KEYWORDS: Data modeling, X-rays, Education and training, Systems modeling, Process modeling, Design and modelling, Artificial intelligence, Virtual reality
Synthetic data has emerged as a critical piece of the machine-learning based approach to X-ray system design and automatic threat recognition development. Physics-based synthetic data integrates virtual models with a physics-based simulation engine, thereby granting users the capability to produce synthetic measurements based on arbitrary, user-specified input objects and materials. Such inputs can range from geometric phantoms that assist in system design to new threat materials and configurations that expedite ATR training in response to emerging threats. We introduce enhancements to the QSimRT virtual model generation pipeline. This incorporates the rapid creation of virtual models representing passenger luggage, stochastically generated electronics, and user-specified model variability for extensive ensemble production. We have employed these models in training ATRs within the aviation security domain. This presentation will discuss the model generation process, emphasize its pivotal features, and share preliminary results derived from the application of these models in ATR training.
The ability to determine the atomic structure or identify the material composition of a sample at high spatial resolution is paramount to a variety of research, imaging, and inspection tasks. We have developed a multi-modality x-ray transmission and x-ray diffraction imaging system that is compact and enables scanning of intact samples. To demonstrate the capabilities of the system, we characterize its spatial and momentum transfer resolution and provide examples of the contrast and utility of the system using a combination of resolution and anthropomorphic phantoms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.