Material classification from sparse spectral X-ray CT using vectorial total variation based on L infinity norm

Doniyor Jumanazarov*, Jakeoung Koo, Jan Kehres, Henning F. Poulsen, Ulrik L. Olsen, Mihai Iovea

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The development of energy resolving photon counting detectors (PCD) has paved the way to spectral X-ray Computed Tomography (CT), with which one can simultaneously extract the energy dependence of a material's linear attenuation coefficient (LAC). Spectral CT has proved to be an advanced technique to classify materials based on their physical properties such as electron density (ρe) and effective atomic number (Zeff). However, the application of spectral CT may be hindered by the poor image reconstruction quality when used for material identification in security screening where rapid scanning is required. Image reconstruction from few projections or low radiation exposure time would enable rapid scanning. The reconstruction quality for each energy bin may also be degraded since the division of photon counts into multiple energy bins naturally leads to higher noise levels. In this work, we explore how to perform accurate spectral CT reconstructions from such data, and propose Lnorm-based vectorial total variation (L-VTV) regularization that uses correlations between multiple energy bins. Using experimental data acquired with a custom laboratory instrument for spectral CT, the L-VTV is tested on “real life” phantoms consisting of materials in the range of 6 ≤ Zeff ≤ 15. For material classification from only 7 projections, the L-VTV gives the relative deviations of 3.5% for ρe and 2.4% for Zeff, whereas the total nuclear variation (TNV) of another state-of-the-art joint reconstruction and the total variation (TV) yield those of 3.4% and 3.1%, and 3.8% and 4.2%, respectively. The L-VTV is now ready for use in security screening.
Original languageEnglish
Article number111864
JournalMaterials Characterization
Volume187
Number of pages13
ISSN1044-5803
DOIs
Publication statusPublished - 2022

Keywords

  • Spectral X-ray CT
  • Electron density
  • Effective atomic number
  • Joint reconstruction
  • Security screening

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