基于种群的输入函数非线性混合效应建模多中心验证[18F]Fdg代谢率体素量化

Matteo Tonietto, F. Zanderigo, A. Bertoldo, D. Devanand, J. Mann, B. Bodini, B. Stankoff
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引用次数: 2

摘要

基于种群的输入函数(PBF)方法为动态正电子发射断层扫描(PET)图像的量化提供了一种侵入性较小的方法。PBF方法需要从一组接受了相同放射性示踪剂的全动脉血液采样的受试者中先验地创建一个输入函数模板。然后使用被分析对象的一个或两个血液样本对模板进行校准。在这项研究中,我们提出使用非线性混合效应方法和新的输入函数模型从一组8名受试者中生成PBF模板。我们使用在不同PET中心获得的25名受试者的独立[18F] FDG数据集验证了我们的PBF方法。结果显示,使用测量的输入函数获得的[18F] FDG净吸收率(Ki)的体素估计与使用所提出的PBF获得的体素估计之间具有高相关性(> 0.98)和低偏差(平均百分比误差=1.0±3.1%),支持将其用于在不同PET中心获得的[18F] FDG图像的量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multicenter Validation Of Population-Based Input Function With Non-Linear Mixed Effect Modeling For Voxel-Wise Quantification Of [18F]Fdg Metabolic Rate
Population-based input function (PBF) methods provide a less-invasive approach to the quantification of dynamic positron emission tomography (PET) images. PBF methods require the a priori creation of an input function template from a group of subjects who underwent full arterial blood sampling with the same radiotracer. The template is then calibrated using one or two blood samples from the subject under analysis. In this study we propose to generate the PBF template from a group of 8 subjects using a non-linear mixed effect approach and a new input function model. We validated our PBF approach using an independent[18F] FDG dataset of 25 subjects acquired in a different PET center. Results showed a high correlation (> 0.98) and low bias (mean percentage error=1.0 ± 3.1%) between the voxel-wise estimates of [18F] FDG net uptake rate (Ki) obtained with the measured input function and those obtained with the proposed PBF, supporting its use for the quantification of [18F] FDG images acquired in different PET centers.
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