4.5 Article

High Spatial Resolution Tomographic Gamma Scanning Reconstruction With Improved MLEM Iterative Algorithm Based on Split Bregman Total Variation Regularization

Journal

IEEE TRANSACTIONS ON NUCLEAR SCIENCE
Volume 68, Issue 12, Pages 2762-2770

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNS.2021.3125001

Keywords

Image reconstruction; Spatial resolution; Attenuation; Tomography; Gamma-rays; Attenuation measurement; Radioactive pollution; Anisotropic and isotropic total variation (ITV); image reconstruction; maximum likelihood expectation maximization (MLEM); split Bregman; tomographic gamma scanning (TGS)

Funding

  1. National Natural Science Foundation of China [41874213, U19A2086, 42074218]
  2. Plan Project of Key Technology of Zigong City [2020YGJC05]
  3. Sichuan Science and Technology Program [2021YJ0328, 22CXRC0067]

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High spatial resolution tomographic gamma scanning (TGS) reconstruction is crucial for radioassay of drummed low-level radioactive waste. Sparse sampling and improved algorithms such as MLEM-TVM have been applied to achieve accurate imaging results in a shorter scanning time, enhancing radionuclide positioning and radioactivity reconstruction accuracy.
High spatial resolution tomographic gamma scanning (TGS) reconstruction is very important for the radioassay of drummed low-level radioactive waste. High spatial resolution means that the divided voxels are finer. Due to the large size of the drum, the traditional image reconstruction method based on complete samples takes a long time to scan. To limit the scanning time of the drum, sparse sampling is required. The maximum likelihood expectation maximization (MLEM) is widely used in TGS image reconstruction from projection data, but for high spatial resolution TGS imaging, its quality is insufficient to accurately describe the media boundary and determine radioactivity. The improved MLEM algorithm based on total variation (TV) regularization, such as the MLEM- TV minimization (TVM) algorithm, has been applied to reconstruct high spatial resolution TGS images. The split Bregman algorithm can quickly solve the partial differential equations of TV regularization. In this work, the split Bregman anisotropic TV (SBATV) and the split Bregman isotropic TV (SBITV) are the first time adopted to improve the iterative process of the MLEM algorithm, which are MLEM- SBATV and MLEM- SBITV. Experimental results show that both the MLEM- SBATV algorithm and the MLEM- SBITV algorithm can accurately reconstruct high spatial resolution TGS transmission images with sparse sampling. The MLEM- SBITV algorithm performs better in reconstructing the TGS emission images from sparse sampling than the traditional MLEM, MLEM- TVM, and MLEM- SBATV algorithms, increasing radionuclide positioning and radioactivity reconstruction accuracy.

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