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

机译: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。

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摘要

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.
机译:数值分析的可变度量优化方法已用于从惰性气体数据中恢复肺内通气-灌注比的已知分布。模拟假想的肺,并计算相应的惰性气体保留量。通过使用7种气体的无误差保留和50室模型,与其他数值方法相比,可以准确,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相一致的大小的随机误差引入保留数据时,恢复的分布在质量上与原始分布有所不同。这是由于数学问题的条件恶劣而导致的,这使得恢复的分布对保留中的小错误极为敏感。因此,当前的测量误差水平代表了从惰性气体测量中得出分布的当前技术中的重要限制。

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