Multidimensional Data Processing Methods for Material Discrimination Using an Ideal X-Ray Spectrometric Photon Counting Detector

Beldjoudi, G.; Rebuffel, V.; Verger, L.; Kaftandjian, Valérie; Rinkel, Jean

doi:10.1109/tns.2011.2171721

“…Thus, optimizing the energy bin thresholds is desired for better performance when the number of energy bins used is low. A similar phenomenon was previously observed in radiography studies by Beldjoudi et al [20,25]. However, contrary to their findings, employing 15, 30, 45 and 90 energy bins does not enhance but rather deteriorates classification performance relative to optimized-bi-energy bins approach.…”

Section: Classification Performance As a Function Of The Number Of Ensupporting

confidence: 85%

“…The development of cadmium telluride (CdTe) energydiscriminating imaging detectors has paved the way for Spectral CT [17][18][19]. Spectral CT with an energy resolved photon counting detector (PCD) is proved to have a number of advantages over dual-energy CT for improving material identification [20,21]. This has resulted in a large interest in PCDs within security applications [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…In material identification by radiography, Rinkel et al [24] presented through experimental data that the false detection rate when using 90 energy bins with a PCD was reduced by a factor of up to 2 and more than 3 (for thin objects) compared to an optimised dual-energy bins approach applied both on the same PCD data and to a dual-layer sandwich detector, re-spectively. Beldjoudi et al [20,25] showed through simulations in Radiography that PCDs with data condensed into two energy bins enhanced recognition of single materials by over 50% relative to dual-layer sandwich detectors. Increasing the number of energy bins resulted in a further rise of 80% in material identification with 90 energy bins compared to dual-layer sandwich detectors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

System-independent material classification through X-ray attenuation decomposition from spectral X-ray CT

Jumanazarov

¹

,

Koo

²

,

Busi

³

et al. 2020

NDT & E International

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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 (e) and effective atomic number (ef f), 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 ≤ ef f ≤ 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.

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“…12 Radiography studies also showed that PCDs have clear advantages over dual-layer sandwich detectors in reducing the false detection rate. [13][14][15] The poor spectral separation in the dual-layer sandwich detectors was found to be the major reason for the lower performance. This has attracted significant attention in PCDs for material characterization within security applications.…”

Section: Introductionmentioning

confidence: 99%

Significance of the spectral correction of photon counting detector response in material classification from spectral x-ray CT

Jumanazarov

¹

,

Koo

²

,

Poulsen

³

et al. 2022

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Photon counting imaging detectors (PCD) has paved the way for the emergence of Spectral X-ray Computed Tomography (SCT), which simultaneously measures a material's linear attenuation coefficient (LAC) at multiple energies defined by the energy thresholds. In previous work SCT data was analysed with the SIMCAD method for material classifications. The method measures system-independent material properties such as electron density, ρ e and effective atomic number, Z eff to identify materials in security applications. The method employs a spectral correction algorithm that reduce the primary spectral distortions from the raw data that arise from the detector response: charge sharing and weighting potential cross-talk, fluorescence radiation, scattering radiation, pulse pile up and incomplete charge collection. In this work, using real experimental data we analyze the influence of the spectral correction on material classification performance in security applications. We use a vectorial total variation (L ∞ -VTV) as a convex regularizer for image reconstruction of the spectral sinogram. This reconstruction algorithm employs a L ∞ norm to penalize the violation of the inter energy bin dependency, resulting in strong coupling among energy bins. Due to the strong inter-bin correlation, L ∞ -VTV leads to noticeably better performance compared to bin-by-bin reconstructions including SIRT and total variation (TV) reconstruction algorithms. The image quality was evaluated with the correlation coefficient that is computed relative to ground-truth images. A positive weighting parameter defines the strength of the L ∞ -VTV regularization term and thus controls the trade-off between a good match to spectral sinogram data and a smooth reconstruction in both the spatial and spectral dimension. The classification accuracy both for raw and corrected data is analyzed over a set of weighting parameters. For material classification, we used 20 different materials for calibrating the SIMCAD method and 15 additional materials in the range of 6 ≤ Z eff ≤ 15 for evaluating the classification performance. We show that the correction algorithm accurately reconstructs the measured attenuation curve, and thus gives higher detection rates. We show that using the spectral correction leads to an accuracy increase of 1.6 and 3.8 times in estimating ρ e and Z eff , respectively.

show abstract

“…12 Radiography studies also showed that PCDs have clear advantages over dual-layer sandwich detectors in reducing the false detection rate. [13][14][15] The poor spectral separation in the dual-layer sandwich detectors was found to be the major reason for the lower performance. This has attracted significant attention in PCDs for material characterization within security applications.…”

Section: Introductionmentioning

confidence: 99%

The significance of the spectral correction of photon counting detector response in material classification from spectral x-ray CT

Jumanazarov

¹

,

Koo

²

,

Poulsen

³

et al. 2021

Quantum Optics and Photon Counting 2021

View full text Add to dashboard Cite

Photon counting imaging detectors (PCD) has paved the way for the emergence of Spectral X-ray Computed Tomography (SCT), which simultaneously measures a material's linear attenuation coefficient (LAC) at multiple energies defined by the energy thresholds. In previous work SCT data was analysed with the SIMCAD method for material classifications. The method measures system-independent material properties such as electron density, ρ e and effective atomic number, Z eff to identify materials in security applications. The method employs a spectral correction algorithm that reduce the primary spectral distortions from the raw data that arise from the detector response: charge sharing and weighting potential cross-talk, fluorescence radiation, scattering radiation, pulse pile up and incomplete charge collection. In this work, using real experimental data we analyze the influence of the spectral correction on material classification performance in security applications. We use a vectorial total variation (L ∞ -VTV) as a convex regularizer for image reconstruction of the spectral sinogram. This reconstruction algorithm employs a L ∞ norm to penalize the violation of the inter energy bin dependency, resulting in strong coupling among energy bins. Due to the strong inter-bin correlation, L ∞ -VTV leads to noticeably better performance compared to bin-by-bin reconstructions including SIRT and total variation (TV) reconstruction algorithms. The image quality was evaluated with the correlation coefficient that is computed relative to ground-truth images. A positive weighting parameter defines the strength of the L ∞ -VTV regularization term and thus controls the trade-off between a good match to spectral sinogram data and a smooth reconstruction in both the spatial and spectral dimension. The classification accuracy both for raw and corrected data is analyzed over a set of weighting parameters. For material classification, we used 20 different materials for calibrating the SIMCAD method and 15 additional materials in the range of 6 ≤ Z eff ≤ 15 for evaluating the classification performance. We show that the correction algorithm accurately reconstructs the measured attenuation curve, and thus gives higher detection rates. We show that using the spectral correction leads to an accuracy increase of 1.6 and 3.8 times in estimating ρ e and Z eff , respectively.

show abstract

Multidimensional Data Processing Methods for Material Discrimination Using an Ideal X-Ray Spectrometric Photon Counting Detector

Cited by 9 publications

References 23 publications

System-independent material classification through X-ray attenuation decomposition from spectral X-ray CT

System-independent material classification through X-ray attenuation decomposition from spectral X-ray CT

Significance of the spectral correction of photon counting detector response in material classification from spectral x-ray CT

The significance of the spectral correction of photon counting detector response in material classification from spectral x-ray CT

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