Characterizing, Modeling and Mitigating Texturing in X-Ray Diffraction Tomography
R1-C3

X-ray diffraction tomography (XRDT) has the potential to dramatically improve the performance of X-ray-based explosives detection systems because of its ability to identify concealed materials based on their microscopic atomic and/or molecular structures. While this sensitivity to molecular composition is necessary for accurately assessing a host of benign and threat materials, XRDT may at times be too sensitive.  The details of the measured X-ray diffraction scatter signatures associated with a given material may vary based on a variety of factors (e.g., grain size or processing history), which presents significant challenges to real-world implementations of XRDT.

In this project, we seek to understand and mitigate these challenges by:

  1. creating a versatile database containing many instantiations of XRD signatures for a broad range of materials of interest
  2. incorporating the database into comprehensive numerical modeling tools that allow for the simulation of various XRDT measurement architectures
  3. analyzing the impact of material variability on specific XRDT measurement schemes.

Along the way, we plan to collaborate with the University of Rhode Island and share our findings with authorized parties (e.g., vendors and government labs) currently investigating practical implementations of next-generation XRDT scanners.

X-ray diffraction tomography may hold the key to improved security and throughput at our airports but, to unlock its potential, we must first understand the natural variability of threat and benign materials.
R1-C3 Team
Project Leader
  • Joel Greenberg
    Assistant Research Professor
    Duke University
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Faculty and Staff Currently Involved in Project
  • Anuj J. Kapadia
    Assistant Professor
    Duke University
    Email

  • Scott D. Wolter
    Associate Professor of Engineering
    Elon University
    Email