A Semiempirical Transparency Model for Dual Energy Cargo Radiography Applications

TL;DR

This paper introduces a semiempirical model for dual energy cargo radiography that improves atomic number predictions by accounting for secondary effects neglected in traditional models, enhancing accuracy and robustness.

Contribution

The work develops a semiempirical transparency model that adjusts the free streaming model to better capture secondary effects, improving material identification in cargo inspection.

Findings

Improved atomic number reconstruction at high-Z materials.

Model maintains accuracy despite uncertainties in source spectra and detector response.

Demonstrates better extrapolation to unseen materials and thicknesses.

Abstract

Cargo containers passing through ports are scanned by non-intrusive inspection systems to search for concealed illicit materials. By using two photon beams with different energy spectra, dual energy inspection systems are sensitive to both the area density and the atomic number of cargo contents. Most literature on the subject assumes a simple exponential attenuation model for photon intensity in which only free streaming photons are detected. However, this approximation neglects second order effects such as scattering, leading to a biased model and thus incorrect material predictions. This work studies the accuracy of the free streaming model by comparing it to simulation outputs, finding that the model shows poor atomic number reconstruction accuracy at high-$Z$ and suffers significantly if the source energy spectra and detector response function are not known exactly. To address these challenges, this work introduces a semiempirical transparency model which modifies the free streaming model by rescaling different components of the mass attenuation coefficient, allowing the model to capture secondary effects ignored by the free streaming model. The semiempirical model displays improvement agreement with simulated results at high-$Z$ and shows excellent extrapolation to materials and thicknesses which were not included during the calibration step. Furthermore, this work demonstrates that the semiempirical model yields accurate atomic number predictions even when the source spectra and detector response are not known exactly. Using the semiempirical model, manufacturers can perform a simple calibration to enable more precise $Z$ reconstruction capabilities, which has the potential to significantly improve the performance of existing radiographic systems.