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Neutron depth profiling (NDP) is a non-destructive analysis method which is widely used in lithium batteries, semiconductors, and other complex and high-precision industries. The NDP spectrum is the second particles of the interaction between neutrons and target nuclides, and then the content and spatial information of the target nuclides in the measured samples are obtained by unfolding the NDP spectrum. At present, the common NDP spectrum unfolding algorithm is the maximum likelihood expectation maximization (MLEM) algorithm. But in some case, the MLEM algorithm falls into the local optimal solution. In this paper, a hybrid PSO-MLEM algorithm by taking advantages of the wide search range of PSO (particle swarm optimization) and the fast convergence speed of MLEM was proposed. In the PSO-MLEM algorithm, the dynamic acceleration factor was used to balance the local optimal and the global optimal on the particle displacement in each iteration, which improved the convergence speed and the accuracy of the algorithm. The PSO-MLEM algorithm was applied to unfold the NDP spectra of lithium batteries with 0, 5, and 10 hours of charging and discharging, which were simulated by Geant4 tool. The unfolding results of PSO-MLEM algorithm were compared to the results of PSO algorithm, MLEM algorithm and singular value decomposition solving least squares (SVDLS) algorithm. The correlation coefficients of the unfolding result by PSO-MLEM algorithm and the reference distributions are 0.993, 0.984, and 0.946, respectively, and the relative average errors are 14.46%, 9.84%, and 9.41%. Compared with PSO algorithm, the convergence speed of PSO-MLEM algorithm is improved from 800 times to 100 times, and the relative error is reduced from about 50% to about 10%. To the MLEM algorithm, the PSO-MLEM algorithm improves the global optimization capability and avoids the problem of local optimal solution caused by the influence of the initial value of the MLEM algorithm, especially with the result of 0 hour. The SVDLS algorithm is worked well in unfolding NDP spectra except the NDP spectrum of lithium battery at 0 hour. Compared to result of SVDLS algorithm, the PSO-MLEM algorithm has better convergence properties and is numerically stable.

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The purpose of this study is to optimize the collimator mask design for the rotational modulation collimator (RMC) imaging system. First, we optimized the slat and slit width of the collimator mask which affects the number of particles entering the detector and, thus, alters the morphology of modulation patterns. However, the conventional collimator mask design with bilateral symmetry posed an inherent ambiguity issue in the estimation of the source location in the reconstructed image. Therefore, we propose a new asymmetric collimator mask design which can solve the ambiguity problem for the development of the dual-particle imaging system. Modulation patterns for various types of asymmetric mask designs were calculated with Monte Carlo simulations, and modulation and transmission efficiencies were compared with ones for the conventional RMC mask design to evaluate the nominal performance of the new mask designs. The proposed asymmetric collimator mask removed the ambiguous artifact from the reconstructed image without compromising transmission and modulation efficiencies. In addition, one can even expect to achieve better spatial resolution in the reconstructed image with the proposed new asymmetric mask design, as compared with the asymmetric mask design suggested from the previous study. • This paper proposes a new asymmetric collimator design for the RMC imaging system. • The morphology of modulation patterns was studied varying several design parameters. • The proposed design removed the artifact without compromising the imaging capability. • The proposed mask design was shown to exhibit an improved performance. [ABSTRACT FROM AUTHOR]