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首页> 外文期刊>IEEE Transactions on Medical Imaging >A study on statistically reliable and computationally efficient algorithms for generating local cerebral blood flow parametric images with positron emission tomography
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A study on statistically reliable and computationally efficient algorithms for generating local cerebral blood flow parametric images with positron emission tomography

机译:利用正电子发射断层扫描生成局部脑血流参数图像的统计可靠且计算效率高的算法的研究

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摘要

With the advent of positron emission tomography (PET), a variety of techniques have been developed to measure local cerebral blood flow (LCBF) noninvasively in humans. A potential class of techniques, which includes linear least squares (LS), linear weighted least squares (WLS), linear generalized least squares (GLS), and linear generalized weighted least squares (GWLS), is proposed. The statistical characteristics of these methods are examined by computer simulation. The authors present a comparison of these four methods with two other rapid estimation techniques developed by Huang et al. (1982) and Alpert (1984), and two classical methods, the unweighted and weighted nonlinear least squares regression. The results show that these methods can take full advantage of the contribution from the fine temporal sampling data of modern tomographs, and thus provide statistically reliable estimates that are comparable to those obtained from nonlinear LS regression. These methods also have high computational efficiency, and the parameters can be estimated directly from operational equations in one single step. Therefore, they can potentially be used in image-wide estimation of local cerebral blood flow and distribution volume with PET.
机译:随着正电子发射断层扫描(PET)的出现,已经开发了多种技术来无创地测量人体的局部脑血流(LCBF)。提出了一种潜在的技术类别,包括线性最小二乘(LS),线性加权最小二乘(WLS),线性广义最小二乘(GLS)和线性广义加权最小二乘(GWLS)。通过计算机仿真检查了这些方法的统计特性。作者将这四种方法与Huang等人开发的另外两种快速估算技术进行了比较。 (1982)和Alpert(1984),以及两种经典方法,即非加权和加权非线性最小二乘回归。结果表明,这些方法可以充分利用现代断层扫描仪精细时间采样数据的贡献,从而提供与非线性LS回归可比的统计可靠估计。这些方法还具有很高的计算效率,并且可以在一个步骤中直接从运算方程式估算参数。因此,它们可以潜在地用于PET的全图像范围的局部脑血流和分布体积估计。

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