Abstract
This paper describes two approaches to modelling lung disease: one based on a multi-compartment statistical model with a log normal distribution of ventilation perfusion ratio ([image omitted]) values; and the other on a bifurcating tree which emulates the anatomical structure of the lung. In the statistical model, the distribution becomes bimodal, when the [image omitted] values of a randomly selected number of compartments are reduced by 85% to simulate lung disease. For the bifurcating tree model a difference in flow to the left and right branches coupled with a small random variation in flow ratio between generations results in a log normal distribution of flows in the terminal branches. Restricting flow through branches within the tree to simulate lung disease transforms this log normal distribution to a bi-modal one. These results are compatible with those obtained from experiments using the multiple inert gas elimination technique, where log normal distributions of [image omitted] ratio become bimodal in the presence of lung disease.
| Original language | English |
|---|---|
| Pages (from-to) | 139-154 |
| Number of pages | 16 |
| Journal | Computational and Mathematical Methods in Medicine |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Jun 2009 |
Keywords
- log normal
- tree
- lung disease
- modelling