Spectral/multienergy CT employing the state-of-the-art energy-discriminative photon-counting detector can identify absorption features in the multiple ranges of photon energies and has the potential to distinguish different materials based on K-edge characteristics. K-edge characteristics involve the sudden attenuation increase in the attenuation profile of a relatively high atomic number material. Hence, spectral CT can utilize material K-edge characteristics (sudden attenuation increase) to capture images in available energy bins (levels/windows) to distinguish different material components. In this paper, we propose an imaging model based on K-edge characteristics for maximum material discrimination with spectral CT. The wider the energy bin width is, the lower the noise level is, but the poorer the reconstructed image contrast is. Here, we introduce the contrast-to-noise ratio (CNR) criterion to optimize the energy bin width after the K-edge jump for the maximum CNR. In the simulation, we analyze the reconstructed image quality in different energy bins and demonstrate that our proposed optimization approach can maximize CNR between target region and background region in reconstructed image. 1. Introduction X-ray computed tomography (CT) has been widely applied in clinical and preclinical applications, since Hounsfield’s Nobel Prize winning breakthrough. A typical conventional CT system employs a broad energy spectrum source and a digital integrating sensor whose output is proportional to the energy fluence integrated over the entire incidence spectrum. Physically, the X-ray spectrum contains much information; the conventional CT system collects photons over the whole X-ray spectrum to ignore spectral responses of materials. Hence, the conventional CT often does not have sufficiently high contrast resolution for biological soft tissues [1]. With the development of spectral detectors and novel contrast agents, CT image contrast resolution could be significantly improved. Recent advances in spectral/multienergy detector technology have allowed for spectral CT systems to identify absorption features in the multiple ranges of photon energies [2–6]. Spectral CT has a stronger capability to distinguish different materials because it can capture images in available energy bins [7–13]. Meanwhile, contrast agent has been widely applied in biomedical imaging to enhance tissue contrast [14–19]. Spectral CT imaging utilizes not only density characteristics of contrast agents but also K-edge characteristics of contrast agents to distinguish different materials.
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