Back

Sensitivity analysis of models of gas exchange for lung hyperpolarised 129-Xe MRS and MRI.

Taylor, Y.; Wilson, F. J.; Kim, M.; Parker, G. J. M.

2023-07-27 bioengineering
10.1101/2023.07.26.550733 bioRxiv
Show abstract

PurposeSensitivity analysis enables the identification of influential parameters and the optimisation of model composition. Such methods have not previously been applied systematically to models describing hyperpolarised 129Xe gas exchange in the lung. Here, we evaluate current 129Xe gas exchange models to assess their precision for identifying alterations in pulmonary-vascular function and lung microstructure. MethodsWe assess sensitivity using established univariate methods and scatter plots for parameter interactions. We apply them to the model described by Patz and MOXE et al., examining their ability to measure: i) importance (rank), ii) temporal dependence, and iii) interaction effects of each parameter across healthy and diseased ranges. ResultsThe univariate methods and scatter plot analyses demonstrate consistently similar results for the importance of parameters common to both models evaluated. Alveolar surface area to volume ratio is identified as the parameter to which model signals are most sensitive. The alveolar-capillary barrier thickness is identified as a low-sensitivity parameter for the MOXE model. An acquisition window of at least 200 ms effectively demonstrates model sensitivity to most parameters. Scatter plots reveal interaction effects in both models, impacting output variability and sensitivity. ConclusionOur sensitivity analysis ranks the parameters within the model described by Patz et al and within the MOXE model. The MOXE model shows low sensitivity to alveolar-capillary barrier thickness, highlighting the need for designing acquisition protocols optimised for the measurement of this parameter. The presence of parameter interaction effects highlights the requirement for care in interpreting model outputs.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.