A point spread function（PSF） for the blurring component in positron emission tomography（PET） is studied. The PSF matrix is derived from the single photon incidence response function. A statistical iterative recons...A point spread function（PSF） for the blurring component in positron emission tomography（PET） is studied. The PSF matrix is derived from the single photon incidence response function. A statistical iterative reconstruction（IR） method based on the system matrix containing the PSF is developed. More specifically, the gamma photon incidence upon a crystal array is simulated by Monte Carlo（MC） simulation, and then the single photon incidence response functions are calculated. Subsequently, the single photon incidence response functions are used to compute the coincidence blurring factor according to the physical process of PET coincidence detection. Through weighting the ordinary system matrix response by the coincidence blurring factors, the IR system matrix containing the PSF is finally established. By using this system matrix, the image is reconstructed by an ordered subset expectation maximization（OSEM） algorithm. The experimental results show that the proposed system matrix can substantially improve the image radial resolution, contrast,and noise property. Furthermore, the simulated single gamma-ray incidence response function depends only on the crystal configuration, so the method could be extended to any PET scanner with the same detector crystal configuration.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.Y4811H805C and 81101175)
文摘A point spread function（PSF） for the blurring component in positron emission tomography（PET） is studied. The PSF matrix is derived from the single photon incidence response function. A statistical iterative reconstruction（IR） method based on the system matrix containing the PSF is developed. More specifically, the gamma photon incidence upon a crystal array is simulated by Monte Carlo（MC） simulation, and then the single photon incidence response functions are calculated. Subsequently, the single photon incidence response functions are used to compute the coincidence blurring factor according to the physical process of PET coincidence detection. Through weighting the ordinary system matrix response by the coincidence blurring factors, the IR system matrix containing the PSF is finally established. By using this system matrix, the image is reconstructed by an ordered subset expectation maximization（OSEM） algorithm. The experimental results show that the proposed system matrix can substantially improve the image radial resolution, contrast,and noise property. Furthermore, the simulated single gamma-ray incidence response function depends only on the crystal configuration, so the method could be extended to any PET scanner with the same detector crystal configuration.
文摘PET-CT是一种将正电子发射断层成像(positron emission tomography,PET)和计算机断层成像(computed tomography,CT)两种影像诊断技术有机结合在一起的一种多模态成像技术。该成像可同时提供准确、全面的功能成像及结构信息成像。PET-CT成像在对活体进行心胸成像时,不可避免地呼吸运动和心跳运动导致的运动模糊严重干扰定量分析,进而影响诊断结果判断。目前常用门控成像技术解决呼吸及心跳运动带来的成像伪影影响,主要包括外接门控设备法和数据驱动法。现有的数据驱动门控方法难以兼顾多模态数据处理稳定性,心脏信号提取精准性以及实时性处理及反馈。本文提出了一种改进的数据驱动门控方法——基于局部信息的主成分分析方法(partial data principal component analysis,PD-PCA),实现了优化数据驱动门控成像完整流程。并在中国科学院高能物理研究所自主研制的Eplus多能全景动物PET-CT设备进行了实验验证,实验结果表明改进后的门控成像方法能够保证在心脏信号提取上实现了更高的精度,可实现实时门控动态高精度成像。
