf, A Tutorial on Support Vector Regression, Statistics and Computing, 14, 2004.
6. Gull, S.F. and G.J. Daniell, “Image-reconstruction from Incomplete and Noisy Data,” Nature 272, 1978.
7. MCNP X-5 Monte Carlo Team, MCNP a General Purpose Monte Carlo n-particle Transport Code, Tech. rep., Los
Alamos National Laboratory, 2003

Metrics

Usage | Shares |
---|---|

Total Views 14 Page Views |
Total Shares 0 Tweets |

14 0 PDF Downloads |
0 0 Facebook Shares |

Total Usage | |

14 |