一种改进的光子计数多物质识别物理模型CT
An improved physics model for multi-material identification in photon counting CT
摘要：
光子计数计算机断层扫描（Photon-counting computed tomography，PCCT）具有能量分辨能力，具有改善传统CT局限性的潜力，包括更好的信噪比（signal-to-noise ratio，SNR）、更高的对比度噪声比（contrast-to-noise ratio，CNR）、更低的辐射剂量，以及最重要的是，同时进行多种材料识别。材料识别的一种潜在方式是通过从PCCT图像数据计算有效原子序数和有效电子密度。然而，目前用于从PCCT图像数据计算有效原子序数和有效电子密度的方法大多基于半经验模型，因此不够准确。在这里，我们提出了一个基于物理的模型来计算各种物质的有效原子序数和有效电子密度，包括单元素物质，分子化合物，以及多材料的混合物。该模型进行了验证，在几种材料下的各种组合的能量箱。对PCCT系统进行仿真，生成PCCT图像数据，并将所提出的模型应用于PCCT图像数据。我们的模型得到的有效原子序数和有效电子密度的相对标准偏差小于1%。我们的研究结果进一步表明，五种不同的材料可以同时识别，并很好地分离在一个有效的原子序数-有效电子密度图。该模型可以作为PCCT同时材料识别的基础。
英文摘要：
Photon-counting computed tomography (PCCT) with energy discrimination capabilities hold great potentials to improve the limitations of the conventional CT, including better signal-to-noise ratio (SNR), improved contrast-to-noise ratio (CNR), lower radiation dose, and most importantly, simultaneous multiple material identification. One potential way of material identification is via calculation of effective atomic number and effective electron density from PCCT image data. However, the current methods for calculating effective atomic number and effective electron density from PCCT image data are mostly based on semi-empirical models and accordingly are not sufficiently accurate. Here, we present a physics-based model to calculate the effective atomic number and effective electron density of various matters, including single element substances, molecular compounds, and multi-material mixtures as well. The model was validated over several materials under various combinations of energy bins. A PCCT system was simulated to generate the PCCT image data, and the proposed model was applied to the PCCT image data. Our model yielded a relative standard deviations for effective atomic numbers and effective electron densities at less than 1%. Our results further showed that five different materials can be simultaneously identified and well separated in a effective atomic number - effective electron density map. The model could serve as a basis for simultaneous material identification from PCCT.
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