Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses

Providing personalised optimal mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalising protective ventilatory treatment. The underlying computational lung model is able to predict global quantities e.g., tracheal flow and tidal volume, as well as local tissue aeration and strains for any ventilation manoeuvre. A novel "virtual EIT" module is added to our computational lung model and allows to simulate EIT voltage patterns based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Similar algorithms are used for reconstructing images from simulated and actually clinically measured EIT voltages allowing an exact comparison of the two data sets with high temporal resolution. As clinically measured EIT images are not used to calibrate the computational model, they can be utilised to validate its predictive capability. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome (ARDS). The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results encourage using the proposed framework for numerically predicting patient-specific responses to certain therapeutic measures before applying them to an actual patient and, on the long run, even finding patient-specific optimal ventilation protocols assisted by computational modeling.

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