Spectral X-ray CT for fast NDT using discrete tomography

Dimitris Kamilis, Nicholas Polydorides, Susanne Lee, Joseph Desjardins

Research output: Contribution to conferencePaperpeer-review

Abstract

We present progress in fast, high-resolution imaging, material classification, and fault detection using hyperspectral X-ray measurements. Classical X-ray CT approaches rely on data from many projection angles, resulting in long acquisition and reconstruction times. Additionally, conventional CT cannot distinguish between materials with similar densities. However, in additive manufacturing, the majority of materials used are known a priori. This knowledge allows to vastly reduce the data collected and increase
the accuracy of fault detection. In this context, we propose an imaging method for non-destructive testing of materials based on the combination of spectral X-ray CT and discrete tomography. We explore the use of spectral X-ray attenuation models and measurements to recover the characteristic functions of materials in heterogeneous media with piece-wise uniform composition. We show by means of numerical simulation that using spectral measurements from a small number of angles, our approach can alleviate the typical deterioration of spatial resolution and the appearance of streaking artifacts.
Original languageEnglish
Publication statusPublished - 11 Sep 2019
EventSolid Freeform Foundation Symposium 2019 - Hilton Hotel, Austin , Austin, United States
Duration: 12 Aug 201915 Aug 2019
http://sffsymposium.engr.utexas.edu

Conference

ConferenceSolid Freeform Foundation Symposium 2019
Abbreviated titleSFFS19
Country/TerritoryUnited States
CityAustin
Period12/08/1915/08/19
Internet address

Keywords

  • Additive manufacturing
  • machine learning
  • Tomography Scanners, X-Ray Computed

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