投影(关系代数)
体积热力学
分解
计算机科学
探测器
约束(计算机辅助设计)
校准
光子计数
基础(线性代数)
先验与后验
能量(信号处理)
迭代重建
扫描仪
光子
算法
光学
材料科学
计算机视觉
人工智能
物理
数学
统计
化学
哲学
认识论
电信
有机化学
量子力学
几何学
作者
Zhoubo Li,Shuai Leng,Lifeng Yu,Zhicong Yu,Cynthia H. McCollough
摘要
Photon-counting CT (PCCT) potentially offers both improved dose efficiency and material decomposition capabilities relative to CT systems using energy integrating detectors. With respect to material decomposition, both projection-based and image-based methods have been proposed, most of which require accurate a priori information regarding the shape of the x-ray spectra and the response of the detectors. Additionally, projection-based methods require access to projection data. These data can be difficult to obtain, since spectra, detector response, and projection data formats are proprietary information. Further, some published image-based, 3-material decomposition methods require a volume conservation assumption, which is often violated in solutions. We have developed an image-based material decomposition method that can overcome those limitations. We introduced a general condition on volume constraint that does not require the volume to be conserved in a mixture. An empirical calibration can be performed with various concentrations of basis materials. The material decomposition method was applied to images acquired from a prototype whole-body PCCT scanner. The results showed good agreement between the estimation and known mass concentration values. Factors affecting the performance of material decomposition, such as energy threshold configuration and volume conservation constraint, were also investigated. Changes in accuracy of the mass concentration estimates were demonstrated for four different energy configurations and when volume conservation was assumed.
科研通智能强力驱动
Strongly Powered by AbleSci AI