We employ an adaptive measurement system, based on sequential hypotheses testing (SHT) framework, for detecting material-based threats using experimental data acquired on an X-ray experimental testbed system. This testbed employs 45-degree fan-beam geometry and 15 views over a 180-degree span to generate energy sensitive X-ray projection data. Using this testbed system, we acquire multiple view projection data for 200 bags. We consider an adaptive measurement design where the X-ray projection measurements are acquired in a sequential manner and the adaptation occurs through the choice of the optimal "next" source/view system parameter. Our analysis of such an adaptive measurement design using the experimental data demonstrates a 3x-7x reduction in the probability of error relative to a static measurement design. Here the static measurement design refers to the operational system baseline that corresponds to a sequential measurement using all the available sources/views. We also show that by using adaptive measurements it is possible to reduce the number of sources/views by nearly 50% compared a system that relies on static measurements.
Ratchaneekorn Thamvichai, Liang-Chih Huang, Amit Ashok, Qian Gong, David Coccarelli, Joel A. Greenberg, Michael E. Gehm, and Mark A. Neifeld, "Adaptive x-ray threat detection using sequential hypotheses testing with fan-beam experimental data (Conference Presentation)," Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 101870A (Presented at SPIE Defense + Security: April 12, 2017; Published: 7 June 2017); https://doi.org/10.1117/12.2266756.5458109118001.
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