Pulmonary function is tightly linked to the lung mechanical behavior, especially large deforma-tion during breathing. Interstitial lung diseases, such as Idiopathic Pulmonary Fibrosis (IPF), havean impact on the pulmonary mechanics and consequently alter lung function. However, IPF re-mains poorly understood, poorly diagnosed and poorly treated. Currently, the mechanical impactof such diseases is assessed by pressure-volume curves, giving only global information. We de-veloped a poromechanical model of the lung that can be personalized to a patient based on routineclinical data. The personalization pipeline uses clinical data, mainly CT-images at two time stepsand involves the formulation of an inverse problem to estimate regional compliances. The estima-tion problem can be formulated both in terms of “effective”, i.e., without considering the mixtureporosity, or “rescaled”, i.e., where the first-order effect of the porosity has been taken into account,compliances. Regional compliances are estimated for one control subject and three IPF patients,allowing to quantify the IPF-induced tissue stiffening. This personalized model could be used in theclinic as an objective and quantitative tool for IPF diagnosis.