Direct atomic number reconstruction of dual energy cargo radiographs using a semiempirical transparency model

Dual energy cargo inspection systems are sensitive to both the area density and the atomic number of an imaged container due to the Z dependence of photon attenuation. The ability to identify cargo contents by their atomic number enables improved detection capabilities of illicit materials. Existing methods typically classify materials into a few material classes using an empirical calibration step. However, such a coarse label discretization limits atomic number selectivity and can yield inaccurate results if a material is near the midpoint of two bins. This work introduces a high resolution atomic number prediction method by minimizing the chi-squared error between measured transparency values and a semiempirical transparency model. Our previous work showed that, through a simple calibration step, the semiempirical transparency model can capture second order effects such as scattering and thus yield more accurate atomic number reconstruction capabilities than a fully analytic model. This method was benchmarked using two simulated radiographic phantoms, demonstrating the ability to obtain accurate material predictions on noisy input images by incorporating an image segmentation step. Furthermore, this approach can be adapted to identify shielded objects if the properties of the shielding are known, taking advantage of the closed-form nature of the transparency model.