Rapid simulation of X-ray scatter measurements for threat detection via GPU-based ray-tracing

Published

Journal Article

© 2019 Elsevier B.V. Scatter-based X-ray imaging has increased in prominence in areas ranging from cancer diagnostics to threat detection in aviation security, due largely to the development of new algorithms and components over the last decade. However, system design and algorithm development are often hindered by an inability to generate a sufficient amount of accurate data. To further the development of scatter-based systems, we created a rapid X-ray scatter simulation tool built with a GPU-centric, parallel ray-tracing framework (NVIDIA OptiX). This tool models a full range of X-ray imaging components and describes 3D objects formed with heterogeneous media using polygon meshes. The scatter simulation algorithm we used is similar to the previously-described hybrid approach; however, instead of employing Monte Carlo techniques, we developed a purely analytical algorithm to sample the distribution of single-scatter events throughout the region of interest. The contribution of scattered photons to the measured signal is then calculated using the first-Born approximation. The accuracy of the implemented pipeline has been validated by comparing simulated against experimental data obtained with a laboratory X-ray scattering system and phantoms of varied materials. Using a single desktop computer with an NVIDIA GTX 770 GPU, we show that the scatter signal generated by an incident fan beam and recorded by a 125×125 element detector array can be simulated in on the order of a few to tens of minutes (depending on the object extent and complexity). As a point of comparison, simulations performed via CPU-based Monte Carlo tools, such as GEANT4 (i.e., the gold standard), can take up to tens of hours to achieve comparable results.

Full Text

Duke Authors

Cited Authors

  • Gong, Q; Greenberg, JA; Stoian, RI; Coccarelli, D; Vera, E; Gehm, ME

Published Date

  • June 15, 2019

Published In

Volume / Issue

  • 449 /

Start / End Page

  • 86 - 93

International Standard Serial Number (ISSN)

  • 0168-583X

Digital Object Identifier (DOI)

  • 10.1016/j.nimb.2019.03.006

Citation Source

  • Scopus