A computationally efficient algorithm for determining regional cerebral blood flow in heterogeneous tissues by positron emission tomography
A computationally efficient algorithm for determining regional cerebral blood flow in heterogeneous tissues by positron emission tomography
复制标题
DOI：
10.1109/42.932746
复制
发表时间：
2001-07-01
影响因子：
10.6
通讯作者：
Sokoloff, L
中科院分区：
文献类型：
--
作者：
Schmidt, K;Sokoloff, L
由于空间分辨率有限，正电子发射断层扫描无法避免将具有不同血流速率和代谢速率的脑组织包含在感兴趣的体素或区域内。由于灰质中的区域脑血流量（rCBF）高于白质中的区域脑血流量（rCBF），因此当确定整个区域的rCBF时，部分体积效应会导致灰质中的rCBF被低估。此外，如果分析中使用的动力学模型未能考虑组织异质性，则加权平均 rCBF 本身就会被低估。我们导出了一种计算有效的方法来估计异质组织中的灰质和加权平均 rCBF，并在模拟研究中验证了该方法。该方法基于将异质组织表示为两种同质组织的加权混合物的模型。使用线性最小二乘算法来估计模型参数。
nclusion of brain tissues with different rates of blood flow and metabolism within a voxel or region of interest is an unavoidable problem with positron emission tomography due to its limited spatial resolution. Because regional cerebral blood flow (rCBF) is higher in gray matter than in white matter, the partial volume effect leads to underestimation of rCBF in gray matter when rCBF in the region as a whole is determined. Furthermore, weighted-average rCBF itself is underestimated if the kinetic model used in the analysis fails to account for the tissue heterogeneity. We have derived a computationally efficient method for estimating both gray matter and weighted-average rCBF in heterogeneous tissues and validated the method in simulation studies. The method is based on a model that represents a heterogeneous tissue as a weighted mixture of two homogeneous tissues. A linear least squares algorithm is used to estimate the model parameters.