Modeling realistic virtual objects within a high-throughput x-ray simulation framework

David Coccarelli; Michael E. Gehm; Joel A Greenberg

doi:10.1117/12.2558947

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19 May 2020 Modeling realistic virtual objects within a high-throughput x-ray simulation framework

David Coccarelli, Michael E. Gehm, Joel A Greenberg

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Proceedings Volume 11404, Anomaly Detection and Imaging with X-Rays (ADIX) V; 1140402 (2020) https://doi.org/10.1117/12.2558947
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

X-ray simulation of realistic object models is relevant across all areas in which X-ray systems are employed, including medical, industrial, and security applications. A particularly exciting area of impact stems from the development of machine learning approaches to classification, detection, and data processing. The continued development of these techniques requires large labeled datasets. Traditionally, this data needed to be collected with physical machines, creating steep logistical challenges. Moreover, the testing and evaluation of such X-ray scanners present their own challenges, as machines need to be shipped to a site capable of handling certain anomalies. To help alleviate these burdens, virtual models and simulations can be used in lieu of empirical measurements. The confluence of powerful computers and advanced data processing techniques presents an opportunity to develop tools to aid in dataset creation as well as system analysis. We present efforts toward the maturity of such tools. Building on previous work to validate the performance of simulation software, we show how modeling realistic virtual objects can produce data representative of real-world measurements. Furthermore, we present the efficiency of such an approach that leverages advances in computer graphics, ray-tracing utilities, and GPU hardware.

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David Coccarelli, Michael E. Gehm, and Joel A Greenberg "Modeling realistic virtual objects within a high-throughput x-ray simulation framework", Proc. SPIE 11404, Anomaly Detection and Imaging with X-Rays (ADIX) V, 1140402 (19 May 2020); https://doi.org/10.1117/12.2558947

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