A Functional Approach to Deconvolve Dynamic Neuroimaging Data.
A Functional Approach to Deconvolve Dynamic Neuroimaging Data.
复制标题
DOI：
10.1080/01621459.2015.1060241
复制
发表时间：
2016-01-02
影响因子：
3.7
通讯作者：
Wang JL
中科院分区：
文献类型：
--
作者：
Jiang CR;Aston JA;Wang JL
正电子发射断层扫描(PET)是一种成像技术，可以用来研究人类生物过程中的化学变化，如癌症的发生或神经化学反应。目前大多数动态正电子发射计算机断层扫描的分析都基于这样的假设，即可以使用线性一级动力学来充分描述被观察的系统。然而，最近有强有力的证据表明，情况并非如此。为了提供对PET数据的分析，使其摆脱这种划分假设，我们提出了一种基于泛函主成分分析的动态PET数据的非参数反卷积分析模型。当线性隔室模型设置是真正的数据生成机制时，这在可能的去卷积函数中产生灵活性，同时仍然执行得很好。由于只需要在相对较少的基函数上执行去卷积，而不是在整个三维体积中逐个体素地执行，因此该方法对典型的脑成像噪声水平具有鲁棒性，同时在计算上也是高效的。通过一维函数和2D图像的模拟来研究这种新的方法，并将其应用于一项神经成像研究，其目标是量化大脑中阿片受体的浓度。
Positron emission tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first-order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. To provide an analysis of PET data which is free from this compartmental assumption, we propose a nonparametric deconvolution and analysis model for dynamic PET data based on functional principal component analysis. This yields flexibility in the possible deconvolved functions while still performing well when a linear compartmental model setup is the true data generating mechanism. As the deconvolution needs to be performed on only a relative small number of basis functions rather than voxel by voxel in the entire three-dimensional volume, the methodology is both robust to typical brain imaging noise levels while also being computationally efficient. The new methodology is investigated through simulations in both one-dimensional functions and 2D images and also applied to a neuroimaging study whose goal is the quantification of opioid receptor concentration in the brain.