Shigwedha Paulus Kapundja, Yamada Takahiro, Hanaoka Kohei, Ishii Kazunari, Kimura Yuichi, Fukuoka Yutaka
Department of Electrical Engineering and Electronics, Graduate School of Engineering, Kogakuin University, Shinjuku, Tokyo, Japan.
Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Osaka, Japan.
Biomed Phys Eng Express. 2021 Mar 15;7(3). doi: 10.1088/2057-1976/abec18.
Logan graphical analysis (LGA) is a method forquantification of tracer kinetics in positron emission tomography (PET). The shortcoming of LGA is the presence of a negative bias in the estimated parameters for noisy data. Various approaches have been proposed to address this issue. We recently applied an alternative regression method called least-squares cubic (LSC), which considers the errors in both the predictor and response variables to estimate the LGA slope. LSC reduced the bias in non-displaceable binding potential estimates while causing slight increases in the variance. In this study, we combined LSC with a principal component analysis (PCA) denoising technique to counteract the effects of variance on parametric image quality, which was assessed in terms of the contrast between gray and white matter. Tissue time-activity curves were denoised through PCA, prior to estimating the regression parameters using LSC. We refer to this approach as LSC-PCA. LSC-PCA was assessed against OLS-PCA (PCA with ordinary least-squares (OLS)), LSC, and conventional OLS-based LGA. Comparisons were made for simulatedC-carfentanil andC Pittsburgh compound B (C-PiB) data, and clinicalC-PiB PET images. PCA-based methods were compared over a range of principal components, varied by the percentage variance they account for in the data. The results showed reduced variances in distribution volume ratio estimates in the simulations for LSC-PCA compared to LSC, and lower bias compared to OLS-PCA and OLS. Contrasts were not significantly improved in clinical data, but they showed a significant improvement in simulation data -indicating a potential advantage of LSC-PCA over OLS-PCA. The effects of bias reintroduction when many principal components are used were also observed in OLS-PCA clinical images. We therefore encourage the use of LSC-PCA. LSC-PCA can allow the use of many principal components with minimal risk of bias, thereby strengthening the interpretation of PET parametric images.
洛根图形分析（LGA）是一种用于正电子发射断层扫描（PET）中示踪剂动力学定量分析的方法。LGA的缺点是在估计噪声数据的参数时存在负偏差。已经提出了各种方法来解决这个问题。我们最近应用了一种称为最小二乘三次（LSC）的替代回归方法，该方法考虑了预测变量和响应变量中的误差来估计LGA斜率。LSC减少了不可置换结合潜能估计中的偏差，同时导致方差略有增加。在本研究中，我们将LSC与主成分分析（PCA）去噪技术相结合，以抵消方差对参数图像质量的影响，参数图像质量根据灰质和白质之间的对比度进行评估。在使用LSC估计回归参数之前，通过PCA对组织时间-活性曲线进行去噪。我们将这种方法称为LSC-PCA。将LSC-PCA与OLS-PCA（普通最小二乘法（OLS）的PCA）、LSC和传统的基于OLS的LGA进行了评估比较。对模拟的¹¹C-卡芬太尼和¹¹C匹兹堡化合物B（¹¹C-PiB）数据以及临床¹¹C-PiB PET图像进行了比较。基于PCA的方法在一系列主成分上进行了比较，主成分的变化取决于它们在数据中所占的方差百分比。结果表明，与LSC相比，LSC-PCA在模拟中分布容积比估计的方差降低，与OLS-PCA和OLS相比偏差更低。临床数据中的对比度没有显著改善，但在模拟数据中显示出显著改善——表明LSC-PCA相对于OLS-PCA具有潜在优势。在OLS-PCA临床图像中也观察到使用许多主成分时偏差重新引入的影响。因此，我们鼓励使用LSC-PCA。LSC-PCA可以允许使用许多主成分，同时偏差风险最小，从而加强对PET参数图像的解释。
