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Spectral X-ray Computed Tomography (CT) exploits advanced photon counting detectors (PCD) to measure a material's spectrally resolved linear attenuation coefficient (LAC) with the simultaneous spectral acquisition at multiple energy thresholds. We present a method for material classification using spectral CT. The method employs a basis material decomposition model and estimates the effective atomic number (Z eff) from the spectral LAC measurements. Basis material decomposition builds on the fact that the LAC of any material can be well approximated by a linear combination of LACs of basis materials, with known Z eff values at the extremes of the relevant Z eff range. Spectral distortions of the energy spectrum due to the physical interactions between photons and the multi-energy-bin PCD such as charge sharing and photon pileup are corrected by a spectral correction algorithm. The validation of the method has been performed with experimental data acquired with a custom laboratory instrument for spectral CT, examining "real life" phantoms with materials in the range of 6 ≤ Z eff ≤ 15. The classification performance is estimated for different numbers of projections, energy bins and basis materials in LAC decomposition. When using just 12 projections, 15 energy bins and two basis materials, the method gives a relative deviation of 2.2% for Z eff , while this deviation is 5.9% when spectral correction is not used. The classification method is now ready for use in security screening where modern spectral CT systems are employed. [ABSTRACT FROM AUTHOR]

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 (ρ e) and effective atomic number (Z eff). 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 L ∞ norm-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 ≤ Z eff ≤ 15. For material classification from only 7 projections, the L ∞ -VTV gives the relative deviations of 3.5% for ρ e and 2.4% for Z eff , 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. • A joint reconstruction algorithm with L infinity norm improves image reconstruction. • The algorithm is robust to few projections and/or low photon counts. • Using correlations between multiple energy bins enhances material identification. • L infinity norm in the algorithm leads to suppression of outliers in gradient magnitudes. • High noise levels in specific energy bins can be alleviated using unaffected bins. [ABSTRACT FROM AUTHOR]

We present a method for material classifications in spectral X-ray Computed Tomography (SCT) taking advantage of energy-resolving 2D detectors to simultaneously extract the energy dependence of a material's linear attenuation coefficient (LAC). The method employs an attenuation decomposition presented by Alvarez et al., and estimates system-independent material properties of electron density (ρ e) and effective atomic number (Z eff), independent of the scanner, from the energy-dependent LAC measurements. The method uses a spectral correction algorithm and the energy range is truncated to exclude bins with photon starvation and spectral distortion present even after correction of detector response. A novel technique of energy bin selection is used for optimized classification performance. The method is tested against another SCT classification method called SRZE for inspecting materials in the range of 6 ≤ Z eff ≤ 23. Our method aims at an increase in the speed of pot processing workflow after the data acquisition, and it achieves explicitly up to 32 times better time efficiency for the reconstruction with comparable accuracy for a range of materials important in threat detection. • Just two spectral detector energy bins needed for accurate material identification. • Correction of the spectral detector response is required for accurate classification. • The low- to high-energy threshold are optimized above 100 keV. • Non-overlapping energy bins improve classification performance. [ABSTRACT FROM AUTHOR]