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We present a new approach for physics-based computational modeling ofdiseased human lungs. Our main object is the development of a model that takesthe novel step of incorporating the dynamics of airwayrecruitment/de-recruitment into an anatomically accurate, spatially resolvedmodel of respiratory system mechanics, and the relation of these dynamics toairway dimensions and the biophysical properties of the lining fluid. Theimportance of our approach is that it potentially allows for more accuratepredictions of where mechanical stress foci arise in the lungs, since it is atthese locations that injury is thought to arise and propagate from. We matchthe model to data from a patient with Acute Respiratory Distress Syndrome(ARDS) to demonstrate the potential of the model for revealing the underlyingderangements in ARDS in a patient-specific manner. To achieve this, thespecific geometry of the lung and its heterogeneous pattern of injury areextracted from medical CT images. The mechanical behavior of the model istailored to the patient's respiratory mechanics using measured ventilationdata. In retrospective simulations of various clinically performed,pressure-driven ventilation profiles, the model adequately reproduces clinicalquantities measured in the patient such as tidal volume and change in pleuralpressure. The model also exhibits physiologically reasonable lung recruitmentdynamics and has the spatial resolution to allow the study of local mechanicalquantities such as alveolar strains. This modeling approach advances ourability to perform patient-specific studies in silico, opening the way topersonalized therapies that will optimize patient outcomes.

An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung