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Image-domain material decomposition using photon-counting CT

机译:使用光子计数CT的图像域材料分解

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Novel CdTe photon counting x-ray detectors (PCXDs) have been developed for very high count rates [1-4] suitable for x-ray micro computed tomography (μCT) scanners. It counts photons within each of J energy bins. In this study, we investigate use of the data in these energy bins for material decomposition using an image domain approach. In this method, one image is reconstructed from projection data of each energy bin; thus, we have J images from J energy bins that are associated with attenuation coefficients with a narrow energy width. We assume that the spread of energies in each bin is small and thus that the attenuation can be modeled using an effective energy for each bin. This approximation allows us to linearize the problem, thus simplify the inversion procedure. We then fit J attenuation coefficients at each location x by the energy-attenuation function [5] and obtain either (1) photoelectric and Compton scattering components or (2) 2 or 3 basis-material components. We used computer simulations to evaluate this approach generating projection data with three types of acquisition schemes: (A) five monochromatic energies; (B) five energy bins with PCXD and an 80 kVp polychromatic x-ray spectrum; and (C) two kVp with an intensity integrating detector. Total attenuation coefficients of reconstructed images and calculated effective atomic numbers were compared with data published by National Institute of Standards and Technology (NIST). We developed a new materially defined "SmileyZ" phantom to evaluate the accuracy of the material decomposition methods. Preliminary results showed that material based 3-basis functions (bone, water and iodine) with PCXD with 5 energy bins was the most promising approach for material decomposition.
机译:新型CdTe光子计数X射线探测器(PCXD)的开发具有很高的计数率[1-4],适用于X射线计算机断层扫描(μCT)扫描仪。它对J个能量箱中的每个光子进行计数。在这项研究中,我们调查使用这些能量箱中的数据进行图像域方法的材料分解。在这种方法中,从每个能量箱的投影数据重建一个图像;因此,我们有来自J个能量箱的J个图像,这些图像与能量宽度较窄的衰减系数相关。我们假设每个仓中的能量散布很小,因此可以使用每个仓的有效能量对衰减进行建模。这种近似使我们可以线性化问题,从而简化了求逆过程。然后,我们通过能量衰减函数[5]拟合每个位置x的J衰减系数,并获得(1)光电和康普顿散射分量或(2)2或3种基础材料分量。我们使用计算机仿真来评估这种通过三种类型的采集方案生成投影数据的方法:(A)五个单色能量; (B)具有PCXD和80 kVp多色X射线光谱的五个能量箱; (C)具有强度积分检测器的两个kVp。将重建图像的总衰减系数和计算出的有效原子序数与美国国家标准技术研究院(NIST)发布的数据进行了比较。我们开发了一种新的材料定义的“ SmileyZ”体模,以评估材料分解方法的准确性。初步结果表明,具有5个能量箱的PCXD基于材料的3基功能(骨,水和碘)是最有希望的材料分解方法。

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