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首页> 外文期刊>Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism >Improved statistical power of the multilinear reference tissue approach to the quantification of neuroreceptor ligand binding by regularization*.
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Improved statistical power of the multilinear reference tissue approach to the quantification of neuroreceptor ligand binding by regularization*.

机译：多线性参考组织方法通过正则化*量化神经受体配体结合的统计能力提高。

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摘要

A multilinear reference tissue approach has been widely used recently for the assessment of neuroreceptor-ligand interactions with positron emission tomography. The authors analyzed this "multilinear method" with respect to its sensitivity to statistical noise, and propose regularization procedures that reduce the effects of statistical noise. Computer simulations and singular value decomposition of its operational equation were used to investigate the sensitivity of the multilinear method to statistical noise. Regularization was performed by truncated singular value decomposition, Tikhonov-Phillips regularization, and by imposing boundary constraints on the rate constants. There was a significant underestimation of distribution volume ratios. Singular value decomposition showed that the bias was caused by statistical noise. The regularization procedures significantly increased the test-retest stability. The bias could be reduced by applying linear constraints on the rate constants based on their normal range. Underestimation of distribution volume ratios by the multilinear method is caused by its sensitivity to statistical noise. Statistical power in the discrimination of different groups of subjects can be significantly improved by regularization procedures without introducing additional bias. Correct distribution volume ratios can be obtained by imposing physiologic constraints on the rate constants.
机译：最近，多线性参考组织方法已广泛用于评估正电子发射断层显像对神经受体-配体的相互作用。作者分析了这种“多线性方法”对统计噪声的敏感性，并提出了减少统计噪声影响的正则化程序。利用计算机仿真及其运算方程的奇异值分解来研究多线性方法对统计噪声的敏感性。通过截断奇异值分解，Tikhonov-Phillips正则化以及对速率常数施加边界约束来执行正则化。分配量比率被大大低估了。奇异值分解表明偏差是由统计噪声引起的。正则化程序显着增加了重测的稳定性。可以通过基于速率常数的正常范围对速率常数应用线性约束来减少偏差。多线性方法对分布体积比率的低估是由于其对统计噪声的敏感性。通过正则化程序可以显着提高对不同组受试者的辨别力的统计能力，而无需引入其他偏见。通过对速率常数施加生理限制，可以获得正确的分配体积比。

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