Computed tomography imaging system design for shape threat detection

In the first part of this work, we present two methods for improving the shape-threat detection performance of x-ray computed tomography. Our work uses a fixed-gantry system employing 25 x-ray sources. We first utilize Kullback-Leibler divergence and Mahalanobis distance to determine the optimal single-source single-exposure measurement. The second method employs gradient search on Bhattacharyya bound on error rate (P_e) to determine an optimal multiplexed measurement that simultaneously utilizes all available sources in a single exposure. With limited total resources of 10⁶ photons, the multiplexed measurement provides a 41.8× reduction in P_e relative to the single-source measurement. In the second part, we consider multiple exposures and develop an adaptive measurement strategy for x-ray threat detection. Using the adaptive strategy, we design the next measurement based on information retrieved from previous measurements. We determine both optimal "next measurement" and stopping criterion to insure a target P_e using sequential hypothesis testing framework. With adaptive single-source measurements, we can reduce P_e by a factor of 40× relative to the measurements employing all sources in sequence. We also observe that there is a trade-off between measurement SNR and number of detectors when we study the performance of systems with reduced detector numbers.