Muon Tomography imaging improvement using optimized scattering tracks data based on Maximum Likelihood Method

TL;DR

This paper enhances muon tomography imaging by applying a maximum likelihood method to optimize scattering track predictions, significantly improving image accuracy over traditional PoCA algorithms.

Contribution

It introduces a maximum likelihood approach based on Gaussian-like muon scattering distributions to improve reconstruction accuracy in muon tomography.

Findings

Maximum likelihood method achieves perfect discrimination of materials.

Discriminate ratio is about 15% higher with maximum likelihood.

Reconstruction image accuracy is greatly improved.

Abstract

Point of colsest Approche algorithm (PoCA) based on the formalism of muon radiogra- phy using Multiple Coulomb scattering (MCS) as information source is previously used to obtain the reconstruction image of high Z material. The low accuracy of reconstruction image is caused by two factors: the flux of natural muon and the assumption of single scattering in PoCA algo- rithm. In this paper, the maximum likelihood method based on the characteristics of Gaussian-like distribution of muon tracks by MCS is used to predict the optimal track of outgoing muon. The receiver operating characteristic (ROC) and the localization ROC (LROC) are used as two analysis methods to evaluate the quality of reconstruction image. From the results of simulation, the perfect discrimination of longitudinal materials could be well achieved by maximum likelihood algorithm and the discriminate ratio that is predicted by the maximum likelihood method is about 15% higher than that of predicted by PoCA algorithm method. It is seen that the maximum likelihood method can greatly improve the accuracy of reconstruction image.