Improving PET receptor binding estimates from Logan plots using principal component analysis
Improving PET receptor binding estimates from Logan plots using principal component analysis
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DOI：
10.1038/sj.jcbfm.9600584
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发表时间：
2008-04-01
影响因子：
6.3
通讯作者：
Koeppe, Robert A.
中科院分区：
文献类型：
--
作者：
Joshi, Aniket D.;Fessler, Jeffrey A.;Koeppe, Robert A.
本研究报告了一种基于主成分分析（PCA）的方法，用于减少正电子发射断层扫描（PET） Logan图中分布体积比（DVR）估计的偏差。将所有现有的去偏方法与提出的PCA方法进行了比较，包括单估计PET研究和进行干预前和干预后估计的干预研究。基于Logan的DVR估计的偏差是由于PET时间活动曲线（TACs）中的噪声，这些噪声作为Logan方程的因变量和自变量的相关误差传播。干预研究显示了同样的偏差，但在DVR估计中也有更高的差异。在这项工作中，通过将曲线拟合到一个低维基于pcaba的线性模型来降低tac中的噪声，从而降低了DVR中的偏差和方差。为了验证该方法，对具有卡芬太尼（CFN）样动力学的c -11标记示踪剂进行了具有真实噪声的tac模拟，用于单次测量和干预研究。将主成分分析方法与现有方法应用于模拟数据，并通过统计分析对其性能进行比较。结果表明，现有的方法要么只能消除部分偏差，要么以牺牲精度为代价减少偏差。该方法消除了近90%的偏差，同时提高了单次和双次测量模拟的精度。在模拟中验证了所提出的方法后，将PCA与现有方法一起应用于DVR单估计和双估计干预研究中获得的人类[C-11] CFN数据。在人体扫描中观察到的结果与模拟研究中看到的结果相似。
This work reports a principal component analysis (PCA)-based approach for reducing bias in distribution volume ratio (DVR) estimates from Logan plots in positron emission tomography (PET). Comparison has been made of all existing bias-removal methods with the proposed PCA method, for both single-estimate PET studies and intervention studies where pre- and post-intervention estimates are made. Bias in Logan-based DVR estimates is because of the noise in the PET time-activity curves (TACs) that propagates as correlated errors in dependent and independent variables of the Logan equation. Intervention studies show this same bias but also higher variance in DVR estimates. In this work, noise in the TACs was reduced by fitting the curves to a low-dimension PCAbased linear model, leading to reduced bias and variance in DVR. For validating the approach, TACs with realistic noise were simulated for a C-11-labeled tracer with carfentanil (CFN)-like kinetics for both single-measurement and intervention studies. Principal component analysis and existing methods were applied to the simulated data and their performance was compared by statistical analysis. The results indicated that existing methods either removed only part of the bias or reduced bias at the expense of precision. The proposed method removed similar to 90% of the bias while also improving precision in both single-and dual-measurement simulations. After validation of the proposed method in simulations, PCA, along with the existing methods, was applied to human [C-11] CFN data acquired for both single estimation of DVR and dual-estimation intervention studies. Similar results were observed in human scans as were seen in the simulation studies.