Computational modelling of respiratory lung networks to predict patient-specific ventilatory responses

Mechanical ventilation system is a lifesaving treatment for patients who develop severe respiratory problems including acute lung failure (Acute Respiratory Distress Syndrome, ARDS). Although the ventilation system can be employed by the medical teams to keep a paitient's lung open to ensure the continuous exchange of oxygen and CO2, at the same time the ventilation pressure can cause severe damage to the lungs that it results in the patient's death. Doctors treating patients for acute respiratory problems have a limited range of parameters to work with when determining the best protocol for mechanical ventilation – pressure limits, oxygen level and air flow, for example. But the lung is a complex organ, and the amount of pressure necessary to keep all parts of the lung open to airflow can actually cause damage to some parts through overdistention of the tissue. In this study, we aim to develop a computational model to simulate the tempo-spatial variation of pressure and volume that occur during an inhalation and exhalation cycles. The computational model proposed in this study is formed around multiple algorithms developed using the concepts of pore network models for multiphase systems. The digital lung models are generated based on CT lung scanning while the simulation results can predict the response patient-specific ventilatory responses including the pressure fields along the entire lung system. The outcome of this work, can help medical teams to treat patients for acute respiratory problems by optimizing the mechanical ventilation systems.