Abstract：
BACKGROUND: Current photon-counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x-ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost-effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain.
OBJECTIVE: This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification.
METHODS: We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies.
RESULTS: In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%.
CONCLUSIONS: Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.
OBJECTIVE: This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification.
METHODS: We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies.
RESULTS: In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%.
CONCLUSIONS: Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.
摘要：
背景：当前的光子计数计算机断层扫描（CT）系统利用半导体探测器，如碲化镉(CdTe)，碲锌镉(CZT),和硅（Si），将X射线光子直接转换为电荷脉冲。另一种方法是间接检测，涉及与硅光电倍增管（SiPMs）耦合的正硅酸钇（YSO）闪烁体。由于其低成本，这提出了一个有吸引力和成本效益的选择，检测效率高,低黑暗计数率，和高传感器增益。
目的：本研究旨在基于我们小组使用多电压阈值（MVT）数字化仪开发的YSO/SiPM探测器，为三能量仓概念验证光子计数CT建立全面的定量成像框架，并评估这种光谱CT用于材料识别的可行性。
方法：我们开发了基于概念验证YSO/SiPM的台式光谱CT系统，并建立了用于三能量仓光子计数CT投影域处理的管道。采用经验A表方法进行基础材料分解，并对能谱CT系统的定量成像性能进行了评估。该评估包括虚拟单能量图像的合成误差，电子密度图像，有效原子序数图像，和线性衰减系数曲线。通过模拟和实验研究，证实了在三能箱光谱CT中采用A表方法进行材料识别的有效性。
结果：在无噪声和有噪声的模拟中，厚度估算实验和定量成像结果证明了较高的准确性。在厚度估算实验中采用实用的能谱CT系统,分解的Al基础材料的估计厚度的平均绝对误差为0.014±0.010mm，平均相对误差为0.66%±0.42%。同样，对于分解的聚甲基丙烯酸甲酯（PMMA）基础材料，厚度估计的平均绝对误差为0.064±0.058mm，平均相对误差为0.70%±0.38%。此外,采用基本材料的等效厚度，可以精确合成70keV虚拟单能图像（相对误差1.85％±1.26％），电子密度（相对误差1.81％±0.97％），和被测材料的有效原子序数(相对误差2.64%±1.26%)。此外,线性衰减系数曲线在40～150keV能量范围内的平均合成误差为1.89%±1.07%。
结论：仿真和实验结果都证明了70keV虚拟单能量图像的准确生成，电子密度,和使用A表方法的有效原子序数图像。定量成像结果表明，基于YSO/SiPM的光子计数探测器能够准确重建虚拟单能量图像，电子密度图像，有效原子序数图像，和线性衰减系数曲线，从而实现精确的材料识别。
目的：本研究旨在基于我们小组使用多电压阈值（MVT）数字化仪开发的YSO/SiPM探测器，为三能量仓概念验证光子计数CT建立全面的定量成像框架，并评估这种光谱CT用于材料识别的可行性。
方法：我们开发了基于概念验证YSO/SiPM的台式光谱CT系统，并建立了用于三能量仓光子计数CT投影域处理的管道。采用经验A表方法进行基础材料分解，并对能谱CT系统的定量成像性能进行了评估。该评估包括虚拟单能量图像的合成误差，电子密度图像，有效原子序数图像，和线性衰减系数曲线。通过模拟和实验研究，证实了在三能箱光谱CT中采用A表方法进行材料识别的有效性。
结果：在无噪声和有噪声的模拟中，厚度估算实验和定量成像结果证明了较高的准确性。在厚度估算实验中采用实用的能谱CT系统,分解的Al基础材料的估计厚度的平均绝对误差为0.014±0.010mm，平均相对误差为0.66%±0.42%。同样，对于分解的聚甲基丙烯酸甲酯（PMMA）基础材料，厚度估计的平均绝对误差为0.064±0.058mm，平均相对误差为0.70%±0.38%。此外,采用基本材料的等效厚度，可以精确合成70keV虚拟单能图像（相对误差1.85％±1.26％），电子密度（相对误差1.81％±0.97％），和被测材料的有效原子序数(相对误差2.64%±1.26%)。此外,线性衰减系数曲线在40～150keV能量范围内的平均合成误差为1.89%±1.07%。
结论：仿真和实验结果都证明了70keV虚拟单能量图像的准确生成，电子密度,和使用A表方法的有效原子序数图像。定量成像结果表明，基于YSO/SiPM的光子计数探测器能够准确重建虚拟单能量图像，电子密度图像，有效原子序数图像，和线性衰减系数曲线，从而实现精确的材料识别。
