A Quasi-3D compartmental multi-scale approach to detect and quantify diseased regional lung constriction using spirometry data

摘要: Spirometry is a widely used pulmonary function test to detect the airflow limitations associated with various obstructive lung diseases, such as asthma, chronic disease, and even obesity-related complications. These conditions arise due change in airway resistance, alveolar compliance, inductance values. Currently, zero-dimensional compartmental models are commonly for calibrating these values, ie, solving inverse spirometry problem. However, compartments cannot capture flow physics or spatial geometry effects, thereby generating low fidelity prediction of diseased lung. Computational fluid dynamics (CFD) offer higher solutions but may be impractical certain applications duration simulations. Recently, novel, fast-running, robust Quasi-3D (Q3D) wire model simulating human was developed by CFD Research Corporation. This Q3D method preserved 3D nature airways favorably validated against solutions. In present study, multi-scale combination further improved predict regional constriction lungs using data. The mesh resolved up eighth generation. remainder alveoli sections modeled approach. then split into different sections, resistance values regions obtained parameter inversion. Finally, diameter reduced create actual model, corresponding can patient-specific drug deposition predictions subsequent optimization orally inhaled products.