Improved reconstruction and classification performance in a hybrid CT+XRD explosives detection system

Identifying and intercepting prohibited items and explosives is a critical focus of aviation security. While computed tomography (CT) systems represent the industry standard for detecting explosives in baggage, x-ray diffraction imaging (XRDI) systems have shown increasing performance and commercial viability. Our approach to explosives detection involves the combination of CT and XRDI into a single, hybrid system where both the CT and XRDI data are utilized in the reconstruction and classification algorithms. In this work, we focus on comparing multiple reconstruction and classifier implementations and quantifying the resulting performance. Our analysis shows higher quality reconstructions lead to improved material separability, better classification performance (detection and false alarm rates), and reduces model uncertainty. Through this work, we demonstrate the relationship between improved quality of reconstructions and the separability of threat from non-threat objects in the domain of explosives detection.