MLEM Image Reconstruction Algorithm for Transmission Tomographic Gamma Scanning in Drummed Nuclear Wastes

MLEM Image Reconstruction Algorithm for Transmission Tomographic Gamma Scanning in Drummed Nuclear Wastes

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Author(s): Ai Jing He, Xian Guo Tuo, Rui Shi, Hong Long Zheng

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DOI: 10.18483/ijSci.1640 88 367 44-48 Volume 7 - May 2018


In this paper, 7 different samples and two radioactive isotopes (60Co and 137Cs) are used to fill a standard industrial waste bucket for simulating the real situation of drummed nuclear wastes. A transmission tomographic gamma scanning (TGS) measurement is carried out based on the TGS system independently developed by the project group for transmission image reconstruction. The data is processed by the MLEM iterative algorithm, it’s able to reconstruct the actual distribution of inhomogeneous media within the drum and to correct attenuation coefficients values of media under particular emission energies. The results show: the MLEM iterative algorithm can reconstruct clear TGS transmission images with accurate resolution of different densities which accord with the actual distributions of inhomogeneous media within the drum; the quality of reconstructed images tend to improve with the increase of transmission energy; the corrected attenuation coefficients values of 72 voxels within the drum under emission energy:661.661keV, 1173.238keV and 1332.513keV are consistent with the reference values, which proves the validity of this method.


Tomographic Gamma Scanning, Transmission Measurement, MLEM Iterative Algorithm, Image Reconstruction


  1. D.C.Camp, H.E.Martz, G.P.Roberson, et al: Nondestructive waste-drum assay for transuranic content by gamma-ray active and passive computed tomography, Nuclear Instruments and Methods in Physics Research, Vol.495 (2002) No.01, p.69-83.
  2. Kawasaki S, Kondo M, Izumi S, et al: Radioactivity measurement of drum package waste by a computed-tomography technique, International Journal of Radiation Applications and Instrumentation, Part A. Applied Radiation and Isotopes, Vol.41 (1990) No.10-11, p.983-987.
  3. Ben WANG, Ge WANG: Image Reconstruction Algorithms for Cone-Beam CT, CT Theory and Applications, Vol.10 (2001) No.02, p.1-8.
  4. W.Q WENG, D.Z WANG, Yong ZHANG: Reconstruction algorithm of transmission image in tomographic gamma scanning, Nuclear Techniques, Vol.31 (2008) No.05, p.396-400.
  5. Q.H ZHANG, H.Z SUI, Feng LU: Monte-Carlo Statistical Iteration Image Reconstruction Algorithm, Atomic Energy Science and Technology, Vol.37 (2003) No.06, p.555-557.
  6. G.X ZUO, Q.H ZHANG, X.L JIA: Experimental Research on the Algorithms for Emission Image Reconstruction of Tomographic Gamma Scanning, Nuclear Electronics&Detection Technology, Vol.32 (2012) No.11, p.1276-1279.
  7. Q.H ZHANG, Feng LI, W.H HUI: Research on tomographic gamma scanning technique, SCIENTIA SINICA Phys, Mech&Astron, Vol.40 (2010) No.08, p.983-991.
  8. R.J.Estep, T.H Prettyman, G.A Sheppard: Tomographic Gamma Scanning to Assay Heterogeneous Radioactive Waste, Nuclear science and engineering, Vol.118 (1994) No.03, p.145-152.
  9. Gordon R, Bender R, Herman G T: Algebraic Reconstruction Techniques (ART) for Three-dimensional Electron Miscroscropy and X-ray Photography, Theoretical Biology, Vol. 29 (1970) No.03, p.471-481.
  10. Keh-Shih Chuang, Meei-Ling Jana, Jay Wu: A maximum likelihood expectation maximization algorithm with thresholding, Computerized Medical Imaging and Graphics, Vol.29 (2005) No. 07, p.1-8.
  11. Prettyman T H, Cole R A, Estep R J, et al: A Maximum-likelihood Reconstruction Algorithm for Tomographic Gamma-ray Nondestructive Assay, Nuclear Instruments and Methods in Physics Research A, Vol.356 (1995) No.02-03, p.470-475.
  12. Shepp L A, Vardi Y: Maximum likelihood reconstruction for emission tomography, IEEE Trans Med Imag, Vol.13 (1994) No.01, p.601-609.

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International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
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