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

Download Project Report (Phase 2, Year 5).

The detection of contraband and explosives concealed within a large volume of confounding items is a challenging task.  X-ray based imaging has been central to this effort for nearly the last two decades, but conventional transmission-based methods lack the material specificity required to accurately detect the target items. X-ray diffraction tomography (XRDT) has the potential to dramatically improve the sensitivity and specificity 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, it may at times be too sensitive: the details of the measured X-ray diffraction scatter signatures can depend on a variety of factors relating to environmental history and conditions.  In this project, we seek to quantify, model, and overcome these challenges in order to help develop these next-generation X-ray detection technologies.

In this project, we will address these challenges by:

  • Creating a database containing tens of texture instantiations (e.g. different orientations, grain sizes, processing history, etc.) of scatter signatures for a broad range of materials of interest;
  • Incorporating the database into comprehensive modeling tools to allow accurate simulation of various measurement architectures; and
  • Analyzing the impact of texturing on XRDT imaging performance and studying new ways to measure and process the data to mitigate the deleterious effects of texturing.

Along the way, we plan to collaborate with ALERT research being conducted at the University of Rhode Island (Projects R1-A1, R1-B1, and R1-C2) 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
    Email

  • Anuj J. Kapadia
    Assistant Professor
    Duke University
    Email

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

Students Currently Involved in Project
  • Mehadi Hassan
    Duke University
  • Shobhit Sharma
    Duke University
  • Siyang Yuan
    Duke University
  • Mingxi Cheng
    Duke University
  • Bi Zhao
    Duke University
  • James Spencer
    Duke University
  • Joshua Carter
    Duke University
  • Yixiao Du
    Duke Kunshan University
  • Caley Buxton
    Duke University
  • Jeffrey Fenoli
    Duke University
  • Mary Esther Braswell
    Duke University
  • David Nacouzi
    Duke University
  • Michael Macalino
    Elon University
  • Chris Brittlebank
    Elon University
  • Jesse Yue
    Duke University
  • Talha Rehman
    Duke University/Berea College
  • Taylor Richards
    Duke University
  • Camen Royse
    NC State University
  • Taylor Smith
    Duke University
  • Sabrina Campelo
    Elon University
  • Brian Keohane
    Duke University
  • Chris MacGibbon
    Duke University
  • Abhiram Kondagunta
    William G. Enloe High School (Enlow Magnet High School), Raleigh, NC [through Duke University]
  • Kaitlyn Szekerczes
    Rising Sun High School, North East, MD [through Duke University]