Kasai Ryosuke, Yamada Kenji
Department of Radiological Technology, Tokushima University Hospital.
Nihon Hoshasen Gijutsu Gakkai Zasshi. 2017;73(8):654-663. doi: 10.6009/jjrt.2017_JSRT_73.8.654.
In this study, we propose an evaluation method for Bayesian estimation of Gumbel distribution parameters by the Hamiltonian Monte Carlo method (HMC method), with changing the pixel size of the CT image to investigate streak artifacts, without using a significant difference test. Placed a titanium endcap in the center of the CT dose index (CTDI) measurement phantom and got the CT image by changing the display-field of view (D-FOV) to S, M, L, LL. We compared Gumbel distribution parameters with conventional estimation method and Bayesian estimation method using HMC method. In addition, we evaluated streak artifacts by Bayesian statistical analysis. The difference in streak artifact between D-FOV was more than 90% except between D-FOV M and L. The effect of streak artifacts is small as the pixel size was small. By using the HMC method, we can estimate the Gumbel distribution parameters accurately and objectively, and quantitatively evaluated that the streak artifacts differ in pixel size using Bayesian statistical analysis.
在本研究中,我们提出了一种通过哈密顿蒙特卡罗方法(HMC方法)对耿贝尔分布参数进行贝叶斯估计的评估方法,通过改变CT图像的像素大小来研究条纹伪影,而不使用显著性差异检验。在CT剂量指数(CTDI)测量体模的中心放置一个钛制端盖,并通过将显示视野(D-FOV)更改为S、M、L、LL来获取CT图像。我们将耿贝尔分布参数与传统估计方法和使用HMC方法的贝叶斯估计方法进行了比较。此外,我们通过贝叶斯统计分析评估了条纹伪影。除了D-FOV M和L之间,D-FOV之间的条纹伪影差异超过90%。随着像素尺寸变小,条纹伪影的影响较小。通过使用HMC方法,我们可以准确、客观地估计耿贝尔分布参数,并使用贝叶斯统计分析定量评估条纹伪影在像素大小方面的差异。