  When dealing with material classification in baggage at airports, Dual-Energy
Computed Tomography (DECT) allows characterization of any given material with
coefficients based on two attenuative effects: Compton scattering and
photoelectric absorption. However, straightforward projection-domain
decomposition methods for this characterization often yield poor
reconstructions due to the high dynamic range of material properties
encountered in an actual luggage scan. Hence, for better reconstruction quality
under a timing constraint, we propose a splitting-based, GPU-accelerated,
statistical DECT reconstruction algorithm. Compared to prior art, our main
contribution lies in the significant acceleration made possible by separating
reconstruction and decomposition within an ADMM framework. Experimental
results, on both synthetic and real-world baggage phantoms, demonstrate a
significant reduction in time required for convergence.
