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

Abstract

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.

Material classification using basis material decomposition from spectral X-ray CT

“…The importance of the spectral correction for detector response artifacts in material identification using spectral CT was presented by Jumanazarov et el. [53]. In this work we therefore use the correction algorithm proposed by Dreier et al [54] to correct the spectral distortions in the PCD used.…”

“…These interactions result in a shift of the measured spectrum from the central energy channels towards low-and high-energies, and thereby large deviation of the extracted spectral LACs from the actual values. In a recent paper [53] we showed that the correction of the detector's spectral response for these distortions is required to correct the measured LACs and significantly enhance the material classification performance. A correction algorithm presented by Dreier et al [54] was used to correct for the spectral distortions occurring in the PCD.…”

Material classification using basis material decomposition from spectral x-ray CT

“…The importance of the spectral correction for detector response artifacts in material identification using spectral CT was presented by Jumanazarov et el. [53]. In this work we therefore use the correction algorithm proposed by Dreier et al [54] to correct the spectral distortions in the PCD used.…”

“…These interactions result in a shift of the measured spectrum from the central energy channels towards low-and high-energies, and thereby large deviation of the extracted spectral LACs from the actual values. In a recent paper [53] we showed that the correction of the detector's spectral response for these distortions is required to correct the measured LACs and significantly enhance the material classification performance. A correction algorithm presented by Dreier et al [54] was used to correct for the spectral distortions occurring in the PCD.…”

SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field

“…For NeRF-based methods, complicated scenes can be simplified through multiple spectral components compared to only normal RGB images, which is superior to previous NeRF-based methods in terms of geometry and texture reconstruction under complex scenes and image quality of the novel viewpoint synthesis. The motivation, that is, the need for spectral radiance fields is: spectral information can provide more details on the material constitution of objects in the scene, which have been utilized in classic vision tasks, such as material classification (Jumanazarov et al 2022). The idea of importing spectral information to rendering is a new perspective, which may provide inspiration for rendering and vision tasks.…”