Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3680-3683. doi: 10.1109/EMBC46164.2021.9630175.
Positron emission tomography (PET) is a physiological, non-invasive imaging technique, which forms an essential part of nuclear medicine. The data obtained in a PET scan represent the concentration of an administered radiotracer in tissues over time. Quantitative analysis of PET data makes possible the assessments of in-vivo physiological processes. The Logan graphical analysis (LGA) is one of the methods that are used for quantitative analysis of PET data. LGA transforms PET data into a simple linear relationship. The slope of the LGA linear relationship is a physiological quantity denoting receptor availability. This quantity is termed distribution volume ratio (DVR). LGA-based estimates of the DVR are negatively affected by the noise in PET data -leading to the DVR being underestimated. A number of approaches proposed to address this issue have been observed to reduce the bias at the cost precision. An alternative regression method, least-squares cubic (LSC), was recently applied to estimate the DVR in order to reduce the bias. LSC was observed to reduce the bias in the LGA-based estimates. However, slight increases were also observed in the variance of the LSC-based estimates. This calls for methods to act against the variance in the LSC-based estimates. In this study, an alternative method is applied for tTAC denoising. This method is referred to as correlated component analysis (CorrCA). CorrCA transform the data by searching for dimensions of maximum correlation. This technique is closely related to other well-known methods such as principal component analysis and independent component analysis. In this study, the data were denoised by CorrCA (to act against the variance in the estimate) and the DVR was estimated by LSC, which provides for minimal bias. The resulting method LSC-CorrCA, gave less-biased estimated with increased precision. This was observed for both simulation results as well as for clinical data, both for C Pittsburgh compound B. Simulation data revealed reduced variances in LCS-CorrCA-based estimates, and the clinical data showed improved contrast between gray and white matter regions.Clinical Relevance-Improved DVR estimates would ease the interpretation of medical images, which will in turn positively influence the clinical processes, from diagnosis to treatment and follow-ups.
正电子发射断层扫描（PET）是一种生理、非侵入性的成像技术，是核医学的重要组成部分。PET 扫描中获得的数据代表放射性示踪剂在组织中的浓度随时间的变化。PET 数据的定量分析使得对体内生理过程的评估成为可能。Logan 图形分析（LGA）是用于 PET 数据定量分析的方法之一。LGA 将 PET 数据转换为简单的线性关系。LGA 线性关系的斜率是表示受体可用性的生理量。这个量被称为分布容积比（DVR）。LGA 基于 DVR 的估计受到 PET 数据噪声的影响——导致 DVR 被低估。已经观察到许多方法可以解决这个问题，这些方法在降低偏差的同时牺牲了精度。最近，一种替代回归方法，最小二乘三次（LSC），被应用于估计 DVR 以降低偏差。LSC 观察到可以降低 LGA 估计的偏差。然而，也观察到 LSC 估计的方差略有增加。这就需要有方法来对抗 LSC 估计的方差。在这项研究中，应用了一种替代方法来进行 tTAC 降噪。这种方法被称为相关成分分析（CorrCA）。CorrCA 通过寻找最大相关维度来变换数据。这项技术与其他知名方法密切相关，如主成分分析和独立成分分析。在这项研究中，通过 CorrCA（对抗估计中的方差）对数据进行降噪，然后通过 LSC 估计 DVR，这提供了最小的偏差。所得的方法 LSC-CorrCA 给出了偏差较小且精度提高的估计。这在模拟结果和临床数据中都得到了观察，包括 C Pittsburgh 复合 B。模拟数据显示 LSC-CorrCA 估计的方差减小，临床数据显示灰质和白质区域之间的对比度提高。临床相关性——改善 DVR 估计将使医学图像的解释变得更容易，这反过来将积极影响从诊断到治疗和随访的临床过程。
