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Purpose: Photon counting imaging detectors (PCD) has paved the way for spectral x-ray computed tomography (spectral CT), which simultaneously measures a sample's linear attenuation coefficient (LAC) at multiple energies. However, cadmium telluride (CdTe)-based PCDs working under high flux suffer from detector effects, such as charge sharing and photon pileup. These effects result in the severe spectral distortions of the measured spectra and significant deviation of the extracted LACs from the reference attenuation curve. We analyze the influence of the spectral distortion correction on material classification performance. Approach: We employ a spectral correction algorithm to reduce the primary spectral distortions. We use a method for material classification that measures system-independent material properties, such as electron density, ρ e , and effective atomic number, Z eff . These parameters are extracted from the LACs using attenuation decomposition and are independent of the scanner specification. The classification performance with the raw and corrected data is tested on different numbers of energy bins and projections and different radiation dose levels. We use experimental data with a broad range of materials in the range of 6 ≤ Z eff ≤ 15 , acquired with a custom laboratory instrument for spectral CT. Results: 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, when the image reconstruction is performed from only 12 projections and the 15 energy bins approach is used. Conclusions: The correction algorithm accurately reconstructs the measured attenuation curve and thus gives better classification performance.

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Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners.

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Photon counting CT (PCCT) is an x-ray imaging technique that has undergone great development in the past decade. PCCT has the potential to improve dose efficiency and low-dose performance. In this paper, we propose a statistics-based iterative algorithm to perform a direct reconstruction of material-decomposed images. Compared with the conventional sinogram-based decomposition method which has degraded performance in low-dose scenarios, the multi-energy alternating minimization algorithm for photon counting CT (MEAM-PCCT) can generate accurate material-decomposed image with much smaller biases.

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Development of spectral X-ray computer tomography (CT) equipped with photon counting detector has been recently attracting great research interest. This work aims to improve the quality of spectral X-ray CT image. Maximum a posteriori (MAP) expectation-maximization (EM) algorithm is applied for reconstructing image-based weighting spectral X-ray CT images. A spectral X-ray CT system based on the cadmium zinc telluride photon counting detector and a fat cylinder phantom were simulated. Comparing with the commonly used filtered back projection (FBP) method, the proposed method reduced noise in the final weighting images at 2, 4, 6 and 9 energy bins up to 85.2%, 87.5%, 86.7% and 85%, respectively. CNR improvement ranged from 6.53 to 7.77. Compared with the prior image constrained compressed sensing (PICCS) method, the proposed method could reduce noise in the final weighting images by 36.5%, 44.6%, 27.3% and 18% at 2, 4, 6 and 9 energy bins, respectively, and improve the contrast-to-noise ratio (CNR) by 1.17 to 1.81. The simulation study also showed that comparing with the FBP and PICCS algorithms, image-based weighting imaging using MAP-EM statistical algorithm yielded significant improvement of the CNR and reduced the noise of the final weighting image.

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Early atherosclerosis changes perfusion of the arterial wall due to localized proliferation of the vasa vasorum. When contrast agent passes through the artery, some enters the vasa vasorum and increases radiopacity of the arterial wall. Technical challenges to detecting changes in vasa vasorum density include the thin arterial wall, partial volume averaging at the arterial lumen/wall interface and calcification within the wall. We used a photon-counting spectral CT scanner to study carotid arteries of anesthetized pigs and micro-CT of these arteries to quantify vasa vasorum density. The left carotid artery wall was injected with autologous blood to stimulate vasa vasorum angiogenesis. The scans were performed at 25-120 keV; the tube-current-time product was 550 mAs. A 60 mL bolus of iodine contrast agent was injected into the femoral vein at 5mL/s. Two seconds post injection, an axial scan was acquired at every 3 s over 60 s (i.e., 20 time points). Each time point acquired 28 contiguous transaxial slices with reconstructed voxels 0.16 × 0.16 × 1 mm3. Regions-of-interest in the outer 2/3 of the arterial wall and in the middle 2/3 of the lumen were drawn and their enhancements plotted versus time. Lumenal CT values peaked several seconds after injection and then returned towards baseline. Arterial wall CT values peaked concurrent to the lumen. The peak arterial wall enhancement in the left carotid arterial wall correlated with increased vasa vasorum density observed in micro-CT images of the isolated arteries.