A framework for modelling whole-lung and regional transfer factor of the lung for carbon monoxide using...

Pulmonary gas exchange is assessed by the transfer factor of the lungs (T _L) for carbon monoxide (T _LCO), and can also be measured with inhaled xenon-129 (¹²⁹Xe) magnetic resonance imaging (MRI). A model has been proposed to estimate T _L from ¹²⁹Xe MRI metrics, but this approach has not been fully validated and does not utilise the spatial information provided by three-dimensional ¹²⁹Xe MRI.

Three models for predicting T _L from ¹²⁹Xe MRI metrics were compared: 1) a previously-published physiology-based model, 2) multivariable linear regression and 3) random forest regression. Models were trained on data from 150 patients with asthma and/or COPD. The random forest model was applied voxel-wise to ¹²⁹Xe images to yield regional T _L maps.

Coefficients of the physiological model were found to differ from previously reported values. All models had good prediction accuracy with small mean absolute error (MAE): 1) 1.24±0.15 mmol·min^-1·kPa^-1, 2) 1.01±0.06 mmol·min^-1·kPa^-1, 3) 0.995±0.129 mmol·min^-1·kPa^-1. The random forest model performed well when applied to a validation group of post-COVID-19 patients and healthy volunteers (MAE=0.840 mmol·min^-1·kPa^-1), suggesting good generalisability. The feasibility of producing regional maps of predicted T _L was demonstrated and the whole-lung sum of the T _L maps agreed with measured T _LCO (MAE=1.18 mmol·min^-1·kPa^-1).

The best prediction of T _LCO from ¹²⁹Xe MRI metrics was with a random forest regression framework. Applying this model on a voxel-wise level to create parametric T _L maps provides a useful tool for regional visualisation and clinical interpretation of ¹²⁹Xe gas exchange MRI.