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 poor results if a material is near the midpoint of two bins. Alternative methods use an analytic transparency model to make material predictions. However, this model neglects second order effects such as scattering, resulting in an inherent model bias and thus inaccurate atomic number predictions.
This work introduces a hybrid approach by defining a semiempirical transparency model. An empirical calibration step is performed to tune model parameters, enabling the semiempirical model to show significantly improved agreement with simulated transparency measurements compared to a fully analytic approach. Furthermore, this framework is capable of making more precise material predictions than existing empirical methods and shows better extrapolation to materials which were not included during the calibration step. 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 after first determining the properties of the shielding, taking advantage of the closed-form nature of the transparency model. Using this framework, manufacturers can perform a simple calibration step to enable more precise Z reconstruction capabilities, which has the potential to significantly improve the performance of existing dual energy radiographic systems.