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一种从惰性气体数据中恢复通气-灌注分布的有效优化技术。随机实验误差的影响。

An efficient optimization technique for recovering ventilation-perfusion distributions from inert gas data. Effects of random experimental error.

作者信息

Jaliwala S A, Mates R E, Klocke F J

出版信息

J Clin Invest. 1975 Jan;55(1):188-92. doi: 10.1172/JCI107910.

Abstract

A variable metric optimization method of numerical analysis has been used to recover known distributions of intrapulmonary ventilation-perfusion ratios from inert gas data. Hypothetical lungs were simulated and corresponding inert gas retentions calculated. By using error-free retentions for seven gases and a 50-compartment model, it was possible to recover distributions containing up to three modes accurately and with greater efficiency than with other numerical methods. When random error of a magnitude consistent with present analytical techniques was introduced into retention data, the recovered distributions differed qualitatively from the original ones. This resulted from the ill-conditioned nature of the mathematical problem, which makes a recovered distribution extremely sensitive to small errors in retention. Thus, present levels of measurement error represent an important limitation in current techniques for deriving distributions from inert gas measurements.

摘要

数值分析中的一种变尺度优化方法已被用于从惰性气体数据中恢复肺内通气-灌注比的已知分布。模拟了假设的肺部,并计算了相应的惰性气体潴留情况。通过使用七种气体的无误差潴留数据和一个50房室模型,能够准确且比其他数值方法更高效地恢复包含多达三种模式的分布。当将与当前分析技术一致大小的随机误差引入潴留数据时,恢复的分布在性质上与原始分布不同。这是由于数学问题的病态性质导致的,这使得恢复的分布对潴留中的小误差极其敏感。因此,目前的测量误差水平是当前从惰性气体测量中推导分布的技术的一个重要限制。

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