Information optimal compressive x-ray threat detection

We present an information-theoretic approach to X-ray measurement design for threat detection in passenger bags. Unlike existing X-ray systems that rely of a large number of sequential tomographic projections for threat detection based on 3D reconstruction, our approach exploits the statistical priors on shape/material of items comprising the bag to optimize multiplexed measurements that can be used directly for threat detection without an intermediate 3D reconstruction. Simulation results show that the optimal multiplexed design achieves higher probability of detection for a given false alarm rate and lower probability of error for a range of exposure (photon) budgets, relative to the non-multiplexed measurements. For example, a 99% detection probability is achieved by optimal multiplexed design requiring 4x fewer measurements than non-multiplexed design.