Parametric and non-parametric Poisson regression for modelling of the arterial input function in positron emission tomography.
Parametric and non-parametric Poisson regression for modelling of the arterial input function in positron emission tomography.
复制标题
正电子发射断层扫描中动脉输入函数建模的参数和非参数泊松回归。
DOI：
10.1186/s40658-023-00591-2
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发表时间：
2023-11-21
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影响因子：
4
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中科院分区：
文献类型：
--
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正电子发射断层扫描（PET）的全面量化需要动脉输入功能（AIF），用于测量某些目标，或使用特定的放射性示踪剂，或用于量化特定的结果测量。AIF代表PET检查过程中动脉血浆中放射性示踪剂浓度的测量值。AIF的测量容易产生误差，因为它是由不同样品与不同设备的多次测量组合而成的复合测量，其中每一个都可能是测量误差的来源。此外，其测量需要早期时间点的高度时间粒度，这需要在记录样本的质量和数量之间进行折衷。由于这些原因，通常期望将模型拟合到该数据，以便在将其用于定量组织中的放射性示踪剂结合之前提高其质量。动脉血和血浆样本中放射性的原始观察结果来自放射性衰变，其测量为记录的计数数量。计数数据有几个特定的属性，包括它们不能为负以及特定的均值-方差关系。Poisson回归是处理计数数据的最基本的建模策略，因为它结合并利用了这些属性。然而，据我们所知，没有以前的研究已经采取这种方法，尽管使用适当的分布假设的结果更高的效率和准确性的优势。在这里，我们为AIF实现了一种泊松回归建模方法，作为其应用的概念验证。我们对输入函数曲线应用了参数和非参数模型。我们表明，负二项分布是一个更合适的误差分布处理过度分散。此外，我们将这种方法扩展到一个分层非参数模型，该模型对缺失数据具有很强的弹性。因此，我们证明了泊松回归应用于AIF数据时是可行和有效的，并提出这是一个有前途的策略，在未来的PET血细胞计数数据建模。
Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures. The AIF represents the measurement of radiotracer concentrations in the arterial blood plasma over the course of the PET examination. Measurement of the AIF is prone to error as it is a composite measure created from the combination of multiple measurements of different samples with different equipment, each of which can be sources of measurement error. Moreover, its measurement requires a high degree of temporal granularity for early time points, which necessitates a compromise between quality and quantity of recorded samples. For these reasons, it is often desirable to fit models to this data in order to improve its quality before using it for quantification of radiotracer binding in the tissue. The raw observations of radioactivity in arterial blood and plasma samples are derived from radioactive decay, which is measured as a number of recorded counts. Count data have several specific properties, including the fact that they cannot be negative as well as a particular mean-variance relationship. Poisson regression is the most principled modelling strategy for working with count data, as it both incorporates and exploits these properties. However, no previous studies to our knowledge have taken this approach, despite the advantages of greater efficiency and accuracy which result from using the appropriate distributional assumptions. Here, we implement a Poisson regression modelling approach for the AIF as proof-of-concept of its application. We applied both parametric and non-parametric models for the input function curve. We show that a negative binomial distribution is a more appropriate error distribution for handling overdispersion. Furthermore, we extend this approach to a hierarchical non-parametric model which is shown to be highly resilient to missing data. We thus demonstrate that Poisson regression is both feasible and effective when applied to AIF data, and propose that this is a promising strategy for modelling blood count data for PET in future.
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影响因子：
2.7
作者：
Pedersen, Eric J.;Miller, David L.;Ross, Noam
通讯作者：
Ross, Noam
DOI：
10.1007/s00259-020-04855-2
发表时间：
2020-12
影响因子：
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影响因子：
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影响因子：
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通讯作者：
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作者：
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